1 DESIGN AND EVALUATION OF CONVERSATIONAL MODELING METHODS FOR INTERPERSONAL SIMULATION By BRENT H ROSSEN A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2011
2 2011 Brent H Rossen
3 To my Wife, Mom, and Dad for their love and support
4 ACKNOWLEDGMENTS I thank my research advisor and supervisory co mmittee chair, Dr. Benjamin Lok. He taught me how to identify a problem, conduct research, and most importantly, how to communicate our findings with the rest of the world. None of this work would h ave been achievable without his advice, direction and support I also thank my supervisory committee members, Dr. Paul Fishwick, Dr. Douglas Dankel, Dr. Mike Robinson, and Dr. Juan Cendan for their ideas and support in this research. I thank my collaborators in healthcare education for their work and insight: Dr. Carole Kimberlin and Dr. Diane Beck for their participation in the pharmacy study I thank Dr. Scott Lind, Dr. Adriana Foster, Dr. Adeline Deladisma, Dr. Hevil Shah Dr. Michael Crary, and Dr. Juan Cendan for their work on virtual patient s and their invaluable feedback on Virtual People Factory T hank you to my collaborators in the Virtual Experiences Research Group: Dr. Aaron Kotranza, Dr. Kyle Johnsen, Dr. Andrew Raij, Dr. Regis Kopper, Joon Chauh, and Shiva shankar Halan The previous and ongoing work of these research collaborators inspired the methods and systems described in this dissertation. Further, t heir friendship, advice, and commiseration made the process of completing this research possible and often even fun Lastly, I thank my family. I thank my parents Jan and Joel, for raising me to believe I can take on anything. And I thank my wife, Elizabeth, for her support through all the bumps and turns of my Ph.D. journey. This research was funded by a University of Florida Alumni Fellowship and Na tional Science Foundation Grant (IIS 0643557)
5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 TABLE OF CONTENTS ................................ ................................ ................................ .. 5 LIST OF TABLES ................................ ................................ ................................ ............ 8 LIST OF FIGURES ................................ ................................ ................................ ........ 10 LIST OF ABBR EVIATIONS ................................ ................................ ........................... 12 ABSTRACT ................................ ................................ ................................ ................... 13 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 15 1.1 Problem Statement ................................ ................................ ........................... 17 1.2 Research Questions ................................ ................................ ......................... 19 1.3 Overview of Approach ................................ ................................ ....................... 20 1.3.1 Human centered Distributed Conversational Modeling ........................... 21 1.3.2 Conversational Knowledge Reuse: Virtual Human Templates and Dynamic Knowledge Sharing ................................ ................................ ........ 23 1.3.3 Conversational Knowledge Reuse: Virtual Human Bootstrapping ........... 25 1.4 Thesis ................................ ................................ ................................ ............... 26 1.5 Innovations ................................ ................................ ................................ ....... 26 2 REVIEW OF LITERATURE ................................ ................................ .................... 28 2.1 Interpersonal Simulation: Virtual Human Training Applications ........................ 28 2.1.1 Healthcare Interview Training ................................ ................................ .. 29 2.1.2 Virtual Patient Simulation ................................ ................................ ........ 30 2.1.3 The Interpersonal Simulator ................................ ................................ .... 32 2.1.4 Challenges in Modeling Healthcare Training Conversations ................... 34 2.2 Conversational Modeling ................................ ................................ .................. 35 2.2.1 The Conversation specific Problem Space ................................ .............. 37 2.2.2 The Challenges of Knowledge Acquisition ................................ .............. 38 2.2.3 Addressing the Challenges of Creating Conversational Corpora ............ 42 3 HUMAN CENTERED DISTRIBUTED CONVERSATIONAL MODELING ............... 44 3.1 Overview ................................ ................................ ................................ ........... 45 3.2 Implementation: Virtual People Factory ................................ ............................ 49 3.2.1 Virtual People Factory Server ................................ ................................ .. 51
6 3.2.2 VPF Web based Clients ................................ ................................ .......... 51 3.2.3 Interaction Interface ................................ ................................ ................. 52 3.2.4 Implementation of Error Gathering ................................ .......................... 53 3.2.5 Editor System ................................ ................................ .......................... 54 3.2.6 Suggestions System ................................ ................................ ................ 55 3.2.7 VPF Web Service API ................................ ................................ ............. 57 3. 3 Evaluations of Human centered Distributed Conversational Modeling and Virtual People Factory for Healthcare Interview Training ................................ ..... 59 3.3.1 Evaluation 1: Speed of creating a virtual patient ................................ ..... 60 220.127.116.11 Methods ................................ ................................ ......................... 60 18.104.22.168 Results ................................ ................................ ........................... 63 22.214.171.124 Discussion ................................ ................................ ...................... 66 3.3.2 Eval uation 2: Perceived Efficacy of Virtual People Factory for Healthcare Education ................................ ................................ .................... 69 126.96.36.199 Methods ................................ ................................ ......................... 69 188.8.131.52 Results ................................ ................................ ........................... 72 184.108.40.206 Discussion ................................ ................................ ...................... 74 3.3.3 Evaluation 3: Usability for healthcare education ................................ ...... 75 220.127.116.11 Methods ................................ ................................ ......................... 75 18.104.22.168 Results ................................ ................................ ........................... 76 22.214.171.124 Discussion ................................ ................................ ...................... 79 4 CONVERSATIONAL KNOWLEDGE REUSE: VIRTUAL HUMAN TEMPLATES AND DYNAMIC KNOWLEDGE SHARING ................................ ............................. 81 4.1 Overview ................................ ................................ ................................ ........... 82 4.2 Virtual Human Templates ................................ ................................ ................. 84 4.2.1 Virtual Human Template Creation ................................ ........................... 85 4.2.2 Conversational Model Generation ................................ ........................... 86 4.2.3 Conversational Model Refinement ................................ .......................... 89 4.3 Dynamic Knowledge Sharing ................................ ................................ ............ 89 4.4 Study ................................ ................................ ................................ ................. 92 4.4.1 Methods ................................ ................................ ................................ ... 92 4.4.2 Population ................................ ................................ ............................... 94 4.4.3 Procedure ................................ ................................ ................................ 94 4.4.4 Data A nalysis ................................ ................................ .......................... 97 4.4.5 Results ................................ ................................ ................................ .. 100 126.96.36.199 Analysis of method usage ................................ ............................ 101 188.8.131.52 Analysis of method quality ................................ ........................... 107 184.108.40.206 Analysis of method efficiency ................................ ....................... 108 4.4.6 Discussion ................................ ................................ ............................. 112 220.127.116.11 How to most effectively spend time when conversational modeling ................................ ................................ ............................... 113 18.104.22.168 The effect of the Conversational Knowledge Reuse methods ...... 114 22.214.171.124 Comparison of the six conversational models .............................. 115 4.5 Technical difficulty of the methods vs. their efficiency ................................ ..... 117
7 4.6 Current Data and Future Work on Factors Influencing the Speed of Conversational Modeling ................................ ................................ ................... 1 19 4.6.1 The Complexity of the Scenario ................................ ............................ 119 4.6.2 The Availability and Motivation of Novice Interviewers and VH Authors 121 4.6.3 Improvement from Novice Interactions ................................ .................. 122 4.7 Potential for New Application Areas ................................ ................................ 125 5 CONVERSATIONAL KNOWLEDGE REUSE: USING VIRTUAL HUMANS TO BOOTSTRAP THE CREATION OF OTHER VIRTUAL HUMANS ........................ 127 5.1 Over view ................................ ................................ ................................ ......... 128 5.2 Roleplay Trainer Creator: Generating Virtual Versions of the Human Partner 133 5.2.1 Overview ................................ ................................ ............................... 133 5.2.2 System Implementation ................................ ................................ ......... 134 126.96.36.199 Selecting questions ................................ ................................ ...... 134 188.8.131.52 Determining question order ................................ .......................... 137 184.108.40.206 Simulating the roleplay partner ................................ .................... 139 5.3 Pilot Study ................................ ................................ ................................ ....... 140 5.3.1 Population ................................ ................................ ............................. 141 5.3.2 Procedure ................................ ................................ .............................. 142 5.3.3 Metrics ................................ ................................ ................................ ... 142 5.3.4 Results ................................ ................................ ................................ .. 142 5.3.5 Discussion ................................ ................................ ............................. 143 5.4 Interpersonal Training Applications ................................ ................................ 144 6 CONCLUSIONS ................................ ................................ ................................ ... 145 6.1 Review of Results ................................ ................................ ........................... 145 6.2 Real World Usage ................................ ................................ ........................... 146 6.3 Future Work ................................ ................................ ................................ .... 147 LIST OF REFE RENCES ................................ ................................ ............................. 149 BIOGRAPHICAL SKETCH ................................ ................................ .......................... 157
8 LIST OF TABLES Table page 3 1 Conversational Modeling Time Requirements for Centralized Conversational Modeling vs. Human Centered Distributed Conversational Modeling ................. 64 3 2 Conversational models created since the completion of the study. .................... 72 3 3 Percentage of participants reporting 7 10 (good excellent) on Surgical History Patient with Melanoma post interaction survey ................................ ...... 72 3 4 Percentage of participants reporting 4 5 (good excellent) on Psychiatry Patient with Depression post interaction survey ................................ ................. 73 3 5 Percentage of participants reporting 4 5 (good excellent) on Neurological Patient with Meningitis post interaction survey ................................ ................... 73 3 6 Comparison of content elicited in browser versus Interpersonal Simulator interviews with Psychiatry Patient having Bipolar Disorder ................................ 74 3 7 Results of the domain expert survey on Educational Value, Usefulness, and Ease of Use of Virtual People Factory. Responses were rated from 1 unlikely, to 7 likely. ................................ ................................ ........................... 77 4 1 Example virtual human patient template ................................ ............................. 86 4 2 Virtual Humans created as part of the experimental study. Scenario in which the patient is the baby and the VH is the mother. ................................ ............... 96 4 3 The time required to create each virtual human, and the size of each virtual human conversational model, ordered by the accuracy of the models. Scenario in which the patient is the baby and the VH is the mother. Anne Animus is shown for illustration purposes and not included in mean or s.d. ..... 100 4 4 The percentage of stimuli from each source in each VH conversational model. Shown for illustration purposes and not included in mean or s.d. ........ 103 4 5 The percentage of stimuli from HDCM and DKS that were accepted into each VH conversational model. Shown for illustration purposes and not included in mean or s.d. ................................ ................................ ................................ .. 104 4 6 Percentage of each stimulus source used for responses during interactions of each conversational model. Shown for illustration purposes and not included in mean or s.d. ................................ ................................ ................... 105 4 7 Percentage of overlapping stimuli based on keyword vector analysis in the conversational models. Along the left is the source model, along the top is
9 the model used for matching. Shown for illustration purposes and only included in mean and s.d. specific to Anne Animus. ................................ ......... 106 4 8 Percentage of overlapping keywords in the conversational models. Shown for illustration purposes and only included in mean and s.d. specific to Anne Animus. ................................ ................................ ................................ ............ 107 4 9 Quality (accuracy) of each stimulus source for each conversational mod el. accuracy. Shown for illustration purposes and not included in mean or s.d. ... 108 4 10 The efficiency of time spent on each conversational model. Efficiency is in accuracy gained per hour spent. Conversational modeling time and conversational accuracy are included for comparison. Shown for illustration purpose s and not included in mean or s.d. ................................ ....................... 109 4 11 Time spent on each method for each conversational model. Author interactions are the author interacting with their own VH. Maintenance tasks include signing the license agreement, changing account settings, and changing character images. Conversational Mod el accuracy included for comparison. Shown for illustration purposes and not included in mean or s.d. ................................ ................................ ................................ .................... 109 4 12 Efficiency of eac h method in terms of the number of stimuli per hour for each conversational model. Shown for illustration purposes and not included in mean or s.d. ................................ ................................ ................................ ...... 110 4 13 Suggestions received per utterance for each conversational model. Shown for illustration purposes and not included in mean or s.d. ................................ 111 4 14 Summary of the usage quality and efficiency of each method. ....................... 113 4 15 Number of stimuli for medications taken by Vic Johnson ................................ 120 4 16 Number of stimuli and accuracy of responses during interactions with Marty Graw for each round of testing ................................ ................................ ......... 123 4 17 Topics covered in the Marty Graw conversation ................................ ............... 124
10 LIST OF FIGURES Figure page 1 1 The flow of data in Centralized Conversational Modeling. All of the conversational data is piped through the knowledge engineer and into the corpus, causing a bottleneck. ................................ ................................ ............. 18 1 2 The flow of data in Human centered Distributed Conversational Modeling. Conversational data flows from Experts and Novices into the Virtual Human System, which processes information and stores it in the corpus. ...................... 22 2 1 Overview of the Interpersonal Simulator (Johnsen, 2008). ................................ 33 2 2 The problem space definition. Bold lines lead to the problem space addressed by the conversational modeling methods in this dissertation. ........... 38 3 1 The Human centered Distributed Conversational Modeling process. ................. 48 3 2 Virtual People Factory System Overview the system is divided into the web application and the web service subsystems. The web application provides browser based clients. The web service supports other client interfaces such as Second Life, Android, and the Interpersonal Simulator. ................................ 50 3 3 The Virtual People Factory Architecture using Windows, Apache, MySQL, and PHP ................................ ................................ ................................ ............. 51 3 4 The Virtual People Factory browser based interaction interface ........................ 53 3 5 The flow of knowledge in the Virtual People Factory system implementation of Human centered Distributed Conversational Modeling ................................ .. 55 3 6 The Suggestion System interface to enter a new response for are a list of the relevant responses in the corpus. The author adds the new s ................................ ....... 56 3 7 The accuracy of the dyspepsia conversational model for each group TA s.d. = 13.3%, S1 s.d. = 6.7%, S2 s.d. = 5.3%, represented by the error bars ........... 65 3 8 Accuracy of the Centralized Conversational Model vs. Human centered Distributed Conversational Model for 33 spoken transcripts, improvement of 4.1% is significant at p < .05. CCM s.d. = 11.1%, HDCM s.d. = 9.7%, represented by the erro r bars. ................................ ................................ ............ 66
11 3 9 Student participant ratings on the educational value of interacting with the virtual patient. ................................ ................................ ................................ ..... 68 4 1 The construction and use of a virtual human template to construct conversational models in the Virtual Human Templates system ......................... 85 4 2 A screenshot of the Virtual Patient Generator used in the study. The Virtual Patient Generator interface was programmatica lly constructed by the Virtual Human Templates system based on the virtual patient template. ...................... 87 4 3 The faces available for select ion in the Virtual Patient Generator ...................... 88 4 4 Overview of the Dynamic Knowledge Sharing process. 1: Novice users interact with the V H using its conversational model; 2: the system simulates unknown stimuli are validated by VH authors, paired with a response, and placed in the conversational model. ................................ ................................ ... 91 4 5 Conversational Knowledge Reuse evaluation study procedure .......................... 95 4 6 Student groups presenting VHs they created to the class and instructor ........... 97 4 7 Mean percentag e of stimuli in the conversational models from each method. DKS s.d. = 9.21%, VHT s.d. = 5.72%, HDCM s.d. = 6.56%, Manual s.d. = 8.46%, represented by the error bars ................................ ............................... 102 5 1 The virtual human medical student creation process, an example of virtual human bootstrapping ................................ ................................ ........................ 129 5 2 The Roleplay Trainer Creator interface for selecting questions by usage ........ 136 5 3 The Roleplay T rainer Creator interface for viewing automatically ordered interview questions and reordering the questions ................................ ............. 139 5 4 The Roleplay T rainer virtual medical student interaction interface ................... 140 5 5 Survey results of participant's self assessed pre post preparednes s and pre post confidence ................................ ................................ ........................ 143
12 LIST OF ABBREVIATION S VH Virtual human. A computer simulation of a human conversational partner. CCM Centralized Conversational Modeling. A typical approach of conversational modeling which follows the patterns established in the field of expert systems, in which a knowledge engineer acquires domain specific knowledge, and then translate s that knowledge i nto a machine readable format (section 1.1) HDCM Human centered Distributed Conversational Modeling. A novel human centered approach to conversational modeling used to facilitate natural language conversation ( section 1.3.1, C hapter 3). VHT Virtual Human Templates. A novel method of Conversational Knowledge Reuse for conversational modeling based on the knowledge acquired for previously created similar conversational models (section 1.3.2, section 4 .2 ) D KS Dynamic Knowledge Sharing A novel method of Conversational Knowledge Reuse for conversational modeling based on the knowledge acquired for conversational models that are still undergoing development (section 1.3.3, section 4 .3 ) VPF Virtual People Factory. A web based application that imp lements the conversational modeling methodologies described in this dissertation (section 3.2)
13 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy DESIGN AND EVALUATION OF CONVERSATIONAL MODELING METHODS FOR INTERPERSONAL SIMULATION By Brent H Rossen December 2011 Chair: Benjamin Lok Major: Computer Engineering Interpersonal ski lls practice using virtual humans offer s structured learning of interview skills provides standardization of practice, and can facilitate learning about unusual conditions. However, t he creation of virtual humans with the ability to understand and respond to natural language requires cost ly engineering by conversation knowledge engineers (generally computer scientists) and incurs logistical cost for acquiring domain knowledge from domain experts ( generally educators) We address these problems using two novel method s entitled Human center ed Distributed Conversational Modeling and Conversational Knowledge Reuse These method s facilitate collaborative development of virtual humans by two groups of end users: domain experts (educators) and domain novices (students) We implemented the s e metho d s in a web based authoring tool called Virtual People Factory. Using Virtual People Factory, medical and pharmacy educators create natural langu age virtual patient interactions This dissertation presents the theoretical background for Human centered Dist ributed Conversational Modeling and Conversational Knowledge Reuse ; the implementation of the Virtual People Factory authoring tool s ; and studies showing that these methods have reduced the logistical cost of acquiring knowledge and thereby
14 improved the feasibility of applying virtual human based interpersonal simulation to real world education
15 CHAPTER 1 INTRODUCTION Virtual h umans (VHs) for natural language conversation are becoming increasingly popular for communication skills training. A VH is a computer simulation of a human conversational partner VHs can communicate using speech, gestures, and facial expressions. VHs for interperson al skills training have been shown to be useful for providing structured learning of interview skills and decreasing anxiety in potentially uncomfortable interviews (Deladisma et al., 2007b; Johnsen, 2008; Raij, 2009) They are also predicted to be useful for providing diverse interview experiences (Johnsen, 2008; Rossen et al., 2008) Projects in medicine, psychology, and the military have been created with collaborative effort between end users (educators and students), artists, programmers, and conversation knowledge engineers (generally computer scientists) (Dickerson et al., 2005; Johnsen 2008; Kenny et al., 2007; Kenny et al., 2008; Kotranza, 2009; Raij, 2009; Villaume et al., 2006) However, these collaborators find acquiring the knowledge necessary t o simulate these co nversations, and processing that knowledge into a conversational model to be logistically difficult and time consuming (Dickerson et al., 2005; Glass et al., 2005; Kenny et al., 2007; Kenny et al., 2008; Villaume et al., 2006) A conversat ional model is a digital representation of the knowledge necessary to conduct a conversation. Conversational models are used in VH simulations to recognize and respond to speech inputs. Preparing a VH to conduct a free form conversation usually takes hundr eds of hours over several months. For example, Vic is a VH created to play the role of a patient having stomach pain. Vic is capable of ten minute s of free form conversation about his symptoms with a pharmacy student. Even for ten minute s
16 of conversation, a team of knowledge engineers and domain experts spent approximately 200 hours and over 6 months t conversational simulation ( Chapter 3 ) The labor intensive nature of conversational model development restricts the number of V Hs that can be created. As a result of such impractical time and expertise requirements, educators have been unable to create the number of scenarios needed to implement an interpersonal skills training curriculum and are even f urther from creating a dive rse training curriculum (Plant et al., 2005; Triola et al., 2007) To address these problems, we propose two new methods based on human centered computing (Sebe, 2010) The first method Human centered Distributed Conversational Modeling (HDCM ) is a novel crowdsourcing method for d eveloping conversational models The second method Conversational Knowledge Reuse is a novel method for reusing portions of previously acquired conversational knowledge to ma ke new conversational models Both of these methods center around minimizing the human effort involved in the process of collecting conversational knowledge and assembling that knowledge into a conversational model. Applying HDCM and Conversational Knowled ge Reuse to the problem of conversational modeling improves the efficiency of human effort used to model a conversation This efficiency results in a significantly shorter time to produce a more accurate conversational model than previous methods This reduction in time and effort increases the feasibility of implementing interpersonal skills training curricula using VHs The methods described in this dissertation provide insights that will open new areas of VH applications and make research possible tha t would have been difficult or
17 impossible to implement using previous methods. Thus, in developing and evaluating these methods for constructing VH conversational models, this dissertation provides innovations to both the study of VH user interfaces and th eir practical applica tion in interpersonal simulation 1.1 Problem Statement The proposed methods address the modeling of natural language conversations for interpersonal skills education using VHs Simulating a natural language conversation requires a robust conversational model that can recognize and respond accurately to a wide range of inputs To develop a robust conversational model knowledge engine ers must acquire a conversation specific corpus reflecting what the user s will say to a VH (stimulus) and what the VH will say back (response) (Dickerson et al., 2005; Kenny et al., 2007; Kenny et al., 2008; Leuski et al., 2006; Reiter et al., 2003; Ruttkay et al ., 2004) The stimuli response space of these conversations is large, and often consists of thousands of stimuli linked to hundreds of responses. Typical conversational modeling methods follow th e patterns established in the field of expert systems, in which a knowledge engineer acquire s domain specific knowledge, and then translate s that knowledge into a machine readable format (Shortliffe, 1976) Knowledge acquisition and engineering is cited as the biggest barrier in expert systems and natural language system s development (Reiter et al., 2003; Wagner, 1990) The time required for knowledge acquisition and translation limits the utility of k nowledge engineer based methods for conversational modeling To develop n atural language conversational models k nowledge engineers acquire d information from the following resources : 1) recordings of people in natural or staged interactions ; 2) Wizard of Oz (human controlled) VH interactions ; and 3) expert
18 descriptions of real world interactions (Ruttkay et al., 2004) The co nversational knowledge engineer uses these resources to seed the conversational modeling process by gather ing the starting stimuli and translating those stimuli into a machine readable conversational corpus. The k nowledge e ngineer then refine s the knowledge corpus by collecting knowledge from VH interactions with users reviewing these user interactions, making change s to the corpus based on conversation errors, validating the corpus changes by consulting experts, and then repeat ing the process (Dickerson et al., 2005; Kenny et al., 2007; Kenny et al., 2008; Leuski et al., 2006) We will hereafter refer to the described method as Centralized Conversational Modeling (CCM) because of the k nowledge e information from experts and novices to the conversation corpus ( conversational knowledge database) as shown in Figure 1 1 Because all of the kn owledge is flowing through the k nowledge e ngineer, t he CCM process is slow, and experts report that communication bottlenecks are often frustrating (Dickerson et al., 2005; Glass et al., 2005; Kenny et al., 2007; Kenny et al., 2008; Villaume et al., 2006) Additional details on the challenges presented in this problem statement are provided in s ection 2.2 .2 Figure 1 1 The flow of data in Centralized Conversational Modeling. All of the conversational data is piped through the k nowledge e ngineer and into the corpus causing a bottleneck.
19 1.2 Research Questions The applicability of VH interpersonal simulation to real world purposes is limited by t he inefficiency of the process described in the problem statement. Based on this overview of the problem, we define three broad research questions, which we address through the research presented in this dissertation: Res earch Question 1: Wha t are the bottlenecks involved in the creation and use of virtual humans in interpersonal skills training? This question is the starting point for understanding the problems we are confronted with, the current challenges in generating virtual human s Analyse s of the existing material on the creation of VH conversational models were performed. These analyses provided insights into the challenges of generating VHs the bottlenecks that slow down the conversational modeling process. Research Question 2: What conversational modeling methods can we design to alleviate the problems caused by the bottlenecks identified in question 1? The answers to this question will provide designers and researchers with new methods for modeling virtual human conversations The two methods presented are novel approaches to generating VH s These methods are designed to improve the efficiency of conversational modeling, and thereby reduce the time required to create robust conversational models Research Question 3: Does use of the software implemented based on the methods designed in response to question 2 result in efficient creation of robust conversational models ? This question aims to validate the conversational modeling methods by showing that they are implementable and that robust conversational models can be created more efficiently than previous methods Following the proposed methodologies, we implemented systems that follow each method. These systems are shown to facilitate the effic ient creation of VH conversational models that are robust to a wide range of inputs. By addressing this question, we provide evidence that the methods succeeded in providing direction for the design of efficient conversational modeling software for use in a real world domain
20 1 .3 Overview of Approach The research into new conversational modeling methods was carried out through an iterative process of analysis, design, implementation and evaluation ( research questions 1 3 ) We analyzed the current state of the art of VH conversational modeling to determine bottlenecks in the process of the creation of diverse sets of VHs ( research question 1 Chapter 2). From this first analysis the problem statement was derived (section 2.2). We address ed the issues presented in the problem statement by applying lessons from human centered design (Sebe, 2010) crowdsourcing (Howe, 2006; Singh et al., 2002) and human computation (von Ahn and Dabbish, 2004) T he combination of human centered design, crowdsourcing and human computation offers inspiration for novel solution s to acquiring knowledge and p rocessing that knowledge into VH conversational model s Based on these concepts, this dissertation presents two new methods fo r the efficient creation of VH conversational models HDCM and Conversational Knowledge Reuse ( research question 2 Chapter 3 4 5 ) We implemented t hese methods in a web based application called Virtual People Factory. Virtual People Factory was evaluated for use in interpersonal skills training in the healthcare field (Filichia et al., 2010; Foster et al., 2010a; Foster et al., 2010b; Palathinkal, 2011; Peden et al., 2011; Pileggi and Childs, 2011; Rossen et al., 2010; Rossen et al., 2012; Rossen et al., 2009; Rossen and Lok, 2012; Shah et al., 2008; Shah et al., 2012; Shah et al., 2009a; Shah et al., 2009b; Surkunalingam et al., 2009) This dissertati on presents three evaluations of the conversational modeling methods implemented in Virtual People Factory as well as a meta analysis of the HDCM method used in real world education ( research question 3 Chapter 3 4 5 ).
21 1.3.1 Human centered Distributed Conversational Modeling The Virtual People Factory implementation of the HDCM method was evaluated first by the researchers in collaboration with healthcare professionals (Rossen et al., 2009) and then independently by additional healthcare professionals to as sess the system within real world contexts (Foster et al., 2010a; Foster et al., 2010b; Peden et al., 2011; Pileggi and Childs, 2011; Shah et al., 2008; Shah et al., 2012; Sha h et al., 2009a; Shah et al., 2009b; Surkunalingam et al., 2009) T hese evaluations were analyzed, and the advantages and limitations for the HDCM process were de termined ( research questions 1 3 Chapter 3 ) (Rossen and Lok, 2012) In contrast to the CCM method described in the problem statement, the HDCM method proposes that VH users (as opposed to knowledge engineers) generate the model themselves through a crowdsourcing process Using HDCM, doma in experts and novices collaborate to teach the VH how to converse. HDCM results in a flow of data, Figure 1 2 that does not have the knowledge engineer bottleneck a s in CCM, Figure 1 1 Domain novices speak with the VH, which gathers new stimuli, and the domain experts add new responses to these stimuli. The VH system itself pro cesses t he knowledge, which eliminates the CCM bottleneck of piping information through the k nowledge e ngineer. Effectively, the novices and the expert collaborate to teach the VH how to conduct this domain specific conversation this process is both human centered and embodies the ideas of crowdsourcing with human computation. HDCM applies the ideas of c rowdsourcing (Howe, 2006; Singh et al., 2002) and h uman c omputation (von Ahn and Dabbish, 2004) to the problem of enumerating the stimuli response space of a conversation. Our evaluation results demonstrate that HDCM sho rtens the time to model the conversation, and the resulting VH
22 conversational model is more comprehensive. Further, the domain experts drive the HDCM process, thus allowing the experts to direct the creation of VH based training curricula and to focus the material on learning goals (Chapter 3 ). Figure 1 2 The flow of data in Human centered Distributed Conversational Modeling. Conversational d ata flows from Experts and Novices into the Virtual Human System, which processes inform ation and stores it in the corpus. There are, however, limitations to the HDCM process including expert time requirements and end user (novice) availability (Rossen and Lok, 2012) W hile the time is shorter than the amount previously required m any experts still perceive the process to be difficult (Chapter 3 ). HDCM also requires a large group of end users. Further, these users must interact with VHs who do not yet have a response to many stimuli, meaning the conversational models will have a high error rate. These in development VHs may provide limited educational value f or novices (students) helping to create the conversational model. We were therefore motivated to make maximal use of the knowledge acquired from novice interactions; this motivation inspired the development of the Conversational Knowledge Reuse method s
23 1. 3.2 Conversational Knowledge Reuse : Virtual Human Templates and Dynamic Knowledge Sharing To alleviate the identified issues in HDCM and to expand on the app licability of HDCM, we designed, implemented, and evaluated two additional conversational modeling methods based on the concept of Conversational Knowledge Reuse Conversational Knowledge Reuse is the idea that we may leverage knowledge acquired for one VH in the creation of a different VH. The methods implementing Conversational Knowledge Reuse are Vir tual Human Templates and Dynamic Knowledge Sharing These new methods were implemented within the Virtual People Factory application. We then re evaluated the Virtual People Factory application within the context of a healthcare course to examine if studen ts could create VHs themselves, a task previously found to be infeasible ( research questions 1 3 Chapter 4 ) (Villaume et al., 2006) Conversational Knowledge Reuse alleviate s the limitations of HDCM and expand s the applicability of VHs The concept of Conversational Knowledge Reuse was based on the observation of significant overlap in knowledge between similar conversational models. Much of the knowledge acquired in the initial stages of HDCM consists of basic questions that are common to the interaction domain (medicine/psychology/military). seeding cover basic utterances used in conversations; typical questions include and Repeatedly acquiring this knowledge for each conversational model takes up the time of both novice and expert users thereby using up two of our limited resources Conversational Knowledge Reuse allows us to leverage this overlap of knowledge to seed new conversational models base d on previous conversational models. For the
24 Virtual Human Templates method, k nowledge e ngineers extract information from existing conversational models and generalize that knowledge into a Virtual Human Template The Virtual Human Template is then customized by a VH author and that knowledge is used to generate a new seeded conversational model ( Chapter 4 ) Virtual human templates streamline the process of seeding the conversational model The initial seeding allow s the subsequent use of HDC M to gather stimuli that are more specific to the current sc enario, thereby using novice time more efficiently. A second method of leveraging the similarity of information in conversational models is dynamically sharing data between closely related convers ational models. When similar conversational models are created at the same time, we can improve these models by making sure that each of the conversational models can answer questions asked of any of the conversational models. This method is called Dynamic Knowledge Sharing and is embedded in the Virtual People Factory system. During the conversational modeling process, the Virtual People Factory system automatically gathers questions asked of othe r similar conversational models. The system then examines if each conversational model contains each question, and shares questions the conversational model does not currently know. By sharing knowledge between the similar conversational models the models can be created more rapidly and cover a greater portion of potential inputs. This again allows the process to further leverage the knowledge provided from novice users. The advantages of Virtual Human Templates and Dynamic Knowledge Sharing further reduce d the time requirements for enumerating the stimulus response space of a conversation. In Chapter 4 we explore the use of these methods in the context of a
25 healthcare course, and examine if by using these methods healthcare students can rapidly generate ro bust VH patients a task that was previously infeasible due to time requirements The report on this evaluation presents a detailed analysis regarding the sources of conversational knowledge, the efficiency of the conversational modeling process, and the q uality of the resulting conversational models 1.3.3 Conversational Knowledge Reuse: Virtual Human Bootstrapping We present an additional Conversational Knowledge Reuse method for reusing acquired knowledge to bootstrap the creation of new VHs known as Vi rtual Human Boots t rapping Virtual Human Bootstrapping allows authors to reverse the roles of VH interactions. The previously described VHs have a fixed role in a conversation. Medical students practice interviewing by conversing with a VH patient (Johnsen et al., 2005) ; teachers re enact classroom sit uations with virtual students (Dieker et al., 2007) ; and soldiers learn conflict resolution in interactions with virtual civilians (Hill et al., 2003) During development of these human VH interactions, the role played by the human and the role played by the VH are fixed. In the examples given, the soldier, teacher, and doctor are huma ns, while the civilian, student(s), and patient are VHs. We have observed that reversing these roles (e.g. a human patient with a VH doctor) will facilitate interpersonal skills training for additional large populations (Rossen et al., 2010) However, traditional methods of creating a VH for ea ch role would double development time and effort. With Virtual Human Bootstrapping authors can rapidly reverse the roles in these conversations, thereby allowing VHs to play the previously human side of the interaction ( research questions 1 3 Chapter 5 ) We implemented t he Virtual Human Bootstrapping method in an application called The Roleplay Trainer Creator The Roleplay Trainer Creator generates VHs that are
26 compatible with the Virtual People Factory system. In C hapter 5 w e describe a study which used The Roleplay Trainer Creator to generate a VH medical student from the conversational knowledge of a VH patient This VH medical student was then used for training standardized patient actors to conduct a practice medical interview (Rossen et al., 2010) To the best of our knowledge, t rai ning standardized patients using VHs is a previously unexplored area of VH applications and this evaluation demonstrates the feasibility of applying VHs to this new application area. 1 .4 Thesis We introduce the concept s of HDCM and Conversational Knowledg e Reuse for conversational modeling. Thesis Statement: T he proposed conversational modeling methodologies improve the efficiency of the human effort used to model a conversation. I mproved efficiency result s in a significantly shorter time to produce more accurate conversational models than previous methods This reduction in time and effort enhances the applicability of virtual humans to real world interpersonal skills education 1.5 Innovations This dissertation provides innovations in the areas of Human Computer Interaction in the subfi eld of Interpersonal Simulation and Artificial Intelligence in the subfield of Conversational Modeling. The i nnovations include the conversational modeling method ologies novel software implementations and evaluations of their efficacy Conversational Modeling Methods We designed the methods of Human centered Distributed Conversational Modeling (Chapter 3 ) and Conversational Knowledge Reuse (Chapter 4, 5 ). The Conversational Knowledge Reuse concept is embodied in three methods: Virtual Human Templates, Dynamic Knowledge Sharing
27 and Virtual Human Bootstrapping. These methods were designed based on analyses of the current state of conversational modeling for virtual humans. Software We implemented the conversational modeling methods in software for creating VH conversational mod els for interpersonal skills training. These methods were implemented within Virtual People Factory (Chapter 3 4 ) Virtual Human Te mplates (Chapter 4 ), and The Roleplay Trainer Creator (Chapter 5 ) These implementations have been released for research use and have been shown to be effective for real world educational training applications (Filichia e t al., 2010; Foster et al., 2010a; Foster et al., 2010b; Halan et al., 2010; Halan et al., 2012; Jackson, 2010; Palathinkal, 2011; Shah et al., 2008; Shah et al., 2009a; Shah et al., 2009b; Surkunalingam et al., 2009) Evaluations The Human centered Distributed Conversational Modeling method was evaluated first in collaboration with a single expert, and then a meta analysis was conducted on the method used in real world practice (Rossen et al., 2009; Rossen and Lok, 2012) The Conversational Knowledge Reuse method s o f Virtual Human Templates and Dynamic Knowledge Sharing were evaluated within the context of a healthcare training c ourse wherein student participants created their own VH conversational models and used those models for practicing their interview skills (Rossen et al., 2012) The Virtual Human Bootstrapping method was evaluated by an expert participant, and a pilot study was performed investigating the applicability of the generated VHs to training (Rossen et al., 2010) The methods and software were evaluated in multiple real world scenarios and found to be efficient for the creation of robust VH conversational models for interper sonal skills training (Chapter 3 4 and 5 ).
28 CHAPTER 2 REVIEW OF LITERATURE 2.1 Interpersonal Simulation: Virtual Human Tr aining Applications Researchers have developed VH interpersonal simulation to provide the advantages of standardization, availability, immediate feedback, and diversity. Compared to human actors, VH conversational partners provide a greater degree of stand ardization and more consistent learner experiences (Johnsen, 2008; Kenny et al., 2008) VH partners are often autonomous, and so can provide anytime anywhere training (Johnsen, 2008; Rizzo et al., 2010) Through after action reviews, VH interactions can provide immediate feedback, and detailed visualizations can enhance learning experiences (Raij, 2009) VH interpersonal simulation can also provide diverse experiences such as abno rmal findings (cranial nerve damage, a breast mass, or a facial b urn) (Kotranza, 2009) ethnically diverse visual representations (Rossen et al., 2008) and using the work descr ibed in this dissertation they can provide a greater variety of conditions and experiences (Rossen and Lok, 2012) Because of these advantages, researchers have begun adopting VH interpersonal simulation in fields where interpersonal skills are essential to job performance. These fields include military leadership (Deaton et al., 2005; Hill et al., 2003; K enny et al., 2007; Rizzo et al., 2010; Traum, 2008) mental health assessment (Ke nny et al., 2008; Rizzo et al., 2010) and medical interviewing (Hubal and Day, 2006; Manganas et al., 2004; Stevens et al., 2005) In military simulation s users learn tactics and negotiation strategie s by interacting with VHs civilians, combatants, and fellow team members (Deaton et al., 2005; Hill et al., 2003; K enny et al., 2007; Rizzo et al., 2010; Traum, 2008) In the field of mental
29 health care clinical psychology students practice clinical interview ski lls and learn strategies to motivate patients to seek further counseling (Foster et al., 2010a; Foster et al., 2010b; Kenny et al., 2008; Parsons et al., 2008; Rizzo et al., 2010) In medicine, medical students learn patient interviewing skills with virtual patients (Hubal et al., 2000; Johnsen, 2008; Kotranza, 2009; Raij, 2009) The collective goals of these research efforts are to examine the efficacy of learning experie nces (Deladisma et al., 2007a; Parsons et al., 2008; Shah et al., 2008) to examine the feasibility of modeling human behavior with VHs (Dickerson et al., 20 05; Kenny et al., 2008) to evaluate user behavior during and after interactions (Deladisma et al., 2007b; Iacobelli and Cassell, 2007; Zanbaka et al., 2007) and to construct cognitively accurate dialogue modeling architectures (Ellaway and McGee, 2008; Traum, 2008) Through that research, e ffective interaction has progressed, but speed and independent function of domain experts r emains problematic (Huang et al., 2007; Triola et al., 2007) The solutions presented in this dissertation focus on making these VH technologies practical. With rapid and accurate knowledge acquisition, we inc rease the potential for widespread adoption of VHs as training tools. 2.1.1 Healthcare Interview Training The conversational modeling methods proposed in this dissertation were developed within the field of healthcare interview training. Healthcare educators use interview training to help students develop their patient interviewing skills. Patient interviewing skills are crucial in all areas of healthcare. Interpersonal skills training results in both improved patient care and reduced lawsuits (Jenkins and Fallowfield, 200 2; Vincent et al., 1995) Healthcare students traditionally train for these interviews by interacting with real patients in various healthcare environments and shadowing clinical
30 practitioners (Barrows, 1987) Given the importance of accurate diagnoses resulting from patient interview s there is demand for additional patient interview practice (Itin, 1999) For this reason, healthcare students practice their inter viewing skills through interactions with standardized patients (Barrows, 1993) Standardized patients are actors trained to play the role of a patient. However, due to the expense of hiring and training actors, students get few of these standardized patient interactions (Parsons et al., 2008; Tamblyn et al., 2009) This limitation is one of the reasons educators in the healthcare fields w ant to provide virtual patients before students interact with standardized patients or real patients 2.1.2 Virtual Patient Simulation Educators use v irtual patient simulation as preparation for standardized patient and real patient interviews. Just as flight simulators help prepare pilots for real flight, interpersonal simulators, such as virtual patient simulators, help prepare healthcare students for real interpersonal intera ctions (Johnsen, 2008; Kotranza, 2009; Raij, 2009) Several studies have shown that virtual patients can be used to evaluate and improve cognitive and behavioral skills as well as or better than t raditional methods (Huang et al., 2007; Johnsen et al., 2007; Kamin et al., 2002; Kotranza e t al., 2009b; Leong et al., 2003) Virtual patient simulation can bridge the gaps in healthcare student education by exposing them to patient disease states that they may otherwise not experience and provide a safe environment to make mistakes (Nutter and Whitcomb, 2001) Virtual pat ient simulation facilitate s interpersonal skills training using lifelike clinical scenarios in which the user becomes the healthcare professional and practices procedural, diagnosis, and communication skills Virtual patients present a condition (stomach ulcer, breast cancer, depression, etc.), and the goal of the user (healthcare
31 develop rapport with the patient through e mpathy, professionalism, and proper procedure. based simulations designed to complement clinical training. The majority of existing virtual patient systems are web based text and video systems (Hayes and Lehmann, 1996; Huang et al., 2007; Leong et al., 2003; Shah et al., 2008) Healthcare students interact with virtual patients by: typing, (Benedict, 2010; Bergin and Fors, 2003; Ellaway and McGee, 2008; Hubal et al., 2000; Johnsen et al., 2007; Kenny et al., 2008 ) The virtual patient is represented either as recorded videos of an actor, animated videos, still images, or a 2D or 3D animated virtual character. Virtual patients are displayed on a monitor, in a web browser, or life size with a project o r, large screen TV, or head mounted display. In this dissertation, we focus on improving typed and sp oken natural language conversations with virtual patients, regardless of representation or presentation. Evaluation of virtual patient applications has shown educational benefit, but they are costly to develop, which makes them available to few medical sch ools (Huang et al., 2007) T he successful projects have been developed with significant collaborative effort from both domain experts and computer science experts (VH developers). The creators of these VHs report that it is logistically difficult and time consuming to create the nece ssary conversational models (Dickerson et al., 2005; Kenny et al., 2007; Kenny et al., 2008; Triola et al., 2007; Villaume et al., 2006) A survey of 142 US m edical s chools
32 reported that 80% of virtual cases cost more than $10,000 and have a median production time of 17 months; further, these virtual patient cases tend to have limited racial and ethnic diversity and few cases are produced (Huang et al., 2007) The proposed methods will make the creation of virtual human patients faster and less costly. 2.1. 3 The Interpersonal Simulator T he conversational models created using the methods described in this dissertation are compatible with many output mediums (see section 3.2.7) and were specifically designed to facilitate VH interactions using the Interpersonal Simulator (Johnsen et al., 2006; Rossen et al., 2009) The Interpersonal Simulator allows users to interact naturally (speech and gestures) with VHs (Johnsen, 2008) The Interpersonal Simulator has been developed as a collaborative effo rt of many researchers, and is not claimed as a contribution of this dissertation (Deladisma et al., 2007a; Dickerson et al., 2005; Johnsen, 2008; Kotranza, 2009; Lind and Lok, 2006; Peden et al., 2011; Raij, 2009; Stevens et al., 2006) The work described in this dissertation provides enhancements to the applicability of the Interpersonal Simulator to real world interpersonal skills education An overview of the Interpersonal Simulator setup is shown in Figure 2 1 Speech interaction is enabled by a spoken language dialog system. The system uses a custom speech recognizer based on the Microsoft Speech API called Exact and Dictation Speech Recognizer fo r continuous speech recognition. Realistic VH body meshes have been provided using a variety of softwa re including Autodesk's Maya, Di o Facial Studio open source Object oriented Graphics Rendering Engine (OGRE). The characters
33 employ both skeletal animation (arm movements, leg movements, and head turning) and morph animation (breathing, blinking, lip synching, and emotional expressions). They also employ a realistic gaze model der ived from research on controlling user impressions with gaze (Fukayama et al., 2002) The VHs are displayed life sized using a projector or large screen display. Figure 2 1 Overview of the Interpersonal Simulator (Johnsen, 2008) The Interpersonal Simulator was validated for use in medical interpersonal skills education (Deladisma et al., 2007b; Dickerson et al., 2005; Johnsen, 2008; Johnsen et al., 2007; Raij e t al., 2007) By combining the Interpersonal Simulator with a sensor enhanced mannequin, researchers found the VH interactions could also improve psychomotor and affective performance in a clinical breast exam (Kotranza, 2009; Kotranza and Lok, 2008; Kotranza et al ., 2009b) Further, researcher s have found that interactions with VHs can elicit racially biased behavior that is predictive of real world
34 racial biases (Rossen et al., 2008) ; and after action reviews can facilitate self reflection on biases displayed during the interactions (Raij, 2009; Raij et al., 2009; Raij and Lok, 2008) Dete a step towards changing biases, and indicates that VH interpersonal skills may be useful for diversity training (Raij, 2009; Rossen et al., 2008) These positive findings have created a dem and for more VH training simulations. For many interpersonal training goals, tens to hundreds of VHs will be required (Plant et al., 2005; Triola et al., 2007) However, using the previous conversational modeling methods, the time and monetary costs required for the creation of VH conversational models makes these training goals infeasible. 2.1. 4 Challenges in Modeling Healthcar e Training Conversations The creation of a virtual patient requires significant collaborative effort between healthcare educators and c omputer scientists (knowledge engineers) (Dickerson et al., 2005; Glass et al., 2005; Kenny et al ., 2007; Kenny et al., 2008; Villaume et al., 2006) As computer scientists, we rarely have the knowledge to create healthcare scenarios on our own Healthcare educators have that knowledge; however, educators do not have the technical expertise necessary to create v irtual patients capable of natural language conversations. A system that enables educators to construct virtual patients for healthcare interview training without computer science expertise would enhance development efficiency and expand utilization. Exis ting systems which allow creation of virtual patients by healthcare educators are restricted to multiple choice interactions or structured queries (Benedict, 2010; Fall et al ., 2005) In multiple choice interactions, students choose their questions and statements from a predefined list. In structured query interactions the users are not
35 given the predefined list directly, each word typed into a text box brings up a menu of questions they can ask that contain the word. These predefined list interactions focus on the fact finding mission in order to reach a diagnosis. In contrast, the inte rpersonal simulation system s addressed by this dissertation use natural conversation to train interviewing skills. Improving interview skills requires practicing those skills, and natural interaction performs better than linear or forced branching for trai ning those skills (Bearman et al., 2001; Johnsen et al., 2007; Saleh, 2010; Yedidia and Lipkin, 2003) Using natural conversation the student interviews the virtual patient by asking questions about present health and past medical history in the own words, in any order. Prior to the work described in t his dissertation the researchers who developed the Interpersonal Simulator created three virtual patient s using CCM. The se first three virtual patients were created over the course of four years. The creation of each virtual patient using CCM required app roximately 200 hours and over 6 months. Because of the limited number of cases created these v irtual patient systems have been useful in a narrow scope T o provide wide spread functional benefit to medical schools tens to hundreds of virtual patient scenarios are necessary, resulting in prohibitive time and cost requirements (Huang et al., 2007; Triola et al., 2007) The proposed conversational modeling methods reduce this time significantly, and thus inc rease the potential for virtual patient based curricula. 2.2 Conversational Modeling There are several aspects to the creation of conversational VHs including: Visual: how virtual humans appear
36 N on verbal : face and body animations, tracking the user, eye gaze and other non verbal modes of communication C onversational : utterances that VHs understand and how VHs respond In this dissertation, we focus on the verbal or conversational aspect of VH creation. VHs conduct natural language conversations (as opposed to multiple choice) using un annotated corpus retrieval approaches and are primarily question answering agents (Dickerson et al., 2005; Kenny et al., 2007; Kenny et al., 2008; Leuski et al., 2006; Leuski and Traum, 2010; Roque and Traum, 2007) The corpus of a VH consists of stimulus response pairs of what the users will say to the VH ( stimuli / questions) and what the VH w ill say back (response). When the user asks a question, the system searches the corpus for the m ost similar question and provides the paired answer using keyword vector matching or statistical distribution methods (Dickerson et al., 2005; Leuski and Traum, 2010) For example, if a user asks will respond with A question answering VH is capable of simulating a healthcare patient for a medical intervie w (Johnsen, 2008; Raij, 2009) This is possible because of the structure of the medical interview itself. The interview occurs within a restricted domain and is driven by the interviewer (medical student). The restricted domain allows a corpus to enumerate the space of a conversation. However, if the interviewer discusses a topic outside of the restricted doma in, it is likely that the VH will not have a response, or will respond incorrectly. In the interview, the interviewer asks questions and the VH patient responds. This is a simplified interaction when compared to human human interviews. While human human i nterviews are still primarily question answering, the human patient
37 may provide backchannel information that is limited or non existent in a purely question answering conversation. Despite this simplification, the VH patient simulation is able to provide b eneficial question answering interactions. The methods described in this dissertation are used to generate VHs that conduct conversation specific question answering or question asking interactions (Rossen et al., 2010; Rossen et al., 2009) We define c onversation specific VHs to be those designed to know only what they need to know for their particular conversation. They do not search for ad ditional information or ad lib, they remain in character to provide a realistic interpersonal experience (Dickerson et al., 2005; Leuski et al., 2006; Rizzo et al., 2010) 2.2.1 The C onversation specific Problem Space Conversation specific agents are distinct from open domain and domain specific conversational agents. Conversation specific VHs focus on providing an interpersonal experience of a specific conversation, with the goal of facilitating experiential learning (Itin, 1999; Johnsen et al., 2005) The methods proposed in this dissertation address the problem of acquiring knowledge for conversation specific conversational models. Open domain conversat ional agents focus on passing the Turing T est (appearing indistinguishable from a human conversational partner) and not on delivering information (Mauldin, 1994; Wallace, 2005) Their goal is believability as a conversational partner and they often employ conversational tricks to accomplish this goal ; such as repeating statements back as questions or using distracting statements (Colby et al., 1971; Weizenbaum, 1966) Domain specific conversational agents typically focus on helping users complete a task such as information search, travel booking, or diagnosis (Goh et al., 2007)
38 We propose a definition of the conversational modeling problem space illustrated in Figure 2 2 Conversational modeling is a subfield u nder the field of Artificial Intelligence Wi thin that subfield, there are modeling approaches that depend on the level of domain specificity. Each level of domain specificity has a certain goal. The conversational modeling methods described in this dissertation address one type of domain spe cificity conversation specific, a nd address one of the problems with modeling conversations at that level of specificity, knowledge acquisition. Figure 2 2 The p roblem space definition Bold lines lead to the problem space addressed by the conversational modeling methods in this dissertation. 2. 2 2 The Challenges of Knowledge Acquisition The creation of conversation specific VHs versus open domain and domain specific conversational agents differs in that conversational tricks are coun ter productive and the data for specific conversations does not exist on the internet or in other easily accessible corpora resources. Conversation specific VH authors have to collect data themselves (Ruttkay et al., 2004) The majority of incorrect responses in this type of conversational model come from unanticipated topics of conversation (Dickerson et al.,
39 2005) This symptom of unanticipated topics indicates a problem with knowledge acquisition for creating conversation specific conversational models. In order for conversational models to respond accurately to a wide variety of inputs, they require a corpus that enumerates the stimulus response space of a conversation (Dickerson et al., 2005; Reiter et al., 2003; Rossen et al., 2009) Using CCM, VH authors have encountered the following challenges to creating corpuses that enumerate the stimulus r esponse space of a conversation: Challenge 1: Expert s lack knowledge detail ed enough for generalization Accu rate conversational models require thousands of stimuli paired with hundreds of responses (Chapter 3 4 ). interactions and asking experts is not detailed enough for generalization (Reiter et al., 2003) Experts are likely to come up with a small fraction of the required number of stimuli. For example, in the study described in Chapter 3 the pharmacy educator was unable to anticipate the 174 syntactical ways to ask the 53 semantically unique questions about Aspirin. The educator was able to predict only 10 o f these semantically unique questions. In a previous study, we have seen that unanticipated stimuli account for the majority of errors (51%) in a conversation modeled using CCM (Dickerson et al., 2005) Challenge 2: Experts phrase questions and conduct interviews differently than novic es do. The target user group for interpersonal skills training is domain novices. Domain e xperts do not phrase questions the same way novices do. With more experience, experts use shorter, more focused questions and infer information from their past exper iences (Westberg and Jason, 2001) Experts also conduct shorter, more
40 focused interviews, and so cover few of the topics that a novice interviewer will cover. The effect of these differences is that if VH authors acquire knowledge from experts the corpuses do not cover the questions asked by novices. Challenge 3 : Limited u se of existing corpora sources and costly creation of new corpora resources Because of the above challenges of acquiring knowledge from expert sources in order to enumerate the space of a conversation between a VH and a novice, VH authors must acquire knowledge from material originating from novice sources. These novice sources have logistical issues regarding legal use of existing material, monetary c ost, required time, and end user availability. For many human human interactions, there are legal restrictions regarding viewing, particularly in healthcar e; the problem being that non healthcare professionals are not allowed to view real patient interview s. Even with staged interactions, such as students interview ing actors, there are the logistical difficulties of hiring and training actors, issues in standardization and repeatability, as well as monetary costs. Wizard of Oz (human controlled) VH interact ions also have the same drawbacks as using actors in terms of availability, compensation, and standardization. Challenge 4: E xtracting knowledge from corpora sources. An additional problem with novice sources is that of extracting utterances from interac tions. After each set of user interactions, the knowledge engineers review the videos and transcripts from those interactions to extract new stimuli and correct gaps in the corpus indicated by the transcripts. Knowledge engineers determine if non responses were caused by missing stimuli or speech recognition errors ; and if incorrect responses were caused by erroneous similarity between existing stimuli missing stimuli, or speech
41 recognition errors Acquiring knowledge from standard novice sources has a tim e cost for both knowledge engineers and collaborators. Challenge 5 : Limited e xpertise availability and costly collaboration The fifth challenge is one of collaboration. Knowledge engineers may not know the domain, so they must collaborate with domain experts to validate the stimuli and create new responses (Sutton et al., 1996) Before knowledge engineers can begin working on a domain specific VH, they need to learn about the domain from an expert. Even after this education, they are not experts themselves. This means that the knowledge engineers will need to contact the domain expert (e.g. a medical doctor, psychologist, military expert, etc ) every time they wan t to a) validate a new stimulus; or b) create a new response to a stimulus. This collaboration takes time, and there are often communication challenges because of differing backgrounds. In practice, these five challenges result in inadequate initial knowledge in the conversational model, few iterations of user testing, and iteration s of testing having a limited number of users. Thus, the resulting conversation corpus has significant gaps in its stimuli coverage. This causes increased response errors and a decre ased ability for the VH interaction to achieve educational and training objectives. The methods proposed in this dissertation, HDCM and Conversational Knowledge Reuse address these challenges. HDCM and Conversational Knowledge Reuse are new approaches to rapidly acquiring knowledge directly from novices and experts They remove the manual identification of utterances from video by knowledge engineers and they create faster collaboratio n through a distributed system.
42 2.2. 3 Addressing the Challenges of Creating Conversational Corpora The cost of developing conversational models is large in part because of the required size of a corpus to facilitate robust conversations. These conversations require a considerable amount of kn owledge about potential speech inputs and outputs. Generalization requires a large data set (corpus) that covers the unusual boundaries of realistic inputs; that is, inputs that real users would say to a VH. While expert knowledge cannot provide the corpu s with realistic novice inputs, expert knowledge is a good way to validate inputs for this corpus and to produce outputs (Reiter et al., 2003; Rossen et al., 2009) Experts can identify if questions are on topic, and can create patient responses to those questions. HDCM accepts these con straints by using a collaborative authoring tool that collects knowledge from novice users, which is validated by the expert users (see C hapter 3 for details) The idea of engaging end users for knowledge acquisition was explored in Open Mind Common Sense (Singh et al., 2002) The goal of Open Mind Common Sense is to build software agents that are capable of common sense. The project uses an online tool for collaborative knowledge acquisition. Their approach is similar to the construction of other co llaborative web based efforts, such as the Open Directory Project or Wikipedia. The contributors for these projects are motivated to improve the project itself. While these projects have found great success, their approach would not work for communication skills training applications -students are not motivated to engage directly in the process of modeling VH conversations for their own training materials, so HDCM engages them indirectly (Villaume et al., 2006) We find a solution fo r ESP Game (von Ahn and Dabbish, 2004) Von Ahn pointed out that human based computation can
43 solve problems that are still untenable for computers to solve, e.g. tagging images for searching. In the ESP Game online players guess what their game partner is looking at by naming parts of an image. They are motivated because the game is fun. Google has used this game to tag huge numbers of images, thus letting Google search images without processor intensive vis ion techniques. We build upon this work by taking and centered) approach to knowledge acquisition for VH conversations. We use interactions with novices to acquire a corpus of realistic input data. The important les son from the ESP Game is to set up the task so that users accomplish their own goals (learning) in a way that causes a beneficial side effect. In the case of conversational interactions that side effect is teaching a VH to conduct a conversation In the following chapters we present our solution s to the various problem s of acquiring knowledge for interpersonal simulation.
44 CHAPT ER 3 HUMAN CENTERED DISTRIBUTED CONVERSATIONAL MODELING This chapter presents the analysis, design implementation, and evaluation of Human centered Distributed Conversational Modeling ( research questions 1 3 ) It also describes an implementation of Human centered Distributed Conversational Modeling, Virtual People Factory It goes on to report a meta analysis on evaluations of Virtual People Factory in four real world applications The initial evaluation of this method and implementation was published in the proceedings of the International Conference on Intelligent Virtual Agents (Rossen et al., 2009) An extended ver sion discussing additional details on the method and implementation as well as a meta analysis of publications by healthcare professionals using Human centered Distributed Conversational Modeling and Virtual People Factory was accepted to the Internation al Journal of Human Computer Studies and is awaiting publication (five year impact factor = 2.3) (Rossen and Lok, 2012) Personal Contributions I conceptualized the Human centered Distributed Conversational Modeling method, implemented the Vir tual People Factory system, and designed and analyzed the initial study. I also collaborated with healthcare practitioners using Virtual People Factory on several of their studies and publications. Collaborators Dr. Kyle Johnsen Dr. Andrew Raij Dr. Aar on Kotranza, and Scott Lind, M.D. were involved in the discussions of the Human centered Distributed Conversational Modeling method and provided valuable reviews and feedback during the development of Virtual People Factory For Evaluation 1 (below), Dr. Carole Kimberlin provided access to study participants (pharmacy students) and served as a participant herself by re creating the Vic Johnson Dyspepsia virtual human patient.
45 Many other healthcare professionals were involved in the additional studies using Human centered Distributed Conversational Modeling and Virtual People Factory and their publications are referenced in Evaluation 2 (below) Relevance to thesis This chapter reports several studies indicating that educators are not only using Virtual P eople Factory in healthcare education, but they also feel it is providing educational benefit. It provides evidence that Human centered Distributed Conversational Modeling reduces the time to model virtual human conversations, and because of that reduction we have seen an increase in both the number of virtual human s created and in the diversity of the applications to which those virtual human s are being applied. 3. 1 Overview Human centered Distributed Conversational Modeling (HDCM) applies the ideas of cr owdsourcing and human based computation to the challenges of conversational modeling in order to alleviate the bottlenecks of Centralized Conversational Modeling ( CCM ) We see in s ection 1.1 Problem Statement and s ection 2.2 2 The Challenges of Knowledge A cquisition conversational model is collecting knowledge from the experts and novices, and using that knowledge to generate a machine readable corpus. We can remove these duties from the knowledge engineer by providing a guided learning system for use by the human centered because it fits the way domain experts think about creating a virtual human ( VH ) and is a natural method for domain novices to participate in the VH creation process.
46 The process of HDCM is collaboration between domain novices (the learners) and a domain expert (the educator) to teach a VH how to conduct a conversation. The expert enters an initial set of questions and responses. This set is the outline of the upfront cost of creating a VH by allowing the conversation to grow through iterative refinement. Next, the ex pert enlists the help of novices. The novices attempt to conduct a conversation with the VH. The VH will perform poorly during this first interaction by either not having a response to a question, or by responding incorrectly. These errors are logged and a re later displayed for the expert one at a time. The expert then enters new responses to each new stimulus, or matches new stimuli to existing responses. After all new stimuli have been processed and all the new responses have been added to the conversatio n al model, the expert initiates a second iteration. They send the interaction to a larger group of novices. After a few iterations of this process, the expert will start to receive diminishing returns -each new interaction produces fewer and fewer new st imuli (s ee section 3. 3.1 for details and 220.127.116.11 for additional evidence of diminishing returns ) (Rossen et al., 2012; Rossen and Lok, 2012) The end condition for this process is dependent on the complexity of the conversation and the required accuracy. For a VH that needs to discuss few topics and a family history of c
47 require more distinguishing factors and more iterations of testing will be necessary with greater number s of users in each iteration. As a data point, in the evaluation of the relatively complex scenario described in section 18.104.22.168 for a 20 minute conversation to achieve 79% accuracy required three iterations consisting of a total of 186 participants. HDC based reasoning. Case specific previous experiences to come up with a response for the current stimulus (Aamodt and Plaza, 1994) Case based reasoning systems learn by identifying successes and failures in order to solve similar problems in the fu ture. In the context of conversational modeling, the stimuli are user questions/statements, and responses are VH speech. Failures consist of either the VH lacking a relevant response, or the VH response being incorrect. Once a failure is identified, the ex pert enters a correct response so that the system can achieve success in the future. Using HDCM, domain experts and novices asynchronously collaborate to teach the VH how to converse They collaborate through a graphical user interface that is useable with out any knowledge of the technical details of conversational modeling, such as XML or case based reasoning. Figure 3 1 shows the iterative process end users follow for creating a VH conversational model and is described in more detail below.
48 Figure 3 1 The Human centered Distributed Conversational Modeling process. Phase 1: A domain expert seeds the VH Conversational model with their best guesses as to what will be said to the VH and what the VH should say back. Phase 2: Multiple novices have a typed conversation with the VH. The system collects new stimuli when the VH does not have a response, and when it responds incorrectly. Phase 3: A domain expert enters responses to which the VH could not respond, or to which the VH responded incorrectly. Phase 4: Phase 2 and 3 are repeated until an acceptable accuracy is reached. In practice, the acceptability of t he accuracy is determined by the domain expert. Through interactions with a VH, the domain novices enumerate the space of what will be said to the VH; while domain experts enumerate the space of what the VH will say back. During interactions with the novic e, the system gathers three types of errors true negative false negative and false positive A true negative error occurs when a user provides a stimulus, and the system cannot find any response because there is no appropriate response in the corpus. W ith a false negative there is an appropriate
4 9 response, but the system fails to match the stimulus to that response. A false positive occurs when an inappropriate response is given, based on a mismatch of stimulus to an item in the corpus. The system enter s these errors into the list of new stimuli. After gathering errors, the expert adds new stimuli and responses to the conversation corpus to facilitate future accurate responses. Compared to CCM, iterations of HDCM are completed faster, and can involve a g reater number of end users. A major barrier to using VHs in curricula is time (Huang et al., 2007) By shortening the iteration cycle and easing the distribution, we shorten the time to create a VH and increase the potential for these systems to be used in the real world. This proces s is a learning system that generates a corpus enumerating the space of a conversation. That corpus forms the basis of a VH conversational model for corpus retrieval conversations. Th e HDCM method is implemented in the web based application Virtual People Factory (VPF). 3. 2 Implementation: Virtual People Factory VPF is a platform for the development and deployment of VHs using the HDCM method. VPF provides interfaces for both expert and novice users as well as a web service for communicating with external applications. The application consists of three subsystems: 1 a browser based interaction system, 2 a VH editor system, and 3 a web service developer API. These three parts are used asynchronously to create conversational models and interact with VHs. VPF's conversational models are referred to as scripts Scripts are use d in the simulation of a conversation. Scripts consist of both the set of stimuli and responses (corpus) as well as supporting tags such as associated animations,
50 emotions, audio speeches, and images. VPF supports script creation through the collaboration of novice users and expert users. The novice users have a client interface to interact with the VHs, and the expert users have a set of interfaces for creating and the coll aborative design and deployment of VH scripts ( Figure 3 2 ) Figure 3 2 Virtual People Factory System Overview t he system is divided into the web application and the web service subsystems The web application provides browser based clients T he web service supports other client interfaces such as Second Life, Android, and the Interpersonal Simulator. Definitions : Script: a representation of the knowledge of a VH (including the stimuli response corpus, animation tags, audio tags, emotion tags, etc ) Text r esponse: the text of a speech based response Audio file: a tag indicating an audio file to play as part of a response Animation: a tag indicating an animation to play as part of a response, as well as the timing of the animation
51 Emotion: a tag of an emotional facial expression to make as part of the response, as well as the timing for the movements involved expressing the emotion Discovery: a tag indicating an important piece of information contained in the respo nse Topic: a tag indicating the subject or theme of the response 3. 2.1 Virtual People Factory Server As shown in Figure 3 3 VPF uses a client server architecture. The server portion of VPF runs on a single PC server containing a Core2 Duo Quad Core processor and 4GB of RAM running the Apache Web Server on Microso ft Windows. The VPF Server runs on the open source software components: PHP Scripting Language, MySQL Database, and the MySQL Ajax Database Access Layer (Rossen, 2010) All commu nication is performed over http using Ajax calls. Data is marshaled and passed from one application to another in JSON or XML format across http by the VPF Web Service API Figure 3 3 The Virtual People Factory Architecture using Windows, Apache, MySQL, and PHP 3. 2.2 VPF Web based Clients The VPF web client s run on the user's local machine in a web browser such as Firefox, Chrome, Safari, or IE7+. The client side of VPF is divided into two syste ms; the
53 In Figure 3 4 information, including the patient we see a transcript of an interaction. Figure 3 4 The Virtual People Factory b rowser b ased i nteraction interface 3. 2.4 Implementation of Error Gathering in this process are gathering errors and facilitating error correction. As described at the end of section 3. 1 during interactions VPF gathers three types of errors true negat ive false negative and false positive True negative and false negative errors, where the VH does not respond at all, are automatically added into a log of new stimuli by VPF. However, VPF cannot reliably identify false positive s. False positive s result from a mismatched stimulus, where the VH did respond, but incorrectly. For example, if the Tums Aspirin all out the wrong medication. Accordingly, when the VH responds incorrectly, the instructions ask at the bottom of the transcript in Figure 3 4 ). Pressing that button logs the false positive
54 error as a new stimulus for the expert to validate later. After gathering errors, the expert uses the VPF Editor System to correct errors by process ing the resulting list and adding new stimuli and responses to the conversation corpus. 3. 2.5 Editor System Domain experts use the VPF Editor System to create and edit VH Scripts The VPF Editor System has facilities for editing scripts, sharing scripts w ith other authors sending scripts to students as VH interactions, and then analyzing and processing the interactions. A design goal of the editing system is to minimize the cognitive load on the tem quickly and successfully accomplish tasks (Lidwell et al., 2003) To this end, users start off with access to the basic features, but can request access to more advanced features -providing access to only the basic features at first helps users to get started quickly with creating a question response VH. The advanced features allow users to divide the script into acts, add audio, add free form xml, and add animation tags. These advanced features provide facilities for use in non VPF interactions such as Second Life and the Interpersonal Simulator (see section 3. 2.7 fo r additional details). VPF has multiple methods to input questions and answers. The manual way is using the Edit Scripts interface. On this page, users see the list of speech responses, and the set of questions (stimuli) that will trigger those responses. Experts annotate these responses with animations, audio, emotions, discoveries, and topics. The recommended way for authors to seed new question response sets is to converse with the character using the Test Script interface. The Test Script interface allo ws the Script Editor to conduct a conversation with their VH within the VPF Editor System During the Test Script conversation, when the VH does not have a response,
55 the expert can immediately enter a new response, or connect the stimulus to an existing re sponse. Experts play both sides of the conversation and seed the conversational model for future interactions with students. After the conversational model is seeded, the expert sends out invitations for students to interview the VH These invitations are automatically gene r ated using VPF's Groups System The Groups System allows experts to add students to the system, and then track their progress. As the students perform interactions, the educator expert can view transcripts and analyze performance. These student interactions also improve the conversational model. Each time a new utterance is encountered; this information is stored in the S uggestions S ystem The overall flow of knowledge is shown in Figure 3 5 Figure 3 5 The f low of knowledge in the Virtual People Factory system implementation of Human centered Distributed Conversational Modeling 3. 2.6 Suggestions System The S uggestions S ystem displays new stimuli to the expert one at a time. Since these conversational models often grow to include hundreds of responses, it becomes difficult to recall the correct response for a given stimulus. To alleviate this problem, t he suggestions syst em provides help in selecting appropriate responses that already exist in the conversational model
56 A screenshot of the interface is shown in Figure 3 6 In this example, f or the new stimulus, "Have you been the system has provided a list of likely responses (the rightmost list in the image) The user has selected one of these likely responses and the system has provided a l ist of similar responses The list of similar responses is used when the script editor would like to select a different but similar, response. The user can also use free text to enter a new response, and the system will find similar responses. Once the u ser presses the Make Change button, the system connects the new stimulus to the existing response, or if a new response was entered, the new stimulus response pair is added to the script. This design leverages the greater ease and speed of recognition me mory over recall memory to improve the experts efficiency in processing new stimuli and improving the conversational corpus (Lidwell et al., 2003) Using this interface, script editors can r apidly process new stimuli and either connect them to existing responses, or add new stimulus response pairs into the conversational model (details on the efficiency of this interface shown in section 22.214.171.124) Figure 3 6 The Suggestion System interface User Input is the utterance spoken by the interviewer, Enter Response is the space to enter a new response for the virtual human, Similar Responses are populated as the author types in a response and fills in the response when selected and Likely Responses are
57 a list of the relevant responses in the corpus The author adds the new stimulus and response pair to the corpus by pressing Make Change or ignores the suggestion by pressing Do Not C hange 3. 2.7 VPF Web Service API VPF is intended as a backend system for generating VHs, and then deploying those VHs using a variety of clients. For this reason, VPF was written using client server principles, and provides a web service API for interfaci ng with the conversational engine. Through the VPF Web Service API VPF VHs can be deployed using a variety of clients. With this technique, users can experience the same VH conversation using different interfaces. The VPF Web Service API was implemented by extending a MySQL Ajax Database Access Layer web service (Rossen, 2010) In a standard MySQL Ajax Database Access Layer web service, each table of the database is represented as its own class. These classes use object relational mapping to allow clients of the web service to access any VH data in the database (after security authentication) The basic web service was extended to provide a conversational simulation API, includin g a speech understanding service and transcript service. Through these services, the VPF Web Service API allows VPF VHs to be used as the backend simulation system for a variety of VH interfaces. Each interface uses the same conversational engine. So far, VPF has been integrated with four interface clients the web browser interface (described above), Android mobile platform, Second Life, and The Interpersonal Simulator. The VPF Android interface simulates VH interactions using text and voice inputs. Users speak into the phone, and receive both text and audio responses. Currently, th e image only. A small screenshot of the interface is shown in
58 Figure 3 2 above The VPF Android interface can be downloaded from the Android Marketplace under the name Virtual Patient VPF also supports distribution of VHs using Second Life through the VPF Second Life application. Using VPF with Second Life provides a body for the VH, support of VH interaction with many users, provides users with their own avatar, and supports user created 3D content (Ullrich et al., 2008) The use of VPF Second Life requires no programming or XML editing. VPF Second Life is a middleware application; VPF VHs are loaded into Second Life by providing a VPF username, password, and script information; S econd Life username, password and a location in S econd Life for the VH to appear; and VPF Second Life then uses libopenmetaverse in combination with the VPF Web Service API to connect Second Life with the VPF server (Freedman et al., 2010) The VPF Second Life application can be downloaded from the VPF home page after login. The VPF Second Life application is used by healthcare practitioners at the University of South Florida to provide virtual clinical skills labs, where VPF characters are constantly available for healthcare student practice (Jackson, 2010) The Interpersonal Simulator allows users to interact with a life size VH and conduct natural language spoken conversations (s ection 2.1.3) Users walk up to these characters and speak; when they speak, an automatic speech recognizer transcribes their words to text. The text is translated into XML queries and sent to the VPF Web Service API VPF provides speech and animation resp onses, which the Interpersonal Simulator renders to an immersive environment.
59 VH interfaces as well. These future interfaces will be created using the same VPF backend th rough the web service API. Using the same backend for many front ends promotes reuse of conversational models created using one client interface to be leveraged for many client interfaces. 3. 3 Evaluations of Human centered Distributed Conversational Modeli ng and Virtual People Factory for Healthcare Interview Training We evaluated HDCM to establish the efficacy of this method for generating conversational models. We further examined if VPF can be used for HDCM by experts in real world practice without assis tance from the VPF developers. Last, we established the usability of VPF and examined limitations of the current version of VPF. The goal of these evaluations was to understand the impact of the HDCM method on conversational modeling and establish if the c urrent implementation of VPF is usable in real world educational settings. We separated the evaluation into three parts: Evaluation 1: a case study evaluation of HDCM and VPF on the creation of one conversational corpus (section 3. 3.1 ) Evaluation 2: a meta evaluation of 4 published case studies that further examine student and educator real world experiences with HDCM and VPF (section 3. 3.2 ) Evaluation 3: a usability evaluation based on self reported feedback from experts on the efficacy of HDCM and VPF in real world educational settings (section 3. 3.3 ) We evaluate HDCM using an in depth examination of the creation of one conversational corpus. We further report on the creation of four additional conversational corpuses and their efficacy for use in healthca re education. Last, we discuss self reported expert feedback on the usability of VPF to create virtual patients and educate students.
60 3. 3.1 Evaluation 1: Speed of creating a virtual patient In this evaluation, we examine if the HDCM approach enables expert s to create conversational models, reduces conversational modeling time requirements compared to CCM and results in a conversational model with increased accuracy for spoken interactions. 3. 3.1.1 Methods To evaluate HDCM, a Dyspepsia (discomfort centered in the upper abdomen) conversational model was developed for an Introduction to Pharmacy Communications course taught in spring of 2008 at the University of Florida College of Pharmacy. The character for t his scenario is named Vic. At minimum, Vic needed to discuss the following topics: Chief Complaint of stomach pain, Age, Weight, Gender, Blood Pressure Readings, Thyroid Readings, Fears of Cancer, Risk Factors (Smoking, Alcohol, Drugs, Allergies), Medical Problems (Hypertension, Hyperthyroidism, Back Spasms), Medications (Zestril, Synthroid, Aspirin, Tums), and his p arents Medical History (Father died of colon cancer, Mother died of a heart attack). Vic needed extensive domain specific knowledge in order t o converse about these topics. This scenario was previously generated using CCM, and the original conversational model is used for comparison (Johnsen, 2008) In t he current study, the pharmacy instructor (domain expert) and pharmacy students provided domain knowledge using the HDCM process. Here is the HDCM process the pharmacy expert followed to create Vic ( generic version shown in Figure 3 1 above ): of questions and responses. To do this, she played the role of a student and asked Vic questions, and responded to those questions herself.
61 web based interface. This iden tified missing stimuli for which Vic did not respond, or responded incorrectly. Phase 3: The Pharmacy Instructor added new responses for the new stimuli, or connected the new stimuli to existing responses. Phase 2 and 3 were repeated three times. Participants Two classes of participants were involved in the study, a domain expert, and two types of domain novices: Domain Expert : t he pharmacy instructor had standard computer experience with word processing and email. The instructor was motivated to participate by a desire to Domain Novices : t he pharmacy instructor recruited pharmacy students from her Introduction to Pharmacy Communication Skills course. The participants consisted of 12 teaching assista nts (TAs) and 174 second year pharmacy students. Participant ages ranged from 20 to 60 with an average of 25.44. The pharmacy students received extra credit in the course for interviewing Vic for a minimum of 10 minutes and 25 questions Procedure The p harmacy instructor uploaded a comma separated list of student names and emails into the VPF Groups system ; VPF generated customized links for each student, and sent out emails. Students went to the website where they completed a consent form, conducted a t yped interview, and completed a post interview questionnaire. Participants had two weeks to conduct their interview at their own convenience. Data Analysis Data analysis was divided into three parts: 1) conversational modeling time; 2) con versation accur acy improvements; and 3) accuracy in comparison to a previous CCM model. Part 1 established the amount of time required to model a
62 conversation using HDCM and VPF. Part 2 established the trend of changing accuracy during each iteration of HDCM. And part 3 established if the resulting HDCM conversational model is more accurate than the previously created CCM conversational model. The HDCM conversational model was created using interactions with the TAs and students of the Introduction to Pharmacy Communicati ons Skills course. Users were divided into three iterations of model improvement, the first 12 teaching assistant participants (group TA), the next 44 student participants (group S1), and the remaining 130 student participants (group S2). The 12 TA interac tions were conducted prior to student interactions in order to seed the system and provide a more developed system for the first round of students. The two student groups were divided based on when the expert processed the first set of student suggestions. The expert processed the first set of suggestions near the end of the first week of the study, and continued to process suggestions throughout week two. Since students participated online, and at their own convenience, the grouping of participants into S1 and S2 was self selected. The students who chose to participate in the first week were included in S1; the students who chose to participate in the second week were included in S2. In section 3. 3.1.2 we show the number of unique questions and responses a cquired from these interactions, as well as the percentage of accurate responses with each group of users. We further evaluated this conversational model for accuracy with spoken inputs using transcripts of previously acquired spoken interactions with 33 working professionals in pharmacy. These 33 transcripts were collected during a previous study of the Interpersonal Simulator. The scenario in the previous study is the same as the scenario for the HDCM
63 model examined in the c urrent evaluation (Vic Johnson with dyspepsia). The CCM model for that study was created by three knowledge engineers and two Pharmacy Experts using interactions with 51 pharmacy students. Creation of the CCM model required 6 months and approximately 200 h ours. During the Interpersonal Simulator study, 35 participants interacted with a life size VH using spoken inputs. Spoken inputs were recognized and transcribed using Dragon Naturally Speaking 9.5. For comparative analysis, two of the 35 transcripts were removed from the test set due to speech understanding errors caused by accents. The transcripts from the remaining 33 interactions are used below to compare the accuracy of the conversational model created using HDCM to the model of the same scenario crea ted using CCM. 3. 3.1.2 Results Conversational Modeling Time Part 1 of the evaluation examined the progress of conversational modeling during the two weeks of development and how much total time was required of the domain expert. There were three iteratio ns of conversational modeling improvement group TA, group S1, and group S2. Participants interacted for an average of 20 minutes, making the total student time 62 hours. These three rounds of user testing required 15 hours of expert time (including 2 hou rs of training time and 13 hours of suggestion processing and script editing) over a period of 2 weeks and created a conversational corpus consisting of 2655 stimuli and 595 responses, these results are summarized in Table 3 1 alongside the results for the previously created CCM model.
64 Table 3 1 Conversational Modeling Time Requirements for Centra lized Conversational Modeling vs Human Centered Distributed Conversational Modeling Method Creators Interactions Expert Time Novice Time Stimuli Responses CCM Knowledge E ngineers, Pharmacy Experts, 51 Students Spoken Interactions ~200 Hours (combined educator and knowledge engineers) 11 Hours (13 Minute Average) 1418 303 HDCM Pharmacy Instructor & 186 Pharmacy Students Virtual People Factory: Web browser 15 Hours 62 Hours (20 minute average) 2655 595 Conversation Accuracy Improvements Part 2 of the evaluation examined the trend of accuracy change for each group of participants. We evaluated the interaction transcripts for accuracy by reviewing the response to each participant question. We marked the response as accurate if there was a s emantic link between the stimuli and response (Leuski et al., 2006) ; meaning there was a respons e and it was correct responses that were accurate for all of group TA, and a simple random sample of 10 transcripts from groups S1 and S2. Figure 3 7 shows the percentage of responses that were accurate for all of group TA, and a random 10 transcripts from groups S1 and S2. The standard deviation of these samples is represented by the error bar in the figure, and exact standard deviations are provided in the caption. This analysis was performed only to establish a trend of increasing accuracy; the important accuracy is how well the conversational model performs with spoken inputs in compariso n to the previously created CCM conversational model. The final accuracy analysis with spoken inputs is provided in the next subsection.
65 Figure 3 7 The accuracy of the dyspepsia conversational model for each group TA s.d. = 13.3%, S1 s.d. = 6.7%, S2 s.d. = 5.3%, represented by the error bars Accuracy with Spoken Inputs Part 3 of the evaluation compared the accuracy of the current HDCM conversational model to the accuracy of a previously created CCM model for spoken i nputs. After the testing and improvements of the case study, we examined the performance of the HDCM model with spoken transcripts and compared that accuracy to the performance of a conversational model created using CCM. To run the comparison, we analyzed the transcripts from 33 spoken interactions between pharmacy students and the previous VH patient with dyspepsia. During the interactions, Dragon Naturally Speaking 9 .5 was able to transcribe spoken utterances at 83.3% accuracy. In order to analyze the s poken transcripts against the CCM and HDCM conversational models, we first removed inaccurate utterances due to speech recognition errors from the transcripts (16.7%). Utterances from the spoken transcripts were designated accurate if a human reader would have been capable of responding correctly. We then processed the remaining utterances using both the HDCM and CCM conversational models. Utterances were processed by feeding each utterance as a stimulus into a simulated conversation using each
66 conversation al model. We then analyzed the accuracy of each response. Accuracy analysis revealed 74.5% accuracy (s.d. = 11.1%) per transcript for the conversational model created with CCM while the one created with HDCM had 78.6% accuracy (s.d. = 9.7%) per transcript for the 33 spoken transcripts. The accuracy data follows a normal distribution with a Shapiro Wilk significance of .353 for CCM accuracy and .320 for HDCM accuracy (where values greater than .05 indicate a normal distribution). Samples for this analysis we re paired because each transcript was processed using both conversational models. Using a paired samples T test on the accuracy numbers for each transcript, we see a significant difference at p < .05 with t = 2.4. A summary of these results is shown in Figure 3 8 Figure 3 8 Accuracy of the Centralized Conversational Model vs. Human centered Distributed Conversational Model for 33 spoken tra nscripts, improvement of 4.1% is significant at p < .05. CCM s.d. = 11.1%, HDCM s.d. = 9.7% represented by the error bars. 3. 3.1.3 Discussion The results of this case study indicate that HDCM saves expert and developer time in creating the speech understa nding portion of a conversational model in
67 comparison to CCM. Using HDCM for conversational modeling yielded a significant 4.1% improvement for spoken interactions in ~7.5% of the expert time. Further, the conversation corpus created with HDCM has increase d depth in the topics that students most frequently asked about. For example, there are only 44 questions about Aspirin in the corpus created with CCM, while there are 174 questions about Aspirin in the HDCM corpus. From this difference, w e see that the ph armacy students concentrated on the medications the patient was taking, and HDCM led to a much larger number of medication related stimuli and responses, and thus allows a more nuanced conversation. Using HDCM, the p harmacy i nstructor was able to develop Vic in approximately 15 hours over 2 weeks, compared to the knowledge engineers and pharmacy experts creating Vic in ~200 hours over 6 months. Table 3 1 shows the differences in time input and conversational model output resulting from using the CCM and HDCM methods. We see that there is a decrease in the expert time by ~92.5% and increase in the total novice time by 545.5%. Involving this many novices in the conversational modeling process is possible because of the reduced logistical constraints provided by HDCM. Given such a large amount of novice data and an effective method for processing this data, the pharmacy instructor was able to create a corpus of nearly double the size of the CCM method. The pharmacy instructor reported an additional advantage of the expert being directly involved; she was able to come up with new stimuli as she processed student tion would remind the p harmacy i nstructor of other stimuli and responses that should be in the conversation. The instructor would see a
68 should also be able to answer if he is taking adult Aspirin, baby Aspirin, or Enteric Coated Aspirin. As a result, she would add new stimuli and responses so Vic could discuss those topics. Feedback from both the pharmacy educator and pharmacy students stated that the experience was educatio nally beneficial. Surveys from student participants (groups 10), 28% neutral (ratings 5 6) and 23% negative (ratings 1 4). A breakdown of individual student ratings can be seen in Figure 3 9 Figure 3 9 Student participant ratings on the educational value of interacting with the virtual patient. easy with minimal training, and that the effort is worthwhile because the scenario can be
69 3. 3.2 Evaluation 2: Perceived Efficacy of Virtual People Factory f or Healthcare Education The success of VPF in the pharmacy domain prompted healthcare educators to use VPF in additional healthcare domains. Since the initial pharmacy study, healthcare educators have used VPF to create 23 additional scenarios used for tea ching healthcare interview skills, we report on four of these scenarios. These four scenarios were chosen because they have been used for published research (Foster et al., 2010b; Shah et al., 2012; Shah et al., 2009a; Surkunalingam et al., 2009) Healthcare experts successfully created these convers ational VHs using VPF with minimal phone and email assistance from knowledge engineers Below we report on a meta evaluation of the parts of their studies that per tained to VPF, in particular, the perceived educational value and usability of VPF. 3. 3.2.1 Methods In this evaluation, we analyzed a series of studies on the perceived efficacy of VPF VHs for healthcare education. The VHs were authored using VPF at the Me dical College of Georgia (now named the Georgia Health Sciences University) and the Philadelphia College of Osteopathic Medicine. Because each of these studies was performed independently, their methods differ. We highlight aspects of each study that evalu ate the efficacy of VPF for healthcare education from a student perspective. Each of the studies received IRB approval before participants interacted with the virtual patient Students participated in the studies on a voluntary basis, and the only inclusio n criteria was enrollment in the surgical, psychiatry, pharmacy, or osteopathy program using VPF. Each of these educational interventions was used in addition to
70 traditional training methods such as standardized patient interactions and expert mentoring. S urgical Patient with Melanoma educational benefit for medical education. Researchers at the Medical College of Georgia evaluated VPF for use in their medical curriculum (Shah et al., 2009a) With minimal phone and email assistance of knowledge engineers, the medical educators conceptualized the patient scenario, seeded the virtual patient conversational model, knowledge base. The patient, Hank Lowry, is a 58 year old male with suspicious skin lesions on his back, chest, and shoulder. The medical educators followed the same process outlined for creating the pharmacy patient in evaluation 1. After receiving a lect ure on obtaining patient history, 51 first based interaction. After the interaction, participants completed a survey regarding the Psychiatry Patient with Dep ression educational benefit for first, second, and third year psychiatry students. Psychiatry researchers at the Medical College of Georgia evaluated VPF for use in teaching and assessing history taking skills for psychiatry in teractions (Shah et al., 2012) The researchers created Cynthia Young, a 21 year old female patient with chief complaint of insomnia and fatigue. The researchers ran two studies to evaluate the perceived educational benefit of this patient for psychiat ry education. The psychiatry researchers followed the same process outlined for creating the pharmacy patient. Participants included 71 first and second year psychiatry students
71 and 67 third year students. After the interaction, participants completed a s ubjective Because of the varying levels of experience of the participants, this study illustrates the uses and limitations of VPF interactions. Specifically, the researchers compared the self reported eff icacy for first and second year students to the efficacy for the third year students. Osteopathy Patient with Neurological Disorder perceived educational benefit for osteopathic education. Researchers at the Philadelphia Colleg e of Osteopathic Medicine created a virtual patient for history taking before a neurological examination (Surkunalingam et al., 2009) The neurological patient, Nelson Sanjaya, is a 20 year old male who is complaining of a suspicious headache, general malaise, fever, and nuchal rigidity. 46 second year medical students at the Philadelphia College o f Osteopathic Medicine used the VPF browser interface to interview Mr. Nelson. After completing the interview, students completed a survey regarding the self reported educational value of the application. Psychiatry Patient with Bipolar Disorder This stu dy compared participant performance between VPF browser based interactions and spoken life size interactions using the Interpersonal Simulator. The Bipolar Disorder character was created by a different type of domain expert, a peer support specialist at th e Medical College of Georgia (Foster et al., 2010b) The peer support specialist is a former patient with bipolar disorder. She wished to convey herself as a virtual patie nt in order to train psychiatry students to help their patients. To this end, she created herself and her husband as virtual humans. The patient character is a woman who presents with psychotic bipolar disorder and later that night develops a crisis. In pa rt 1 of the bipolar
72 scenario, the participant interacts with the patient to assess her current state. In part 2, the participant interacts with the husband after the patient has a depressive episode and attempts to commit suicide. 25 third and fourth year medical students interacted with the scenario -15 of the participants interacted using the instant message browser based character and 10 interacted with the life sized character by speaking. After the interactions, domain experts evaluated the completen ess of the content elicited during the interviews. 3. 3.2.2 Results Details of the VHs described in the previous section are presented in Table 3 2 Table 3 2 Conversational models created since the completion of the study. Scenario Name Users Stimuli Responses Modeling Time Melanoma Patient Hank Lowry Surgery 621 189 21 h ou rs Depression Patient Cynth ia Young Psychiatry 1314 345 15 h ou rs Bipolar Disorder Patient Denise Psychiatry 1605 220 11 h ou rs Meningitis Patient Nelson Sanjaya Osteopathy 777 228 25 h ou rs Hank Lowry, Surgical History Patient with Melanoma After conversing with Hank Lowry, participants filled out a survey on the educational efficacy of the experience (all questions were on a 10 point scale, and 7 10 was considered positive), the results are reported in Table 3 3 Table 3 3 Percentage of participants reporting 7 10 (good excellent) on Surgical History Patient with Melanoma post interaction survey Question First Year (N=51) How much did you enjoy this interaction? 65% Do you feel this interaction was a valuable learning experience? 73% How easy was it to use Virtual People Factory? 77%
73 Cynthia Young, Psychiatry Patient with Depression After conversing with Cynthia Young, pa rticipants filled out a survey on the educational efficacy of the experience, the results are reported in Table 3 4 Table 3 4 Percentage of participants reporting 4 5 (good excellent) on Psychiatry Patient with Depression post interaction survey Question First and Second Year (N=71) Third Year (N=67) Helped learn to formulate questions about depression symptoms 57% 31% Valuable educational tool 66% 24% Easy to use 71% 66% Open ended feedback suggested that this virtual patient experience is particularly useful in the first two years of medical school to decrease anxiety and offer practice before interviewing real patients during the clerkship years. Third year students reported lower usefulness, and reported that the system wou ld have been more useful in their first two years. Nelson Sanjaya, Neurological Virtual Patient with Meningitis After conversing with Nelson Sanjaya, participants filled out a survey on the educational efficacy of the experience, the results are reported in Table 3 5 Table 3 5 Percentage of participants reporting 4 5 (good excellent) on Neurological Patient with Meningitis post inter action s urvey Question Second Year (N=46) Beneficial in preparation for live patient encounters 79% Valuable educational tool 71% User friendly 71% Would like to have VPF virtual patients available for future training 92% Denise, Psychiatry Patient with Bipolar Disorder This study compared browser based interactions to spoken life size interactions. The two systems were compared based on the content elicited during the interaction as seen in Table 3 6
74 Participants interacting with the virtual patient using a browser were more likely to ask about suicide, grandiosity, and elevated mood; while participants interacting with the virtual patient in the life size interact ion were more likely to ask about distractibility and the duration of the illness. From this study, we see that 1) students can successfully perform an assessment in either medium and 2) the differences in the medium may cause the users to focus on differe nt topics in the interaction. Table 3 6 Comparison of content elicited in browser versus Interpersonal Simulator interviews with Psychiatry Patient having Bipolar Disorder Browser (N=15) Interpersonal Simulator (N=10) Suicidal Ideation 100% 80% Grandiosity 73% 40% Elevated mood 93% 80% Distractibility 60% 80% Illness duration 60% 100% 3. 3.2.3 Discussion These studies indicate that VPF may be a viable and well received method for augmenting current interview training curricula The domain experts who ran these studies remarked that VPF provides an alternative method for practicing patient interviews in a resource time and cost effective manner. In a prior study of US and Canadian medical schools 74% took 3 months to more than 2 years full time to develop a single virtual patient scenario compare this to the 11 25 hours of Table 3 2 to develop a virtual patient using VPF (Huang et al., 2007) The medical educators further stated that VPF allowed medical students to learn correct history taking techniques prior to interacting with patients in the clinic. Students who used these scenarios have remarked and first and second year students,
75 interviewing patients can be very nerve w racking and this may be a great bridge to becoming relaxed in patient interviewing virtual patients are particularly useful du ring the first two years, but have decreased utility as the students reach the third year and beyond. This may be because students begin frequent interactions with both standardized patients and real patients during their third year. This finding is relate d to the types of scenarios involved in these studies; given more advanced topics we may find a different trend. The majority of students found their interactions to be educationally valuable if placed in an appropriate stage of the curriculum. Further, th e educators were able to create educational virtual patients themselves in collaboration with students, and with minimal assistance from computer scientists. Medical educators have indicated these properties are essential in order to see widespread adoptio n of virtual patients in the healthcare field. 3. 3.3 Evaluation 3: Usability for healthcare education The final evaluation assesses domain expert feedback on the usability and acceptability of VPF and HDCM. Usability and acceptability are measured using th e following metrics: 1 Usability: 2 Acceptability: the perceived usefulness for education 3. 3.3.1 Methods Domain experts (N=11) from the above research studies were issued a digital survey on usability self reported usability of VPF for healthcare education. It also assessed their self reported view on how useful VPF is in preparing students for patient interviews. The
76 usability and acce ptance survey was Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology survey (Davis, 1989) Cronbach alpha of .98 for perceived usefulness and .94 for perceived ease of us e. This questionnaire has 12 questions; responses were rated from 1 unlikely, to 7 likely. Questions were modified to refer specifically to VPF and medical education, for the system Virtual People Factory useful in student education added at the end of the survey to also assess if the educators found VPF to be educationally valuable, if they plan to continue using VPF in their courses, and if VPF coul d save time in teaching particular topics. 3. 3.3. 2 Results Seven out of the eleven domain experts responded to the survey. Survey results indicate that, on average, domain experts felt the system was easy to use and would be useful for healthcare education as seen in Table 3 7 Additionally, 3 out of 7 domain experts responded that they will continue to use VPF in their courses (4 responded that this option was not applicable as they were not teaching courses). Of the 3 domain experts who will continue to use VPF in teaching their courses, 2 out of 3 will use VPF to replace a portion of their lecture, and they note that this will save them on average 90 minutes of lecture time. Further, to introduce a patient case, experts normally take 8 10 hours to create the paper and pencil case. If we consider that a VPF virtual patient takes 15 hours, those 5 extra hours allow the expert to provide an enhanced learning experience and the ability to distribute learning to students prior to coming to class. Prior distribution allows
77 students to be ready to discuss the case and be tested d uring the class. These advantages indicate that not only will the VPF interactions provide enhanced learning experiences they can also be used to review patient cases that will then be discussed in the lecture, thus saving the time used to introduce the patient case and get students to the point where they can discuss the case. Table 3 7 Results of the domain expert survey on Educational Value, Usefulness, and Ease of Use of Virtual People Factory R esponses were rated from 1 unlikely, to 7 likely N Mean Std. Deviation Educational Value 7 6 0.0 Usefulness Work more quickly 6 5.2 1.0 Job performance 7 5.9 0.4 Increase productivity 7 6.0 0.6 Effectiveness 7 6.0 0.8 Make job easier 4 5.5 0.6 Useful 7 6.4 0.5 Ease of Use Easy to Learn 7 6.1 0.9 Controllable 7 5.6 1.0 Clear & Understandable 7 5.6 0.5 Flexible 7 5.6 0.5 Easy to Become Skillful 7 6.1 1.2 Easy to Use 7 5.9 0.9 In discussing VPF, healthcare experts made the following negative remarks regarding time and accuracy : development process is time consuming patient] is time consuming the raining for an answer in all th e possible ways it can be asked it sometimes still doesn t respond properly t would be nice to be able to save questions as a template and plug in new answers based on the scenario.
78 In discussing VPF, healthcare experts made the following negative remarks regarding the web browser interactions : It can be d ifficult to get students to treat a VPF interaction as an actual clinical interaction In discussing VPF, healthcare experts made the following positive remarks regarding ease of use: [VPF is] e asy to use once you learn the system involved in script development and analysis. from scratch and then give them a history from their favorite food to their feelings of In discussing VPF, healthcare experts made the following positive remarks regarding educational value: [VPF interactions are] an excellent way to review criteria for depression and bipolar The experie ntial learning aspect of this program empowers students and gives them the skills to continue that learning in other aspects of their education. have the opportunity to review their own transcripts which is very useful and it makes people awar e of areas of possible
79 3. 3.3. 3 Discussion The results of evaluation 3 indicat e that medical educators perceive VPF to be highly useful and beneficial to their students. The results further suggest that the majority of the healthcare educators will continue to use VPF in their courses. In courses, VPF will be used in conjunction wit h both lectures and standardized patient interactions. When combined with lectures, VPF interactions may be used to replace the initial discussion of a patient case, and then used to prompt additional discussion after the interactions. Some experts also st ate that they would recommend VPF for adoption. Some negative remarks made by the experts include the process still being time consuming, that the VH patients still give inaccurate responses, and that the browser based interactions are limited. In C hapter 4, we discuss Conversational Knowledge Reuse, which further reduces the time required and improv es the accuracy of interactions. The browser based interactions are limited by design and it is understandable that students would not treat these interactions the same as a full clinical interaction. The intention is for VPF to be used for creating conversational models and for simple practice, and then full training interactions will be conducted using more immersive interfaces, such as the Interpersonal Simul ator (Johnsen, 2008) While these limitations are present, the time and accuracy issues will continue to be reduced by future work, and the interactivity can be im proved by using more immersive clients. One limitation of this evaluation is that the educators involved with these studies are collaborators of the researchers and have put time into learning and using the system. There is an inherent desirability bias in
80 report survey and the sunk cost of learning the system may cause additional positive feedback influences. These self reported results are an initial check that educators are not only using VPF in healthcare education, but they also feel it is providing educational benefit. We further find that the paradigm of using HDCM to create educational virtual patients is acceptable to healthcare educators. This is an important finding as it is unusual to ask students to be a part of the creation of educational materials. Generally, educators create materials on their own and then use those materials in class. These ini tial results indicate that involving students as active participants in the creation process may be a successful technique.
81 CHAPTER 4 CONVERSATIONAL KNOWL EDGE REUSE : VIRTUAL HUMAN TEMP LATES AND DYNAMIC KNOWLEDGE SH ARING This chapter presents the analysi s, design implementation, and evaluation of two new Conversational Knowledge Reuse method s Virtual Human Templates and Dynamic Knowledge Sharing ( research questions 1 3 ) These two methods provide new sources of conversational knowledge and help further reduce the time taken for conversational modeling of virtual humans. We describe a study which use d Virtual Human Templates and Dynamic Knowledge Sharing along with Human centered Distributed Conversational Modeling to generate seven new virtual human patients within the context of a healthcare course. Students in the course (N=32) author ed the virtual human patients themselves Integrating the creation of virtual human patients into a course was previously infeasible due to time requirements and was made possible by the addition of Virtual Human Templates and Dynamic Knowledge Sharing to the conversational modeling process This study provides evidence for the usability and efficiency of Conversational Knowledge Reuse in the creation of a diverse set of virtual human s A publication on the evaluation of the s e method s and imple mentation s was submitted to the Journal of Autonomous Agents and Multi Agent Systems and is currently in review ( impact factor = 2.1 ) (R ossen et al., 2012) Personal Contributions I conceptualized the Conversational Knowledge Reuse concept and the Virtual Human Templates and Dynamic Knowledge Sharing method s implemented the Virtual Human Templates system extended Virtual People Factory to use Dynamic Knowledge Sharing and designed ran, and analyzed the study.
82 Collaborators Shivashankar Halan was involved in the design of the Virtual Human Templates system and assembled the template used in the study. He was also involved in the design and implementation of the study. Dr. Michael Crary provided access to study participants ( dysphagia students ) and provided valuable feedback in the design and evaluation of the study Relevance to thesis This chapter describes two new method s and their integration into the Virtual People Factory system These additions are evaluated in a user study which demonstrates a reduction in the time to model virtual human conversation s This evaluation also shows that these methods make the process feasible for domain novices to rapidly generate new virtual human conversational agents within the context of a healthcare course 4. 1 Overview Applications of virtual human (VH) interpersonal skills training require a diverse set of VH scenar ios. I nterviewing skills cannot be trained by interviewing the same person over and over ; learning these skills require s diverse experiences covering many issues (Huang et al., 2007) Providing diverse experiences is perhaps the most compelling reason to develop VH s for interpersonal skills education (Johnsen, 2008) VHs can be modeled to provide a wide variety of human aesthetic characteristics in combination with a variety of issues to prov ide both contextually and ethnically diverse experiences (Ro ssen et al., 2008; Rossen and Lok, 2012) VHs can also simulate scenarios that students may otherwise never encounter in training or that take place in a setting inappropriate for training (Kotranza et al., 2009a) However, using previous technology, acquiring the knowledge necessary for each VH s imulation has been costly in terms of both time and money (Dickerson et al., 2005; Glass et al., 2005;
83 Huang et al., 2007; Kenny et al., 2007; Kenny et al., 2008; Villaume et al., 2006) To address the limitations of conversational modeling, we propose a novel method of generating new convers ational models from existing conversational data, Conversational Knowledge Reuse. The Conversational Knowledge Reuse method allows VH creators to leverage previous and ongoing efforts towards creating similar VHs. The concept is that small portions of conv ersational data, such as sentences in a transcript or question response pairs from a conversational model, can be processed and reused to generate new unique conversational models For example, we can create the conversational model for a virtual patient w ith a stomach ulcer, and then reuse portions of that conversational model to create a virtual patient with appendicitis. The Conversational Knowledge Reuse method will enable VH authors to create a wider variety of VHs, and thereby provide more diverse exp eriences using VHs. Chapter 3 described work on c onversational modeling using Human centered Distributed Conversational Modeling (HDCM). U sing Conversational Knowledge Reuse we extend the conversational modeling process defined in HDCM to include new meth ods of reusing conversational data for the creation of VH conversational models. Employing Conversational Knowledge Reuse results in robust conversational models in significantly shorter time. This rapidity opens up new application areas such as producing diverse sets of VHs In this chapter we explore two implementations of Conversational Knowledge Reuse 1) Virtual Human Templates ( VHT ) and 2) Dynamic Knowledge Sharing ( DKS ) These two applications of Conversational Knowledge Reuse share the underlying
84 method of reusing conversational data acquired for the creation of one conversational simulation in the creation of a different conversational simulation The process for VHT extracts the knowledge from a previously created conversational model and stores it in a template To create a template, a knowledge engineer processes conversation specific knowledge in to knowledge that is applicable to a whole domain of conversations such as patient interviews. This generalized information is then customized by a VH author to generate a new conversational model (details in section 4. 2). In contrast to the preprocessed knowledge from VHT, DKS knowledge is re used dynamically and shared among a group of similar VHs during creation Because the VHs are similar t he VH co nversational models for each scenario should all be able to respond correctly to the stimuli for any of the other scenarios. This stimulus knowledge is shared by simulating interactions from one VH VHs in the group, and thereby discovering what questions the other VHs cannot yet respond to correctly (details in section 4. 3) Using the methods of Conversational Knowledge Reuse the authors of VH conversational models do no t need to start over every time t hey create a new VH. Further, the process of creating VH templates develops a warehouse of knowledge to draw from. Using this warehouse, each successive VH conversational model is more robust and faster to create than the previous generation of VHs. 4. 2 Vi rtual Human Templates This section describes the creation of a virtual human template, the generation of a new conversational model using a template, and the process of refining that model after it is generated. A knowledge engineer processes t he original conversational model to turn it into a virtual human template (section 4.2.1) A virtual human template is a
85 generalized corpus of knowledge. VHT uses the template to construct a Virtual Patient Generator interface. Using the Virtual Patient Generator, a V H author fills in the parameters of the template, and the system generates a conversational model customized to their particular scenario. The generated conversational model is compatible with Virtual People Factory, and is now ready for the HDCM process. An overview of the process for creating and using a virtual human template is shown in Figure 4 1 Figure 4 1 The construction and use of a virtual human template to construct conversational models in the Virtual Human Templates system 4. 2.1 Virtual Human Template Creation A virtual human template is created based on one or several existing conversational models. The corp ora of the original conversational models are reviewed and generalized by a knowledge engineer. A knowledge engineer generates a virtual human template by reviewing each stimulus and processing the generalizable stimuli and responses from the origi nal conversational model(s). To process a single stimulus and response the k nowledge e ngineer first examines the set of similar stimuli and responses. The overlapping information that is specific to the original conversational ar ked as a variable. This parameterized stimulus and response are then inserted into the template.
86 A small example template presenting several stimulus response pairs is shown Table 4 1 Within the template, the scenario specific information from the original responses turned into [var] s for customization The resulting template facilit ates the generation of new conversational models, as described in section 4.2.2. Table 4 1 E xample virtual human patient template Original Stimulus Original Response Template Stimulus Template Response Are you Vic Johnson? I am Vic Johnson Are you [var]? I am [var] What is your blood pressure? My blood pressure is 110/80 What is your blood pressure? My blood pressure is [var] Any family history of diseases? Diabetes runs in my family Any family history of diseases? [var] runs in my family 4. 2.2 Conversational Model Generation The VH template created by the k nowledge e ngineer is f F or the template used in the current study X uthors of VH patients use the virtual patient gene rator to customize the template and generate new VH conversational model s A screenshot of the interface for the virtual patient generator can be seen in Figure 4 2 As shown in the figure, VH authors fill in the [var] information for each question, and the generator uses th at information to create the customized field of the example ( Figure 4 2 ) Diabetes the 23 paired stimuli that are linked to this response. As an alternative to filling in the [var], the VH author may choose to completely replace the response by pressing
87 other and g randmother both had diabe Figure 4 2 A screenshot of the Virtual Patient Generator used in the study The Virtual Patient Generator interface was programmatically constructed by the Virtual Human Templates system based on the virtual patient temp late. T he process of using the virtual patient generator currently consists of five steps, the last of which transfers the new conversational model to Virtual People Factory. Within Virtual People Factory, the conversational model is refined using the HDCM process described in Chapter 3 The following are the steps of virtual patient generation:
88 1. Overview: Provides a brief description of the upcoming process 2. Select VH: Initiate s the creation of a new VH or allows the selection of a previously worked on VH. 3. Select Image: Allows the selection of an image for the VH. The selection in cludes a range of faces as shown in Figur e 4 3 These faces were created using 4. Customize Template: In this step the author fills in the conversation specific information. a. Demographics: This is the first template based page It includes template b. Medical History: Includes entries for hereditary diseases, existing medical problems, previous surgeries, accidents and allergies. c. Social History: Includes entries for marital status and socially based risk factors. Risk factors include alcohol consumption, tobacco consumption, sexual history, and occupation. d. Chief Complaint: Includes entries regarding the chief complaint, history of the complaint, and symptoms associated with the comp laint. 5. Complete: This is the final step in the patient generation process. The user confirms their choices and the system generates a conversational mod el from the customized template. The system then transfers that model to Virtual People Factory. Figur e 4 3 The f aces available for selection in the Virtual Patient Generator
89 4. 2.3 Conversational Model Refinement After the VHT system transfers the conversational model to Virtual People Factory, the author is redirected to Virtual People Factory to continue developing the conversational model using the HDCM process. The template has seed ed the process of HDCM which will speed up the conversational modeling but VHT does not supersede HDCM Templates are not used in isolation because they cannot completely cover the stimuli response space of a particular conversation. We have previously discussed in Chapter 3 that we cann ot predict the complete stimuli response space of a conversation. VH templates cover only the overlapping information of an interaction domain which in t his case is patient interviewing The template covers common patient interaction stimuli such as brings you to the office today are common in many patient interactions, and therefore cover the first layer of stimuli th at are likely to be encountered. However, the templ ate may not cover the stimuli and responses to follow medications are you taking t cover that stimulus because it is specific to the current scenario. The Virtual Human Templates system is capable of covering this additional question, but will only do so if it was covered in the source conversational model For these reasons, creating a VH conversational model using a virtual human template still requires HDCM to prepare the model for high quality VH interactions 4. 3 Dynamic Knowledge Sharing The second method of Conversational Knowledge Reuse explored is DKS In contrast to VHT, which statically leverages conversational model overlap, DKS
90 dynamically leverages that same overlap VHT is static because it requires a knowledge e ngineer to process existing knowledge and put it into a statically stored format. DKS i s dynamic because it reuses knowledge immediately as it appears in the system. The advantage of VHT is that the knowledge has been validated and converted into a format that can be used rapidly, but there is a delay before the knowledge is available. Using the DKS method, knowledge can be made available immediately, but must be processed by the VH author in the same way HDCM knowledge is processed. DKS is used when a group of similar VH conversational models are being developed at the same time. The knowled ge from these scenarios may be shared among the conversational models to improve the total knowledge available for modeling each conversation The way DKS shares this kno wledge is by taking each conversation conducted with one of the VHs and simulating tha t conversation with all of the other VHs. DKS allows stimuli from interactions with one VH to be tested against the current knowledge of the other VHs, and any new stimuli are added into the Suggestions System ( described in Chapter 3 ) An overview of the DKS process can be seen in Figure 4 4 In the DKS process, end users ( who are generally novices) conduct an interact ion with one of the VHs using its own conversat ional model. That same interaction is then simulated with each of the other conversational models. Simulation consists of testing e ach stimulus from the interaction against each of the other conversational models as though the interaction was occurring wi th each of the VHs individually Whenever a VH cannot respond to a stimulus, that stimulus is fed into that suggestions system, the same way they are processed in HDCM.
91 Figure 4 4 Overview of the Dynamic Knowledge Sharing pr ocess. 1: Novice users interact with the VH using its conversational model ; 2: the system simulates and unknown stimu ; 3: the unknown stimuli are validated by VH authors paired with a response and placed in the conversational model As can be anticipated, because these stimuli were acquired during conversations with a different VH, the relevance of the stimuli acquired using DKS is o f ten lower than the relevance of conversations intended for that specific VH. However, the net effect is an increased usage of knowledge acquired from every novice interaction, which results in more total knowledge for all of the conversational models. The relevance of stimuli acquired from these resources is analyzed in section 4. 4.4 and discussed in section 4. 4.5
92 4. 4 Study 4. 4 .1 Methods The Conversational Knowledge Reuse methods were evaluated within a course. The authors of the VHs were the students of that course To the best of our knowledge, h aving students generate natural language conversational models has not been tried before and was previously infeasible due to the time required to create VH conversational models Having students create multiple choice virtual p atients was previously explored and was found to be overly challenging for the students (Vi llaume et al., 2006) The experiment was conducted within a course called Dysphagia Management Dysphagia is the medical term for the s ymptom of difficulty swallowing and can be observed in patients suffering from a variety of medical conditions. The course was taught over a period of four months (one semester) during the spring of 2011. Students in the Dysphagia Management course are preparing to be clinical practitioners in the field of Speech Language Pathology After these students graduate, pati ent interviews will be critical to the quality of their clinical practice. Traditionally, the only way these students learn patient interviewing skills is by practicing with real patients under the supervision of a professor. Since a professor is required for supervising practice interview experiences, students are provided with a limited number of practice opportunities. Th e Dysphagia Management course prepares students on the theory of patient interviewing for dysphagia related conditions, but does not pr ovide them with real patient interactions. As such, the course instructor felt the students would receive benefit from creating virtual patients and using those patients to prac tice their interviewing skills.
93 To implement the proposed Conversational Knowle dge Reuse methodology within the course, students work ed in small groups to create and revise their own virtual patient in scheduled exercises throughout the semester. During creation of the conversational models, the students were not required to use a pa rticular conversational modeling method, and were instructed in how to use all four methods: Manual, HDCM, VHT, and DKS Manual Manual additions are the direct method of entering information into a conversational mo del. The author uses the Virtual Peopl e Factory Edit Scripts interface to manually add new stimuli and connect them to new responses. HDCM. HDCM knowledge comes from either interaction between a public user and a VH, or between a VH author and their own VH. Stimuli from public interactions are collected automatically du ring interactions with public interviewers (non author interviewers) when the VH does not respond, or when the user manually identifies that the VH has responded incorrectly Stimuli from author interactions are collected simi larly to public interactions, except the interviewers are the VH authors. Authors interview author manually identifies that the VH has responded incorrectly (additional det ails in Chapter 3). Knowledge from this method is pushed into the suggestions system where it is processed to become part of the conversational model. Virtual Human Templates. Stimuli from this source come from the virtual patient generator during the c ustomization of the template for the initial seeding of the conversational model.
94 Dynamic Knowledge Sharing Stimuli from this source come from the DKS system and are similar to a public true negative but are collected from interaction s with other conver sational VHs. Questions the conversational model does not contain are pushed into the suggestions system where they are processed to become part of the conversational model. For the purposes of evaluating Conversational Knowledge Reuse, the data analysis focuses on 1) evaluating which modeling methods the students chose to use, 2) the accuracy of the resulting conversations and how that relates to the methods used, and 3) the time that went into each method and which strategies were most effective. Using t his information, we establish ed the relative effectiveness of Manual HDCM, VHT and DKS 4. 4.2 Population The course consisted of 32 health profession students working towa rds a graduate degree in Speech Language Pathology. All 32 of the students in the c ourse chose to participate in the study. The class was predominantly female (87.5%). Participants were first or second year graduate students (72% first year). 4. 4.3 Procedure In the first week of class in spring 2011, students received lectures on the basic causes of dysphagia. Students were then introduced to Virtual People Factory by the experimenters during a class room lecture. Before starting the study, participants received and signed an IRB consent form. The students wer e told that participation in the study was optional, and that an alternative assignment would be provided if they chose not to participate. All students in the course chose to participate. The study proceeded as shown in Figure 4 5
95 Figure 4 5 Conversational Knowledge Reuse evaluation s tudy p rocedure After signing the consent form, participants received a one hour introductory training on V irtual People Factory. As part of the training, the experimenters had students practice interviewing Vic Johnson, a patient created in a prior pharmacy class, described in Chapter 3. They were then issued individual logins for Virtual People Factory and gi ven their group assignments. The participants were randomly assigned into seven groups of four or five. Each group created one VH patient. Each group was provided with a profile for a patient to produce The profile (created by the course instructor) included a brief background of the patient containing gender and family history Participants made up the name, culture, and personality of the patient. The patient scenarios were all similar in that t he patient presented with a swallowing disorder. However, one of the seven was conversationally dissimilar because the patient was a difference, this conversation is used as a co ntrasting example in the data analysis. The seven scenarios are shown in Table 4 2 Participants constructed their VH outside of class time, and the amount of time spent on their VH was strictly voluntary. Virtual People Factory tracked the amount of
96 time each participant spent creating their VH. Participants were aware that this time was being recorded. Table 4 2 Virtua l Humans created as part of the experim ental study Scenario in which the patient is the baby and the VH is the mother. Participants used a v irtual p atient t emplate to create their initial VH conversational model. To refine the content the VH was able to discuss t hey used the HDCM with DKS process as well as manually entering new stimuli and responses Participants refined their conversational model using three rounds of interleaved VH development and patient interview interactions (as shown in Figure 4 5 ) During a round, each participant was asked to interact individually with their virtual patient, and then in teract individually with two other team s virtual patients. By the end of the thir d round, every participant was asked to ix interactions), and interact with their own patient three times. A visual representation of the study procedure is shown in Name of virtual patient Number of Students Diagnosis Culture / Sex Personality Marty Graw 4 Esophageal Stricture Haitian / Male Anxious and amiable Vinny Devito 5 Brainstem Stroke Italian American / Male Loves food and spending time with family Jackie Dauer 5 Supraglottic Laryngectomy African American / Female 62 year old widow who is friendly Kahlua Lopez 5 Left Hemisphere Stroke Hawaiian / Female L ower middle class (English is second language) Johnny A Seed 4 Diverticulum Caucasian / Male Elderly male, Lively, happy, optimistic John Smith 4 Head and Neck Cancer with Radiation None / Male None Anne Animus 5 Baby has GERD Caucasian / Female Single Mother
97 Figure 4 5 At the last week of class, the instructor conducted a review of the created virtual patients in class as seen in Figure 4 6 Figure 4 6 Student groups presenting VHs they created to the class and instructor 4. 4.4 Data Analysis We performed a n analysis of the sources of stimul i (Manual, HDCM, VHT, and DKS ) contained in each conversational model The analysis establish ed the relative usage, quality and efficiency of each conversational modeling method. Usage (model) : T he percentage of each conversational model that originated from each method Usage (responses) : T he percentage of each transcript that originated from each method Quality : T he percentage of accurate responses during interaction s originating from each method Efficiency (model) : T he increase in quality per hour spent on each conversational model Effici ency (method) : T he average number of stimuli added to a conversational model for each hour spent on a method.
98 To establish the usage, quality, and efficiency for each of the methods, we computed the following metrics for each method: percentage of each con versational model, percentage used in interactions, accuracy of responses, and time spent. These metrics were also calculated for each conversational model as a whole t o establish the effect the methods had on the overall quality of the conversational mode l The remainder of this section describes each of the metrics Percentage of the conversational model. We calculate d t he percentage of the conversational model s that originated from each of the methods. The percentage of stimuli sources that made up each conversational model shows the usage of each method to build the conversation al model but does not show if these stimuli were used during interactions. Percentage used in interactions. During an inter action between a human and a VH, each utterance from the human may cause a response from the VH. Utterances are either questions or statements typed by the human interviewer. We calculated the percentage of responses that originated from each method The percentage of responses from each method during intera ctions shows what percentage of the actual conversations originated from each method. Usage was analyzed using the final (3 rd ) round of transcripts because that was when the conversational models were most developed. Only transcripts of interactions with p ublic users were analyzed for the usage of each source. By comparing the usage of each source with the quality of the interactions, we can evaluate how the method sources chosen by authors affected the performance of the conversational model in practice.
99 A ccuracy of responses. While the distribution of response sources indicates the usage of each conversational source during interactions, it does not indicate the quality of those responses We define q uality as the percentage of accurate responses from eac h method The quality portion of the analysis was determined by the accuracy of responses during interactions Accuracies were also analyzed using the final (3 rd ) round of public interaction transcripts. To determine the accuracy, the transcripts were manually reviewed for the accuracy of the given responses. As in the analysis of HDCM (Chapter 3), the responses were marked as accurate if there was a s emantic link between the utterance from the user and the response from the VH (Leuski et al., 2006) symptoms and medical history. Utterances that were problematic due to incompletely or incorrectly typed statements were removed from t he transcripts prior to analysis Time spent. Time was calculated in hours spent on each Virtual People Factory page. Pages were grouped by conversational modeling method. Time spent idle on the homepage and time in which participants spent more than one and a half hours on a single page were considered periods of inactivity, and so were discounted from the time calculations. Overlapping time, in which multiple pages were open, were recorded as only the first page opened plus non overlapping times. The time spent on both HDCM and DKS together is the time authors spent using the Suggestions System. Because any time spent on the Suggestions Syst em is spent processing suggestions from both HDCM and DKS we computed the time based on the ratio of the number of suggestions processed for HDCM or DKS over the total number of suggestions processed
100 4. 4.5 Results We first present an overview of the resu lting conversational models the time spent on those models, and the resulting accuracy of responses during interactions ( Table 4 3 ) There were seven conversational models produced To determine the accuracy, the transcripts from the final (3 rd ) round of interactions were manually reviewed for the accuracy of the given r esponses. The 3 rd round of transcripts consisted of 51 transcripts with 6 to 9 interactions per VH. In those 51 transcripts there were 2365 utterances for an average of 46.37 utterances per transcript with s.d. = 25.14. The accuracy resulting from this ana lysis was used to sort the tables in this section Based on the accuracy results shown in Table 4 3 the VHs did not respond or responded incorrectly to between 10.28 and 16.44 utterances per interaction. The accuracy of the Anne Animus conversational model is lower than any of the others. The data analysis from the Anne Animus conversational model is included in the analyses to provide contrast to the other six models. The authors of the model were unable to use the Conversational Knowledge Reuse methods due to the dissimilarity of the scenario. Table 4 3 The time required to create each virtual human, and the size of each virtual human conversational model ordered by the accuracy of the model s Scenario in which the patient is the baby and the VH is the mother Anne Animus is s hown for illustration purpos es and not included in mean or s.d. Name of virtual patient Stimuli Responses Time (hours) Accuracy Marty Graw 1238 362 17.49 77.83% Vinny Devito 1358 465 22.84 72.84% Jackie Dauer 1576 557 14.65 70.82% Kahlua Lopez 837 302 15.06 69.07% Johnny A Seed 1253 343 14.58 68.56% John Smith 897 191 11.71 64.26% Anne Animus 635 277 15.39 51.07% Mean 1193.17 370.00 16.06 70.56% s.d 280.67 127.63 3.80 4.46%
101 Note that t he largest conversational model Jackie Dauer did not provide the most accurate responses Nor did Vinny Devito, the conversational model in which the authors spent the most time. In fact, there is no t a significant relationship between the accuracy of the conversational model and the total time spent on the model (Pears on Correl ation r 2 = 0. 388 p = 0 .186) ; the breakdown of time spent per method and the relationship to accuracy is presented in section 126.96.36.199 The Marty Graw conversational model was the most accurate, and its accuracy is more than one standard deviation higher than the mean. In the following sections, we report ed an analysis of the usage quality and efficiency of stimuli sources and evaluate d how they relate to the overall accuracy of the conversational models Throughout the results sections, we identified relationships but did not attempt to clarify the implications of those patterns We explain the implications of the patterns and lessons learned in section 4.4.6 Discussion 4. 4.4.1 Analysis of method usage Percentage of conversational model fr om each method The largest source of new stimuli was from the DKS method followed by stimuli from the VHT method ( Figure 4 7 ) Note that there is a large variance in the usage of each source (as indicated by the s.d. error bars in Figure 4 7 ), particularly for the Manual method. T hese numbers were calculated from the averages on a per model basis which means each conversational model is weighted evenly, rega rdless of the size of the model The distribution shown in Figure 4 7 was not uniform throughout the created conversational models. The distribution on a per model basis ( Table 4 4 ) shows that DKS was the largest source of conversational knowledge for the Marty Graw, Jackie Dauer, Kahlua Lopez, and Johnny A Seed models, but not for the other three. T he
102 Vinny Devito conversational model include s the large st percentage of stim uli from the manual method compared to the other VHs ( Table 4 4 ) This shows that the Vinny Devito authors chose to use the Manual method more than the other groups The John Smith model has a large percentage of stimuli from VHT, 10.04% more than the next highest. The maximum number of stimuli that can come from VHT is fixed. Since the thod i ndicates that after using the v irtual p atient g enerator did not add as much to the conversational model as the other groups Figure 4 7 Mean percentage of stimuli in the conversational models from each method DKS s.d. = 9.21%, VHT s.d. = 5.72%, HDCM s. d. = 6.56%, Manual s.d. = 8.46% represented by the e rror bars In contrast to the other six, t he Anne Animus conversational model has a significantly larger percenta ge of stimuli from the HDCM method and almost no stimuli from the DKS method ( Table 4 4 ) This indicate s that the authors of the Anne Animus conversational model could not make use of the DKS method. H owever, we note that a 11.84% 26.06% 27.16% 34.94% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% Manual HDCM VHT DKS
103 large percentage of the conversational model still originated from VHT indicating that they tried to use Conversational Knowledge Reuse Table 4 4 The percentage of st imuli from each source in each VH conversational model Shown for illustration purposes and not included in mean or s.d. Name of virtual patient Total Stimuli Manual HDCM VHT DKS Marty Graw 1238 12.44% 28.27% 24.72% 34.57% Vinny Devito 1358 25.26% 29.75% 24.23% 20.77% Jackie Dauer 157 6 7.92% 21.72% 22.74% 47.63% Kahlua Lopez 837 2.15% 35.01% 28.20% 34.65% Johnny A Seed 1253 17.48% 16.20% 24.82% 41.50% John Smith 897 5.80% 25.42% 38.24% 30.55% Anne Animus 635 3.3 1% 61.89% 32.44% 2.36% Mean 1193.67 11.84% 26.06% 27.16% 34.94% s.d. 281.49 8.46% 6.56% 5.72% 9.21% Percentage of suggestions accepted from HDCM vs. DKS We compare the usage of HDCM and DKS directly because both HDCM and DKS operate through the Suggestions System. However, the source of the knowledge from DKS may be less relevant than from HDCM and is expected to have a lower acceptance rate. For five of the six conversational models, HDCM suggestions were accepted approximat ely twice as often as DKS suggestions (as seen in Table 4 5 ). The notable exception to this observation is the Jackie Dauer conversational model. The Jackie Dauer con versational model has a significantly higher percentage of accepted suggestions from both the HDCM and DKS sources ( Table 4 5 ); this indicates that the Jackie Dauer au thors were unusually likely to accept suggestions into the model. This behavior may have caused the Jackie Dauer conversational model to be the largest.
104 Table 4 5 The percentage of stimuli from HDCM and DKS that were accepted into each VH conversational model. Shown for illustration purposes and not included in mean or s.d. Name of virtual patient HDCM Suggestions Processed HDCM Suggestions Accepted DKS Suggestions Processed DKS Accepted Marty Graw 429 76.92% 1150 36.78% Vinny Devito 498 73.49% 812 34.61% Jackie Dauer 348 92.24% 883 85.16% Kahlua Lopez 444 64.41% 1014 28.50% Johnny A Seed 264 74.24% 1001 52.85% John Smith 287 75.61% 1136 24.38% Anne Animus 475 78.53% 1617 0.99% Mean 378.33 76.15% 999.33 43.71% s.d. 93.31 9.04% 134.33 22.53% Percentage of stimuli used from each method during interactions W hile the VHT and DKS stimuli were the most represented in the conversational models (as shown in the previous section), they were not the mo st often used for responding during interactions ( Table 4 6 ) The stimuli originating from the HDCM method were used approximately three times as often as the other methods The stimuli from the other three methods were used a similar amount, with sti muli from the VHT method being used slightly more than the other two There were also 9 .17 % of the utterances which received no response. To put the number of with an average of 46.37 utterances per interaction, there was no r esponse to an average of 4.25 utterances per interaction Note that t he Vinny Devito conversational model has more hits against stimuli from the Manual method than the other models Also note that the Anne Animus conversational model has significantly mor e responses from the HDCM source, zero responses from the DKS source, and the large st percentage of utterances with no response
105 Table 4 6 Percentage of each stimulus source used for responses during interactions of each conversational model Shown for illustration purposes and not included in mean or s.d. Manual HDCM VHT DKS No Response Marty Graw 14.29% 54.21% 12.45% 13.92% 5.13% Vinny Devito 30.34% 38.06% 11.67% 10.77% 9.16% Jackie Dauer 17.31% 40.64% 17.67% 17.67% 6.71% Kahlua Lopez 3.44% 58.42% 17.18% 4.47% 16.49% Johnny A Seed 14.03% 39.37% 21.72% 19.46% 5.43% John Smith 4.96% 40.22% 20.94% 21.76% 12.12% Anne Animus 15.84% 61.99% 9.50% 0.00% 28.51% Mean 14.06% 45.15% 16.94% 14.67% 9.17% s.d. 9.71% 8.79% 4.18% 6.36% 4.44% Required overlap for Dynamic Knowledge Sharing usage. We hav e mentioned that the Anne Animus model is not similar enough to the other conversational models to make use of Dynamic Knowledge Sharing. To formalize the dissimilarity, we an alyzed the overlap of the conversational models. We analyzed the overlap in two ways: 1) using the keyword vector matching algorithm used for simulations 2) using the overlap of keywords Using the keyword vector matching algorithm used for simulations we took the stimuli from each conversational model, and checked if each other model had a response to that stimulus. If there was a response it was marked as an overlapping piece of knowledge Table 4 7 presents the percentage of overlapping stimuli in the conversational models. This data was computed bi directional ly by matching all each stimulus from each model against each of the other models -Marty Graw may have a response to all of Excluding Anne Animus, the conversational models had an average of 85.01 % overlap with s.d. =
106 7.59 %. In contrast, the Anne Animus convers ational model had an average overlap of 63.95 % overlap with s.d. = 9.64 %. Table 4 7 Percentage of overlapping stimuli based on keyword vector analysis in the conversational models Along the left is the source model, along the to p is the model used for matching. Shown for illustration purposes and only included in mean and s.d. specific to Anne Animus Marty Vinny Jackie Kahlua Johnny John Anne Mean s.d. Marty 89.7% 94.8% 82.7% 91.8% 81.9% 58.8% 88.2% 5.7% Vinny 89.5% 93.1% 82.3% 90.9% 82.2% 61.7% 87.6% 5.1% Jackie 76.0% 75.6% 72.6% 83.0% 68.0% 53.1% 75.0% 5.5% Kahlua 85.4% 84.1% 96.1% 91.8% 80.4% 56.6% 87.6% 6.3% Johnny 81.3% 81.5% 90.8% 75.7% 72.5% 50.1% 80.4% 7.0% John 90.1% 90.9% 95.8% 87.1% 93.2% 63.0% 91.4% 3.3% Anne 63.0% 67.1% 87.6% 67.9% 76.2% 62.4% 70.7% 9.6% Mean 84.5% 84.3% 94.1% 80.0% 90.1% 77.0% 57.2% s.d. 5.9% 6.2% 2.2% 5.8% 4.1% 6.4% 5.0% We calculated the overlap of keywords by comparing the keywords in each of the conversational models. Words were designated as keywords if they were not stop words. Stop words are common words that convey little meaning to a sentence, computation is a custom list developed for healthcare scenarios. The overlap of keywords is presented in Table 4 8 Excluding Anne Animus, the conversational models had an average of 51.74% overlap with s.d. = 3.18%. In contrast, the Anne Animus conversational model had an average overlap of 34.36% with s.d. = 2.84%. From this data, we see that the Anne Animus conversational model had less knowledge in common with the other conversational models than the others had with each other. This single data point provides only enough information to indicate that there is a thre shold of information in common below which DKS is not useful. However,
107 this data does not provide enough information to indicate exactly where that threshold lays. Table 4 8 Percentage of overlapping keywords in the conversational models Shown for illustration purposes and only included in mean and s.d. specific to Anne Animus Marty Vinny Jackie Kahlua Johnny John Anne Marty Graw 53.72 % 50.05 % 48.89 % 55.12 % 50.00 % 30.65 % Vinny Devito 53.72 % 51.58 % 46.70 % 55.28 % 48.52 % 31.82 % Jackie Dauer 50.05 % 51.58 % 50.05 % 57.97 % 47.16 % 36.17 % Kahlua Lopez 48.89 % 46.70 % 50.05 % 52.86 % 52.86 % 38.06 % Johnny A Seed 55.12 % 55.28 % 57.97 % 52.86 % 51.42 % 33.52 % John Smith 50.00 % 48.52 % 47.16 % 52.79 % 51.42 % 35.94 % Anne Animus 30.65 % 31.82 % 36.17 % 38.06 % 33.52 % 35.94 % Mean 51.56% 51.16% 51.36% 50.26% 54.53% 49.99% 34.36% s.d. 2.70% 3.56% 4.03% 2.63% 2.51% 2.26% 2.84% 4. 4.4.2 Analysis of method quality Quality of stimuli from each method To evaluate the qua lity of each method we separated the overal l conversational model accuracy into the accuracy per method The DKS method had the highest average response accuracy but also had a high standard deviation ( Table 4 9 ) The VHT, HDCM, and Manual methods were all within 3% of each other. Given the overlapping standard deviations of the accuracies, we find that none of the methods can be said to be unequivocally higher quality than the others. The knowledge from both VHT and DKS requires similarity of the scenarios, and the Anne Animus scenario was dissimilar from both the original scenarios from which the virtual patient template was generated and the other dysphagia scenarios. T he Anne Animus conversational model had low accura cy for VHT For VHT, only 33.33% of the responses were accurate, indicating the inappropriateness of the virtual patient template knowledge for the Anne Animus scenario. For example, the template ions with Anne Animus,
108 interviewers were asking, baby Since the authors filled out the template regarding Anne, the responses were incorrect. As further evidence of the dissimilarity of the conversational model, t he Anne Animus conversational model gave no responses using DKS stimuli, and so the DKS accuracy data is not applicable (N/A) This dissimilarity caused both a lack of knowledge reuse for DKS and low accuracy when the knowledge was attempted to be reused from VHT. Table 4 9 Quality (accuracy) of each stimulus source for each conversational model. The Model Accuracy overall conversat ional model accuracy Shown for illustration purposes and not included in mean or s.d. Model Accuracy Manual HDCM VHT DKS Marty Graw 77.83% 89.74% 75.68% 85.29% 81.58% Vinny Devito 72.84% 69.23% 71.70% 69.23% 96.67% Jackie Dauer 70.82% 67.35% 75.65% 78.00% 76.00% Kahlua Lopez 69.07% 100.00% 78.24% 70.00% 100.00% Johnny A Seed 68.56% 67.74% 71.26% 77.08% 60.47% John Smith 64.26% 50.00% 67.12% 76.32% 73.42% Anne Animus 51.07% 71.43% 63.50% 33.33% N/A Mean 70.56% 74.01% 73.27% 75.99% 81.35% s.d. 4.46% 17.92% 4.01% 5.89% 14.90% 4. 4.4.3 Analysis of method efficiency Efficiency of time spent on each conversational model To calculate the efficiency of time spent on each conversational model, we divided the accuracy of the model by the time spent on that model to calculate the percen tage gain per hour. The conversational model found to have the highest efficiency was John Smith ( Table 4 10 ) John Smith also had the lowest accuracy. This finding m ay at first appear to discount this formula for computing efficiency; however, we note that the John Smith authors spent less total time than any of the other groups, which means a greater portion of their time was used on VHT. The second highest efficienc y conversational model was Jackie Dauer. The Jackie Dauer conversational model also had the third highest accuracy.
109 Table 4 10 The efficiency of time spent on each conversational model. Efficiency is in accuracy gained per hour spent. Conversational modeling time and conversational accuracy are included for comparison. Shown for illustration purposes and not included in mean or s.d. Modeling Time Accuracy Efficiency (per hour) Marty Graw 17.49 77.83% 4.45% Vinny Devito 22.84 72.84% 3.19% Jackie Dauer 14.65 70.82% 4.83% Kahlua Lopez 15.06 69.07% 4.59% Johnny A Seed 14.58 68.56% 4.70% John Smith 11.71 64.26% 5.49% Anne Animus 15.39 51.07% 3.32% Mean 16.06 70.56% 4.54% s.d. 3.80 4.46% 0.75% While we did not find a strong relationship between the total time spent and the accuracy of the conversational models, we did find a strong relationship between the time spent on HDCM and DKS and the accuracy of the conversational model (see Table 4 11 for method time and accuracy) HDCM to accuracy had a Pearson Correlation r 2 = 0 793 p < 0 .0 5 and DKS to accuracy had a Pearson Correlation r 2 = 0 859 p < 0 .05 M odel accuracy HDCM time, and DKS time were found to be normally distributed according to a Shapiro Wilk test for normality Note that Vinny Devito conversational model has the largest amount of time spent manually inputting conversational knowledge. Also note that the Marty Graw s cenario had the largest amount of time spent on the DKS method. Table 4 11 Time spent on each method for each conversational model Author interactions are the author interacting with their own VH. Maintenance tasks include signing the license agreement, changing account settings, and changing character images. Conversational Mo del accuracy included for comparison. Shown for illustration purposes and not included in mean or s.d. Manual HDCM DKS VHT Author Interactions Maintenance Accuracy Marty Graw 2.82 2.50 6.70 0.28 3.56 1.62 77.83% Vinny Devito 7.51 2.53 4.12 0.84 4.96 2.88 72.84%
110 Table 4 11 continued Manual HDCM DKS VHT Author Interactions Maintenance Accuracy Jackie Dauer 3.62 1.57 3.98 0.23 3.02 2.24 70.82% Kahlua Lopez 3.39 1.59 3.63 0.88 1.68 3.88 69.07% Johnny A Seed 2.85 1.03 3.89 0.39 3.55 2.88 68.56% John Smith 2.25 0.81 3.22 0.20 3.74 1.49 64.26% Anne Animus 2.05 1.34 4.56 0.28 6.01 1.14 51.07% Mean 3.74 1.67 4.26 0.47 3.42 2.50 70.56% s.d. 1.91 0.72 1.24 0.31 1.07 0.90 4.46% Efficiency of each method in number of stimuli per hour T he VHT method was found to be the most efficient method of adding stimuli to a conversational model (as shown in the M ean row of Table 4 12 ). Manual was found to be the least efficient method of adding stimuli to a conversational model. On average, VHT was appr oximately five times more efficient than HDCM, nine times more efficient than DKS and twenty four times more efficient than Manual. Table 4 12 Efficiency of each method in terms of the number of stimuli per hour for each convers ational model. Shown for illustration purposes and not included in mean or s.d. Manual HDCM VHT DKS Marty Graw 54.65 139.94 1099.40 63.84 Vinny Devito 45.66 159.72 391.54 68.37 Jackie Dauer 34.52 218.61 1595.56 188.90 Kahlua Lopez 5.31 184.16 269.54 79.81 Johnny A Seed 76.89 197.84 800.86 133.66 John Smith 23.10 280.45 1712.62 85.15 Anne Animus 10.22 293.25 734.26 3.29 Mean 40.02 196.79 978.25 103.29 s.d. 25.01 49.48 602.16 48.80 Limits of stimuli per hour e fficiency metric T he numbers in Table 4 12 reflect the raw efficiency of each method; but they do not take into account the limitations of the method s For example, the virtual patient template used in the study has 320 stimuli.
111 Thus, while VHT may be able to provide a rate of 978.25 stimuli per hour, it provides a maximum of 320 stimuli total. There is also a limit to the HDCM method based on the number of interviews conducted. HDCM can only draw kn owledge from suggestions provided in the interviews. To illustrate this point, w e use the Marty Graw conversational model There were a total of 2 7 interviews with the Marty Graw VH in the three rounds of interactions. These interviews provided 429 suggestions through HDCM 330 of which the authors accepted into the conversational model With only the VHT, HDCM, and Manual methods, the Marty Graw conversational model would only have had 810 stimuli of its 1238 stimuli. Part of the cause of this limi ted number of suggestions is the diminishing returns of interactions. I f the suggestions are all processed, which makes the VH better at responding correctly to utterances there will be fewer incorrect responses, and thus fewer suggestions gained for each additional interaction. The top four most accurate conversational models show diminishing returns for each round of development ( Table 4 13 ) whereas the bottom two and Anne Animus show non diminishing returns. Table 4 13 Suggestions received per utterance for each conversational model Shown for illustration purposes and not included in mean or s.d. Round 1 Round 2 Round 3 Marty Graw 0.441 0.176 0.116 Vinny Devito 0.306 0.146 0.144 Jackie Dauer 0.283 0.221 0.164 Kahlua Lopez 0.388 0.255 0.205 Johnny A Seed 0.230 0.119 0.184 John Smith 0.374 0.190 0.247 Anne Animus 0.282 0.366 0.333 Mean 0.337 0.184 0.177 s.d. 0.078 0.049 0.046
112 Examining the suggestions received per utterance of the Vinny Devito model, w e see an additional negative effect of using the Manual method early in the modeling process. After Round 1, the suggestions received per utterance for the Vinny Devito model dr opped significantly. T his drop may be due to the time the authors spent manually entering stimuli. That manual work may have diminished the returns of the HDCM and DKS method s (both more efficient methods) thus lowering the overall efficiency of their modeling process and increasing the time required The limited resource of novice interactions and diminishing returns are the reason for the DKS method. Continuing our example of the Marty Graw model, the DKS method provided an additional 1150 sug gestions, 423 of which the authors accepted into the conversational model. While the DKS method is subject to the same diminishing returns as the HDCM method, the DKS method extracts additional knowledge from every interview, thereby enhancing the overall knowledge acquired from every minute of novice time. And while there were only 2 7 interactions with the Marty Graw VH, DKS simulated the Marty Graw VH with the 159 interactions of the other VHs. Using a ll four methods in combination allowed the Marty Graw conversational model to reach 1238 stimuli and 77.83% accuracy within the limited number of novice interactions 4. 4.6 Discussion The results of this evaluation indicate that students within a course c an now generate VHs using HDCM with Conversational Know ledge Reuse All six conversational model s (excluding Anne Animus) were as dense as the corpuses of virtual patients created by domain experts and used in classroom instruction (Rizzo et
113 al., 2010; Rossen and Lok, 2012) That is, the six groups created VHs of complexity potentially usable for classroom instruction. The total time required by these groups of students to create a VH was similar to the time spent by a single expert previously (11 25 hours Chapter 3 ) (Rossen and Lok, 2012) However, taking into account the cost of each student learning the Virtual People Factory system and their unfamiliarity with the medical content a similar amount of time spent by a group of students may indicate a faster ove rall time compared to a single expert These findings indicate that the use of VHT and DKS decreased the total t ime to model the conversations. 188.8.131.52 How to most effectively spend time when conversational modeling Given the high efficiency of time spent on VHT ( Table 4 14 ) we find that VHT was the most effective method of acquiring conversational knowledge. Table 4 14 Summary of the usage quality and efficiency of each method. Usage (model) Usage (responses) Quality (accuracy) Efficiency (stimuli) VHT 27.16% 16.25% 75.99% 16.3 per minute HDCM 26.06% 44.16% 73.27% 3.3 per minute DKS 34.94% 14.24% 81.35% 1.7 per minute Manual 11.84% 15.90% 74.01% 0.7 per minute The next most effective method was HDCM because of th e high efficiency of the method as well as the high usage of the knowledge during interactions Table 4 14 After VHT and HDCM, it is not as obv ious which is more effective between Manual and DKS While the Manual method has a slightly higher usage during interactions the DKS method has a higher quality and efficiency. The deciding factor was the relationship between time spent on DKS and the ove rall quality of the conversational model. This relationship indicates that DKS is the more effective
114 method. Table 4 14 presents a summary of usage quality and effic iency of each method ordered by where time was most effectively spent. 184.108.40.206 The effect of the Conversational Knowledge Reuse methods From the study results w e see that the Conversational Knowledge Reuse methods (VHT and DKS ) have improved the efficiency of conversational modeling and thereby facilitated a greater coverage of the conversational space in a shorter period of time The effect becomes apparent when we examine the example of Anne Animus. Because the Anne Animus scenario was different than the other dysphagia scenarios, both the VHT and DKS knowledge was ineffective. The effect was that the Anne Animus authors were using only the HDCM and Manual creat ion methods to generate the conversational model. Unfortunately, with only these tools, the resulting Anne Animus conversational model was of relatively low quality (accuracy = 51.07%). This is despite the authors using a similar amount of time compared to the other groups ( 15 39 hours vs. mean of 1 6 06 hours ). We acknowledge that the time taken by the Anne Animus group to examine and discard suggestions from the DKS method (4.56 hours) contributed to the total time used on conversational modeling. However, the Anne Animus group did process all of the HDCM suggestions they received and spent 2.05 hours manually adding stimuli and responses yet Anne still had no response to 28.51% of the utterances The fact that they processed all of their HDCM suggestions indicates that without Conversational Knowledge Reuse there was no t enough information available to enumerate the conversational space and create a robust conversational model.
115 220.127.116.11 Comparison of the six conversational models The following is a discussion comparing and contrasting each of the conversational models based on their usage, quality, and efficiency The highest quality conversational model in the study was Marty Graw (accuracy = 77.83%) The Marty Graw authors made e fficient use of their time by putting over 50% into processing suggestions (HDCM and DKS) the highest percentage of all the groups This distribution of time contrasts with the time spent by the authors of the Vinny Devito conversational model The Vinny Devito authors achieved the second highest quality (accuracy = 72.84%) but the model required over 20% longer than the Marty Graw conversational model ( 22.84 hours vs. 17.49 hours) and achieved 5% less accuracy. The additional time spent by the Vinny Dev ito authors was caused by putting 33 % of their time (7.51 hours) into the Manual method whereas t he Marty Graw authors spent the least time on the Manual method (2.82 hours 12% ). The Vinny Devito authors put M anual time into the model early in the conver sational modeling process, and thereby lowered the effectiveness of HDCM and DKS by causing fewer suggestions to be gathered The contrasting examples of Marty Graw and Vinny Devito indicate that while one can make progress using the Manual method, it is more efficient to spen d time on the HDCM and Conversational Knowledge Reuse methods. The third most accurate conversational model was that of Jackie Dauer. The Jackie Dauer authors used less time (14.65) than the Vinny Devito authors to achieve a similar ( within 2 .2 %) accuracy (70.82%) authors achieved this accuracy by accepting a high percentage of the suggestions from both HDCM ( 92.24% ) and DKS ( 85.16% ) compared to the average (HDCM = 76.15% and DKS = 43.71%) By accepting suggestions that were off topic, and giving their VH a response to those
116 suggestions, their c onversational model received a boost in efficiency (second highest efficiency with 4.83% accuracy per hour) which resulted in an overall high accuracy for a relatively low time sp ent In contrast, for the next highest accuracy model, Kahlua Lopez the authors took the opposite approach than the Jackie Dauer authors. They accepted a low percentage of HDCM suggestions (64.41%) and a low percentage of DKS suggestions (28.50%). They al so added very few manual stimuli and responses (3.44%). Yet they achieved only a slightly lower accuracy (69 .07 %) than the Jackie Dauer authors did in approximately the same amount of time ( 15.06 hours ). This data indicates that the Kahlua Lopez authors we re very careful with the stimuli they added to the conversational model. The effect was that Kahlua Lopez had no response to utterances more often than any other VH (16.49%) but when she did respond, the responses were highly accurate. The two strategies represented by Jackie Dauer and Kahlua Lopez may be appropriate at different stages in the conversational modeling process. The Johnny A Seed conversational model does not stand out in any measure. Accuracy was near the middle (6 8 5 6% ) a nd modeling time was similar to Jackie Dauer and Kahlua Lopez (14.58). It may be that the consistent middle ground strategy is a less successful than either accepting a high percentage of the suggestions or being careful with the suggestions accepted. The lowest accuracy conversational model was John Smith. The John Smith authors spent fewer hours on modeling the conversation than any other group (11.71 hours). However, the John Smith model had the highest efficiency of accuracy gained for time spent (5.49% per hour).
117 Since the John Smith authors spent less time processing suggestions or manually editing than the other author groups, they received a greater proportion of their efficiency from VHT. Overa ll, we see that different methods are useful at different stages of modeling the conversation. In the first stage, VHT is the most effective, and rapidly seeds the HDCM with DKS process (evidenced by the modeling efficiency of John Smith). In the beginning of the HDCM with DKS process, it may be effective to accept only the suggested stimuli that are highly pertin ent to the conversational model in order to keep the number of suggested stimuli coming from each interaction high (evidenced by the accuracy of K ahlua Lopez). In the later stages, it may be most effective to accept nearly all of the stimul i into the conversational model to achieve maximum coverage of the conversational space (as evidenced by the accuracy of Jackie Dauer). The Manual method is the l east efficient, and should be avoided in the early stages because of the time and efficiency cost s but may be helpful as needed in the later stages to achieve a higher overall coverage of the conversational space (as evidenced by the total time and accura cy of Vinny Devito). In conclusion, we find that the conversational modeling methods are most effective when used in order of their efficiency, first VHT to seed the model, then HDCM with DKS to refine the model, and then Manual to improve the content of t he conversational model 4.5 Technical difficulty of the methods vs. their efficiency This section compares the technical difficulty in implementing eac h of the conversational methods to the efficiency gained by the VH author. The relative ease of implementation and its relationship with efficiency may inform computer engineers
118 where to spend engineering resources. The difficulty of impl ementing the modeling methods appears to be proportional to their efficiency. The Manual method is the easiest to implement, but only allows Centralized Conversational Modeling and is the least efficient method of modeling a conversation. The DKS method is easy to implement but only after HDCM is implemented DKS only requires the background simulation of previously conducted interactions The HDCM method requires two complex systems the tracked interaction system and the suggestions system The interaction system records new stimuli and facilitates user s reporting incorrect respo nses. The interaction system also feeds new knowledge into the suggestions system where it is processed by VH authors. The suggestions system itself has several features that facilitate efficient processing of conversational knowledge (see section 3.2.6). The most efficient method, VHT, als o requires the most implementation and requires knowledge engineering of the templates The implementation of an efficient VHT system requires a complex database backend, an interface to customize the templates, and the g eneration of those templates into a conversational model. Despite the cost of engineering, the VHT method is still effective overall, because computer engineer and knowledge engineer time is leveraged to improve the efficiency of a large number of VH autho rs. It is also important to note the dependency of the conversation knowledge reuse methods on HDCM and Centralized Conversation Modeling to generate the original knowledge that can be reused. Conversational knowledge reuse methods, especially VHT, work on ly when there is conversation knowledge that has been already acquired. For example, if you would like to apply the conversational
119 modeling methods discussed in this paper to a new domain, like training school teachers to have better interactions with chil dren, you will have to build the first few conversational models using HDCM or Centralized Conversational Modeling. Only when sufficient knowledge has been acquired through these methods can a template be created for VHT implementation. 4. 6 Current Data an d Future Work on Factors Influencing the Speed of Conversational Modeling This section describes the variables that determine the speed of conversational modeling using HDCM with Conversational Knowledge Reuse. When modeling conversations for question answ ering VHs the complexity of the scenario and the availability of resources are central to the efficiency of the conversational modeling methods. We discuss what is currently know n about the limitations of these resources and indicate areas where future research will be conducted to further understand the effect of these variables on conversational modeling. 4.6.1 The Complexity of the Scenario In h ealthcare, interviews have varying levels of complexity. The complexity is determined by the health proble ms the virtual patient has, the number of medications and the other topics covered by the scenario. The added complexity for each additional topic is influenced by the complexity of the new topic as well as its interactions with the other topics. These interactions cause the required corpus to grow non linearly with the number of topics. D ifference s in the complexity of new topics are evident if we examine adding a new medication to a patient scenario The number of additional questions asked for
120 each additional medication is dependent on the issues caused by the medication. In the Vic Johnson scenario of Chapter 3, Vic was taking four medications: Zestril, Synthroid, Tu ms, and Aspirin. The number of stimuli asked about each of these medications varies (as seen in Table 4 15 Ulcer), which required more questions to be asked about Aspirin as compared to the other medications The symptom of stomach pain from Aspirin is simpler than some other medication side effects. Had Vic been taking too much Zestril instead of too much aspi rin, he would have had low blood pressure and kidney failure, which would have caused a wider set of secondary symptoms, and likely even more questions about Zestril than were asked about Aspirin. The number of additional stimuli required for one additiona l medication is determined by the implications of that medication on the scenario. Table 4 15 Number of stimuli for medications taken by Vic Johnson Stimuli Aspirin 252 Zestril 177 Synthroid 149 Tums 118 The added complexity of a new topic is also dependent on interactions with other topics of the scenario. For example, if Vic Johnson were also taking Ibuprofen, another medication that may cause stomach pain, there would have been many questions about Ibuprofen; but it would also increase the number of questions about Aspirin. These additional questions about Aspirin would be necessary to distinguish the side effects of Aspirin from the side effects of Ibuprofen.
121 A s a general heuristic, these examples indicate that interactions between the topics cause the complexity of the scenario to grow non linearly with the number of topics. Examining this as a mathematical system, i f we consider the selected topics of the scenario to be the independent input variables and the n umber of required stimuli in the corpus to achieve accuracy to be the dependent output variable, we see that the required number of stimuli (output) is not directly proportional to the number of topics (inputs) The addition of one new topic may require ne w stimuli on many other topics to distinguish the new topics from the existing topic s and would thereby increase the linear fashion This growth pattern warrants further research into the limits of the current technology to represent complex interactions. 4.6.2 The A vailability and Motivation of N ovice Interviewers and VH A uthors The conversational modeling methods require interactions with end users (generally novices) to develop the conversational models. The speed of development depends in part on the number of novice users and their motivation to conduct the interaction. The mo re novice users, and the longer the conversations, the more knowledge will be gathered. However, there are some considerations with how both novice interviewer time and VH author time can be used most effectively. Novices are unlikely to be willing to interact with the same VH multiple times and there are a limited number of novice interviewers available We should therefore strive to acquire maximum knowledge from each novice interaction. In order to get more depth in the conversation and maximize know ledge gathering, VH authors should process all of the knowledge as it arrives during interactions. Processing knowledge immediately causes future questions to be answered correctly and triggers the interviewers to ask
122 follow on questions, thereby gathering additional knowledge. T hough processing knowledge immediately appears ideal, it is also unrealistic. To make knowledge processing more efficient for the VH author, VH authors generally gather a set of suggestions, and process the set all at once. Processi ng sets of suggestions at once saves the VH author time by allowing them to get into a flow of processing suggestions To determine the correct number of interactions in each of these sets developers will weigh the cost of novice interactions against the cost of VH author time Making this calculation will require studying the amount of time saved by processing batches of suggestions at once as well as the amount of knowledge lost by waiting to process novice suggestions. In the Conversational Knowledge Re use study, we required the VH authors to separate their development into three rounds. This separation lowered the difficulty of analyzing and presenting the study results, but may not be the most efficient method of gathering novice knowledge In practice VH authors may achieve better results by process ing the knowledge as soon as the knowledge is available and they have time to process it. Future research will examine the differences in efficiency between processing large blocks of suggestions and proces sing suggestions more frequently. 4.6.3 Improvement from N ovice I nteractions W e examine the effect of novice motivation and VH author availability using an example from the Dysphagia Management study (section 4.4) The Marty Graw conversational model achie ved the highest accuracy of the seven conversational models in the study. An accuracy of 77.83% was achieved using 27 interactions with Marty Graw as well as 159 simulated interactions using DKS. In interactions directly with Marty Graw, Marty had improved accuracy of responses with each round of testing
123 and development. This improvement provided a diminishing number of new stimuli per interaction for each subsequent round of testing ( Table 4 16 ). The rounds had 8 10 interactions plus 53 simulated interactions per round Given these interactions the Marty Graw scenario gained 16 20% accuracy per round of development. These gains in accuracy depended on th re e factors: complexity of the scenario motivation of the novices, and availability of the VH authors. Table 4 16 Number of stimuli and accuracy of resp onses during interactions with Marty Graw for each round of testing N Accuracy of respon ses Average number of questions Average number of new stimuli Round 1 8 42.17% 55.00 24.25 Round 2 10 58.47% 59.20 10.40 Round 3 9 77.83% 41.11 4.78 The complexity of the Marty Graw scenario is indicated by the topics covered in the conversation ( Table 4 17 ). The complexity of this conversation lies in the required depth in each topic as well as the interaction between topics. The scenario contained both deep and shallow topics as well as topics that interact. The Chief Complaint contains deep topic s covering the description of the issue (swallo wing pain), the onset of the issue, and the social implications of the issue. T he f amily history contained shallow topics because the the pertain to the chief complaint and so do not warrant further explo ration by the interviewer The medications and social history are examples of topics that interact. The medication history interacts with the chief complaint, and so was explored in depth because each medication might cause a swallowing disorder if taken i mproperly The social history also interacts with the chief complaint because eating is a significant part of Marty Graw as a Gumbo Chef
124 Table 4 17 Topics covered in the Marty Graw conversation Subject Topics of conversa tion Chief Complaint Swallowing pain caused by esophageal stricture, diet changes to accommodate pain Medication History Avapro, Nexium, Tums, and Motrin Medical History GERD, high blood pressure, tonsillectomy at 18 years, allergic to tree nuts and penicillin, 55 years old Family History Father died of a heart attack, mother has diabetes and high cholesterol Social History Gumbo Chef, married with two daughters, BA in culinary arts, stress from financial problems, stress from work, stress from family Risk Factors Drinks wine daily, previously smoked but does not now, overweight, weight loss caused by diet changes, no exercise, no illegal drugs Greeting/Exit Introduction and exit The second factor is the motivation of the novice interviewers. The novices involved in the development of the Marty Graw had higher motivation than in previous VH developments interviews w ere a part of their coursework and were internally motivated because the interviewers were also VH authors themselves. T he interviewers asked an average of 51.93 questions per interaction. As a comparison, in the development of Vic Johnson (Chapter 4), th e novices asked an average of 37.95 questions per interaction Internal and external motivation strategies have been previously explored for developing conversational models and can be used to increase the motivation of novice participants (Halan et al., 2010) The last factor is the availability of the VH authors. Because this development was on ly during the development phases of the process, that is, between rounds of interactions (see section 4.4.3 Procedure ). If the authors were available more frequently (such as after every interaction), we may have observed larger gains for each novice inter action. We base
125 this expectation on the understanding of how these conversational models grow ; with earlier processing of novice knowledge, the interviewers would have been able to explore more depth in the conversation and therefore provided greater depth of knowledge acquisition Together the complexity of the conversation, the motivation of the novices, and the availability of the VH authors determined the speed of accuracy improvement when developing the Marty Graw conversational model. The convers ation is relatively complex, and is t ypical of a VH patient scenario; the motivation of the novices was higher than in the previous study developing Vic Johnson (Chapter 4) ; and the participation of the authors was similar to the previous study To provide a greater understanding of the effect of the complexity of the conversation, the motivation of novices, and the availability of VH authors, future research will examine varying levels of conversational complexity, strategies for participant motivation, an d differing patterns of author participation. 4. 7 Potential for New Application Areas The Conversational Knowledge Reuse methods described in this chapter may open new application areas. Because these VHs can be generated more quickly they may be useful f or applications where rapid generation is essential. For example, with rapid conversational modeling methods, VHs could be used for emergent situations such as an epidemic of infectious disease. VH authors could rapidly generate a set of VHs to represent p atients displaying signs of the infectious disease, and healthcare practitioners could use these VHs to learn to recognize the symptoms. This system may also be beneficial for using the VH creation process as an educational tool. For example, during the st udy presented, we also examined if the students improved their
126 ability to understand patients. We found that students did improve their ability to perceive the personality of a conversational partner (Halan et al., 2012)
127 CHAPT ER 5 CONVERSATIONAL KNOWLEDGE REUSE: USING VIRTUAL HUMANS TO BOOTSTRAP THE CREATION OF OTHER VIR TUAL HUMANS This chapter presents the analysis, design implementation and evaluation of a novel method for using virtual human knowledge to bootstrap the creation of new virtual human s Virtual Human Bootstrapping ( research questions 1 3 ) I t also demonstrates that this Conversational Knowledge Reuse technique opens a new application of virtual human interpersonal simulation. The implementation of Virtual Human Bootstrapping method is called The Roleplay Trainer Creator The Roleplay Trainer Creator uses existing virtual human conversational knowledge to generate new virtual human s. We describe a study which uses The Roleplay Trainer Creator to generate virtual human medical students. These virtual human medical students are then used for trai ning standardized patient actors to conduct practice medical interviews The design and evaluation of Virtual Human Bootstrapping was published in the proceedings of the International Conference on Intelligent Virtual Agents (Rossen et al., 2010) Personal Contributions I conceptualized the method, implemented the Roleplay Trainer Creator and designed and analyzed the study. Collaborators Dr. Juan Cendan was involved in the discussions of the bootstrapping method and provided valuable reviews and feedback during the d evelopment of The Roleplay Trainer Creator Dr. Cendan also provided access to study participants ( standardized patients ) and served as the domain expert author in creating the virtual human medical student used in the study Relevance to thesis This ch apter describes a method and system that demonstrates an additional application of Conversational Knowledge Reuse to rapidly
128 generate virtual human conversational models, and opens up a new application area for virtual human s. 5. 1 Overview Across a wide r ange of fields, interactions with conversational virt ual humans (VHs) are becoming a part of training interpersonal skills. Medical students practice taking a medical history by conversing with a VH patient (Johnsen et al., 2005) teachers re enact classroom situations with virtual students (Dieker et al., 2007) and soldiers learn conflict resolution in interactions with virtual civilians (Hill et al., 2003) As described in section 1.3.3, d uring development of these human VH interactions, the role played by the human and the role played by the VH are fixed. If these roles could be reversed (e.g. a human patient with a VH doctor) it woul d facilitate interpersonal skills training for additional large populations (Rossen et al., 2010) T raditional methods of creating a conversational model for the reversed role would double development time and effort. To solve this problem, w e propose a new Conversational Knowledge Reuse method Virtual Human Bootstrapping which allows authors to rapidly reverse the roles in these conversations, thereby allowing VHs to play the previously human side of the interaction. The Virtual Human Bootstrapping method uses recordings of human VH interacti ons to bootstrap the creation of a new VH representing the human. When a human take the (originally) human role in the conversation. This is accomplished by
129 sufficient size and scope, we can create a conversational model for the human role in the human VH interaction. The Virtual Human Bootstrapping method was implemented in a web based application called T he Roleplay Trainer Creat or We conducted a study investigating use of T he Roleplay Trainer Creator to apply this bootstrapping technique to medical education. In med ical education interview training, standardized patients (human actors) are paid to roleplay a patient for medical student education. To help train standardized patients for standardized patient interactions, we propose to create virtual medical students f rom hundreds of interactions of medical students with a virtual patient. The virtual medical student generation process is as follows (see Figure 5 1 ). First, m edical students interact with a virtual patient, resulting in a set of interaction logs. Next, those logs are used by T he Roleplay Trainer Creator t o generate a VH medical student. Last, that VH medical student is used to train standardized patients. We report on a pilot study using a generated virtual medical student to train standardized patients. Figure 5 1 The virtual human m edical student creation process, an e xample of virtual human bootstrapping Driving application: healt hcare standardized patient training Standardized patient interactions have been used since 1963 for training medical interview skills. The medical interview is a 5 to 30 minute conversation in which the healthcare professional and patient exchange inform ation. The healthcare professional elicits both verbal and
130 physical symptoms from the patient -for the purposes of this research we focus only on the verbal content. Standardized patients are hired actors that roleplay a medical condition. They interact with many medical students ( i.e. hundreds of students over several days, one student at a time ) and need to represent the same patient in each student interaction Representing the same patient with each student is crucial to providing standardized (i.e. e qual ) education and evaluation of students. Standardizing standardized patients is a difficult problem (Lei et al., 2004; Tamblyn et al., 2009; Walters et al., 2005; Wessel et al., 2003) On average, standardized patients respond with 90.2% correct verbal content of responses, but their accuracy can be as low as 30% (Tamblyn et al., 2009) Reasons for these accuracy errors include: forgetting the correct answer, misunderstanding the question, or use of preparation materials that do not provide Typically, s tandardized patients are trained by personnel that are experienced in the field of healthcare simulation At the University of Florida, healthcare simulation experts spend one hour review ing written materia ls that pertain to the case with the standardized patient. The written materials are approximately ten typed pages. The case material contains instructions on dress, conduct of emotion, and a description of the health history (e.g. Smoking: 2 packs a day) Standardized patients are also shown videos of interactions between medical students and other standardized patients conducting the medical interview. In most instances the standardized patient then takes that written material home and commits the detail s to memory in an effort to portray the character consistently.
131 The standardized patient s grasp the specific goals of the scenario but can exhibit performance variability particularly when specific instructions have not been provided (Tamblyn et al ., 2009) particular answer to this question has not been presented by the case materials the standardized patient has the ability to answer that as they see fit. In many cases this may no t be a problem; however, a response could persuade the interviewing student that alcohol plays a particular role in the scenario when, in fact, it may be irrelevant. These types of details are very important to the standardization of training and testing. T he current methods for training leave out some of these specifics due to the large burden of information as it is currently presented and the passive nature of engagement of the standardized patient as they are largely reading thes e materials with varying levels of supervision. Preparation materials given to standardized patients are also sometimes incomplete because the material creators are unable to anticipate all questions that medical students may ask. The reason for this is t hat the training mater ials are generated by medical interviewing experts We have found that experts cannot predict the many paths that novices will pursue in a medical interview. I n our experience with developing VH patients from 2005 to 2011 (Johnsen et al., 2005) the contents of patient cases are only the essential information the patient is supposed to convey rather than an exhaustive set of what will be asked This limited set of data results in standardized patients: Changing their answers between in teractions because they do not know what the "correct" answer is.
132 Wi th additional training using a v irtual medical student we may be able to increase the standardization of standardized patients. We propose a method for genera ting virtual medical students derived from the questions real medical students asked virtual pati ents As virtual patients are modeled after standardized patients, we anticipate that the questions students ask virtual patients will be predictive of the questions students will ask stan dardized patients. Given a dataset that covers the space of questions medical students will ask, we can generate a representative virtual medical student. The well as th e critical but unusual questions. Since the questions came from medical students themselves, they will be phrased in the manner medical students would use. T hus standardized patients may be able to interact with the virtual medical student to prepare for interactions with real medical students. The Roleplay Trainer. The proposed standardized patient trainer the Roleplay Trainer, allows for engagement of the standardized patient with a system that mimics a dialogue and forces the discovery of previously d efined critical information during that dialogue (details in section 5. 2 ) The Roleplay Trainer provide s the standardized patients with: 1. More complete coverage of the questions that medical students will ask, 2. T he correct responses to those questions, 3. E xpe rience answering the questions and 4. Fe edback on the ir responses to those questions The Roleplay Trainer could serve as a method to enhance recall of important details and minimize the variability in responses that are presented by the standardized patie nt By conversing with the virtual medical student, standardized patients may be better able to standardize their interactions with real medical students, providing
133 increased educational value to the students and more accurate evaluation of medical student competency. 5. 2 Roleplay Trainer Creator: Generating Virtual Versions of the Human Partner 5. 2 .1 Overview The Roleplay Trainer Creator enables domain experts to create question asking VHs from the interaction logs of question answering VHs. These question asking VHs are used to train roleplay partners (standardized patients) to conduct a question answering con versation (such as a medical interview). The challenge with generating a question asking VH that accurately represents a specific class of human (a medical student) within a specific type of conversation (a medical interview) is determining: 1. The representa tive questions to ask. The VH needs to ask not only the common and to the point questions, but also uncommon and possibly off topic questions. 2. The order in which to ask them. The VH ask s questions in an order that is appropriate for a medical student, rath er than a medical expert. The Roleplay Trainer Creator enables a domain expert to create a VH roleplay partner by assisting the expert in selecting representative questions, and ordering those questions appropriately. The system extracts questions that wer e actually asked by users by analyzing the interaction logs of question answering VHs. The original question answering VHs were developed using a collaboration between end users, and domain experts using Human centered Distributed Conversational Modeling These VHs went through conversational model development until they had a robust ability to answer questions. We can infer that because these VHs have a robust ability to answer questions, the logs of these conversations enumerate the space of both sides o f the conversation. Using these log files, we can extract the questions for the question asking side of the conversation.
135 to do the tedious work, and then leverages human intelligence for only the portions of the process that need human intervention. There are two categor ies of questions to select, the most common questions and the critical questions. Selecting the common ques tions is accomplished by: 1. Identifying the semantically identical questions, 2. Analyzing the usage of the questions in the interaction log s and 3. Presenting the questions and responses to a domain expert for final selection To identify semantically identical questions, each question asked was categorized by its similarity to questions that were previously validated for use in the existing question answering VH conversation. Similarity was determined using lexical keyword matching. Matching is performed by comp aring the low frequency words, or keywords, in a question to the low frequency words in the validated questions in the VH conversational model. Next, e ach question in the transcript logs is compared to previously validated questions in the question answer ing conversation al model. The question in the question answering VH conversation al model that has the highest similarity to the question being examined is categorized as semantically identical. Using the test bed VH conversation al model it was previously f ound that the accuracy of this algorithm achieves 80% for new material and 90% for training data (Rossen et al., 2009) The semantically identical questions were then counted once for each log file they appeared in and divided by the total number of log files t o determine the frequency of usage. The frequency of usage is displayed in the interface for selecting which questions to use in the question asking VH seen in Figure 5 2 Experts place check marks next to the questions to select them. They can also choose to select all of the questions used in
136 more than X % of the interactions. With this interface, the most common questions can be quickly selected. In essence, this pro bed scenario can be seen in Figure 5 2 Figure 5 2 The Roleplay Trainer Creator i nterface for selecting questions by usage The example shows the creation of the virtual medical student used in the pilot answeri ng script t o be selected for the question responses from the question The uncommon, but important, questions are needed as well. This is where the expert is essential; they can look through the rest of the questions and select the important questions.
137 Selecting questions for the test bed scenario For the question answerin g VH used in our study, there were 493 semantically unique questions. The appropriate number of questions to use for the question asking VH was determined by examining the average length of a typical virtual patient interview in the test bed scenario. Afte r eliminating outliers, the average length was 40 questions 27. Using the upper end of the average length plus standard deviation, we determined that approximately 70 questions was an appropriate length to maximize the number of questions the system coul d ask the standardized patient while remaining a realistic length. The expert was able to select 71 questions for our virtual medical student. This number was a compromise between maximizing coverage, minimizing expert time to select questions, and having a reasonable interaction length. The expert first selected the top 19 most commonly used questions (questions used in > 8.5% of interactions), and then selected individual questions from the rest of the list. For example, in our test bed scenario the follo wing questions about aspirin were important, but were asked by less than 2% of the users: Sele cting the frequently used and important questions took the expert approximately 60 minutes. Next, the questions were ordered. 5. 2 2 .2 Determining question order The next challenge was determining the question ordering. The system again pre processes the d ata, and enlists the expert to validate the results. The goal is to have the VH roleplay partner (virtual medical student) ask questions in an order similar to the
138 order used by real novices (medical students). Real novice users may take a meandering route ; this is in contrast to how an expert would conduct the interview. Expert interviewers tend to choose an optimal path to arriving at a diagnosis, and so skip extraneous questions. However, these questions are essential to the simulation in that novices wi ll be asking roleplayers these questions. 1. For each selected question, the system finds the locations in the all the log files where the question (or a semantically identical question) was used 2. It normalizes each of those value s according to how far through the interaction the question was used. Normalizing these values is important because the interviews range from 5 minutes to 90 minutes. 3. Last, the system calculates the average of the normalized values. This method correctly sorts questions asked at the beginning and end of the interaction. For example, greetings and eliciting the chief complaint usually happen near the beginning of a medical interview, and medical advice and exit phrases happen at the end. In Figure 5 3 we see that the list of automatically ordered questions is in roughly the correct order. Using the arrows on the left side of the interface the domain expert can re arrang e any out of order questions. the VH will ask, in the order it will question answering VH In the scenario for the pilot study, this method automatically arranged questions in an order roughly similar to a real medical student. T he Rol e play Trainer Creator provided an interface to the expert for validating and fixing the order. Reordering and validating the order of the 71 questions in the test bed scenario required approximately 30 minutes.
139 Figure 5 3 The Roleplay Trainer Creator i nterface for viewing automatically ordered interview questions and reordering the questions 5. 2 2 .3 Simulating the roleplay partner As described in section 5. 1, there are four main goals for the interaction experience. The goals are to provide roleplay trainees with: 1. Coverage of the questions that novices will ask, 2. The correct responses to those questions, 3. E xperience answering the questions, and 4. Feedback on responses to those questions. These four goals were implemented using the Virtual People Factory interaction interface. Virtual People Factory provides a web browser interface to interact with VHs using text base d chat. This interface is similar to online chat interfaces such as AOL Instant Messenger or Gmail Chat in which a user types an input, and receives a text based response. For simulating the roleplay interaction, the Virtual People Factory interface was au gmented with additional features to enable effective training of roleplay partners. A screenshot of the interaction interface is shown in Figure 5 4 The virtual role play partner (virtual medical student) asks questions, and the user (standardized patient) enters text based responses. If the user does not know the answer to a
140 display the answer that the question answering VH would give to the current question asked by the question asking virtual roleplay trainer. As the user goes through the learn the correct response for the case. This experience provides a way to practice formulating responses and learn the case beyond rote memorization. Figure 5 4 T he Roleplay Trainer virtual medical student interaction interface 5. 3 Pilot Study To evaluate The Roleplay Trainer Creator we used a previously generated question answering VH patient case. The case selected is the Dyspepsia case described in the quote below. You are presenting at an outpatient clinic with pa in in the middle of your stomach. The onset of the pain was two months ago. You have been taking Tums twice a day for the stomach pain. You have also been having low back pain for the past three months. You have been taking two Aspirin three times a day fo r the back pain. The Aspirin consumption has caused a stomach ulcer, which is the source of the pain.
141 The case description is the same for the VH patient as it is for the standardized patients. The scenario was originally developed as a 35 year old male, a nd was rewritten to be generic for use with male and female users of varying ages. This case was selected because the VH patient conversation was well developed for answering questions (Rossen et al., 2009) and it was a scenario that was not currently availabl e to the standardized patients at the medical school where the pilot study was conducted. The evaluation study examined the Roleplay Trainer for: 1. Usability: as measured by the standardized patients perceived ease of use 2. Acceptability: as measured by the standardized patients perceived usefulness 3. Learning : as measured by the standardized patients perceived feelings of preparation and confidence for playing the role of the patient pre and post experience The usability and usefulness survey was based on Da ease of use, and user acceptance of information technology survey (Davis, 1989) There are 12 questions in this questionnaire; responses are rated from 1 (unlikely) to 7 preparation and confidence for playing the role of the patient pre and post experience. Preparedness and confidence were rated on a Likert scale from 1 (not very) to 7 (very). This pilot study is intended as a feasibility study, rather than an exhaustive examination of a finished appl ication. We further used the study to elicit feedback from standardized patients on future directions for the project. 5. 3 .1 Population Five ( n = 5) standardized patients at The University of Florida conducted a medical interview with the virtual medical student. The participants varied in age from 22 to 79 with a mean age of 38, and two of the participants were female. Participants varied in
142 pre vious standardized patient experience with as few as 6 previous interactions with medical students, and as many as 100 with a mean of 33.4. Participants were paid their regular hourly wage for the hour they participated in the study. 5. 3 .2 Procedure 1. Participants were introduced to the scenario using a three page printed description on hard copy paper. The paper scenario provided only the most basic information about the scenario. Participants had 10 minutes to review the material. 2. They read and signed the consent form. 3. They were issued the digital pre study questionnaire on a Dell D400 laptop using Internet Explorer 7. 4. They used the laptop to conduct the medical interview with the virtual medical student. 5. They filled out the digital post study question naire 5. 3 .3 Metrics Pre study questionnaire experience in playing a standardized patient, and assessed their feelings of preparedness and confidence in playing the role of the patient in the given scenar io. Preparedness and confidence were rated on a Likert scale from 1 not very to 7 very. Post study questionnaire R e preparedness and confidence in playing the role of the patient. This questionnaire also assesse 5. 3 .4 Results survey to post survey (see Figure 5 5 ). These results display a reduction of variance in
143 sy mptoms. Participants who felt unprepared and low confidence prior to the training experience felt more prepared and confident afterwards, and the participant who felt prepared and confident prior to the interaction felt less prepared and confident after. Figure 5 5 Survey results of p articipant's self assessed pre post preparedness and pre post confidence The perceived usefulness and ease of use were analyzed by computing the mean value of the perceived usefulness and ease of use questions for each participant, then taking the mean over all participants. Survey results indicate that, on average, participant s felt the system was easy to use (6.3 out of 7 0.9), and that it would be moderately helpful for doing their job (4.5 out of 7 1.7). There was significant variance indicated by the high standard deviation; three users felt that interaction with virtua l medical students would be helpful to very helpful for preparation (>= 5), one felt it would be moderately helpful (4), while one user felt the system would not be helpful (3). 5. 3 5 Discussion The results of the study indicate that training standardized patients with a virtual medical student has potential. Three out of five participants reported an increased feeling of preparedness and confidence after interacting with the virtual medical student. The one user whose ratings decreased pre to post surve y rated himself pre 1 2 3 4 5 6 7 1 2 3 4 5 pre-prepared post-prepared 1 2 3 4 5 6 7 1 2 3 4 5 pre-confident post-confident
144 experience at maximum (7), and so may have overrated his preparedness and confidence in the pre survey. According to the usability survey results, the users felt it was easy to converse with the virtual medical student. The usefulness s core had a high standard deviation, indicating that some users found the system to be useful for preparing and others did not. In future studies, with a larger number of users, we may be able to determine which users gain greater benefit from the experienc e, such as less experienced users or users with greater comfort with technology. 5. 4 Interpersonal Training Applications We present Roleplay Trainer Creator, a system that uses logs from user interactions with a VH to generate a new unique VH. This VH is a virtual representation of the aggregate set of users. This method of generating a new VH from existing interaction logs has significant potential for interpersonal training applications. This research has shown that a question asking VH generated using th is method could become a beneficial training tool for increasing the standardization of roleplay partners. Since the time of this research, an independent study was conducted comparing the Roleplay Trainer interactions with traditional standardized patient training techniques using videos The researchers found similar training benefit from each method, and concluded that the Roleplay Trainer has advantages in diversity, participant engagement, and availability (Palathinkal, 2011)
145 CHAPTER 6 CONCLUSIONS 6. 1 Review of Results In this dissertation, we claimed that: The proposed conversational modeling methodologies improve the efficiency of the human effort used to model a conversation. Improved efficiency results in a significantly shorter time to produce mor e accurate conversational models than previous methods. This reduction in time and effort enhances the applicability of virtual humans to real world interpersonal skills education. The results of our evaluations demonstrate that Human centered Distributed Conversational Modeling and Conversational Knowledge Reuse are faster than Centralized Conversational Modeling and can result in a more comprehensive enumeration of the conversational space (Rossen et al., 2012; Rossen and Lok, 2012) These methods speed up the modeling process by enabling novice and expert users to create conversational models in a distributed fashion and reuse conversational knowledge from the creation of previous virtual humans in the creation of new virtual human s (VHs) The se conversational modeling methods enable educators to create conversational models themselves i mprove the efficiency of efforts used to model conversations and result in conversational model s with increased accuracy for both typed and spoken i nteractions. The results also demonstrate that these methods have improved the applicability of VHs to real world applications. Using Human centered Distributed Conversational Modeling and Conversational Knowledge Reuse (Virtual Human Templates, Dynamic Kn owledge Sharing and Virtual Human Bootstrapping) both experts and novices created VH conversational models in 11 to 25 hours for new conversational domains These models were of equivalent or better quality compared to those created in 200
146 hours using Ce ntralized Conversational Modeling. As a result of this improved efficiency, novices can, for the first time, develop accurate conversational models for VH patient interactions within the time available during a single semester (Rossen et al., 2012) This increase in speed has enabled the application of VHs to education in pharmacy (Rossen et al., 2009) osteopathy (Surkunalingam et al., 2009) psychiatry (Foster et al., 2010a; Foster et al., 2010b) dentistry (Pileggi and Childs, 2011) patient centered counseling ( Jackson, 2010) dysphagia management (Rossen et al., 2012) and standardized patient training (Rossen et al., 2010) These findings demonstrate our method implementations have facilitate d the creation of VHs for diverse medical contexts. Last ly w e find that Virtual People Factory is an educationally valuable tool. B oth students and healthcare educators report that the system is a viable method for educating healthcare students in communication skills (Filichia et al., 2010; Foster et al., 2010a; Foster et al., 2010b; Palathinkal, 2011; Peden et al., 2011; Pileggi and Childs, 2011; Rossen et al., 2010; Rossen et al., 2012; Rossen et al., 2009; Rossen and Lok, 2012; Shah et al., 2008; Shah et al., 2012; Shah et al., 2009a; Shah et al., 2009b; Surkunalingam et al., 2009) Educators find that having control over virtual human creation gives them the ability to provide focused learning experiences. Furthermore, educators who have used Virtual People Factory in classroom education are continuing to u se Virtual People Factory on an on going basis 6 .2 Real World Usage In August 2008, we opened Virtual People Factory to the public: http://vpf.cise.ufl.edu. As of September 2011 Virtual People Factory had 5 6 active user s outside of our research group inc luding VH researchers, healthcare practitioners,
147 psychologists, and even high school students. Our healthcare collaborators have used Virtual People Factory to integrate VHs into healthcare curricula at the University of Florida College of Medicine, Univer sity of Florida College of Dentistry, Georgia Health Sciences University, Philadelphia College of Osteopathic Medicine, University of Central Florida College of Medicine, and University of South Florida College of Medicine. From their work, Virtual People Factory facilitated the creation of 37 VHs with over 6 00 questions each (a ten fold increase over the previous rate of VH creation) Those VHs conducted over 2 700 interactions consisting of more than 1 05 ,000 utterances. Now that we have achieved this level of efficiency in the creation of VH conversational modeling there is promise for expanding VH curricula. Our collaborators in healthcare education have already found the incre ased number of VHs to be useful and are hoping to have many more VHs for inter personal skills training With t he continued work of healthcare educators creating VH medical interactions they may achieve the goal of providing a warehouse of diverse VHs 6.3 Future Work T he methods presented in this dissertation have improved the effi ciency of conversational modeling; h owever, there are opportunities to further improve the efficacy of the conversational modeling processes. The foll owing is a list of possible future research to improve knowledge acquisition for VH conversational models. Motivational strategies: further examination of leaderboards to inspire competition narratives to promote dramatic interest and deadlines to encourage timely participation for novice interactions (Halan et al., 2010) Capture conversation: automatically capture knowledge from typed conversations between two humans, and use that knowledge to seed the conversational modeling process for domains i n which virtual human templates have not yet been engineered
148 Conversational model clustering : use document clustering algorithms to determine scenario similarity and indicate the applicability of Dynamic Knowledge Sharing for groups of VHs. Human centered Distributed Conversational Modeling for Virtual Human Templates: use crowdsourcing and human computation to expand existing templates. G eneric response templates : develop a comprehensive template of generic stimuli and responses for fallback during deploy ment use This would allow VHs to respon d realistically to off topic questions. Knowledge acquisition from r oleplay training : use r oleplay t ra ining as a source of knowledge to produce a variety of responses for the original VH These responses could be tag ged based on personality traits and used for specific learning goals (e.g. dealing with a verbally aggressive patient), o r used to provide a variety of equivalent responses to increase the realism of the original VH These projects would further leverage the limited resources necessary for conversational modeling and improve interactions with VHs. But t he ultimate goal of interpersonal simulation is not to provide ever more realistic interaction, it is to improve interactions with real humans. Using t he me thods provided in this dissertation it is now more feasible to create VH based interpersonal skills training curriculums. Now that we can more quickly create diverse sets of VHs, the next step is to validate them for additional practical applications Fo r example, after interviewing a variety of VH patients with stomach pain, are medical students better able to diagnose a human patient with stomach pain? Given a variety of both visual stimuli and conversational scenarios, can VHs be used to not only ident ify ethnic and cultural biases, but alleviate them as well? If these applications are found to provide significant learning, VH based interpersonal skills training c ould be validated for further integration into healthcare curriculums
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157 BIOGRAPHICAL SKETCH Brent H Rossen was born in 1981 in Petaluma, California to Jan and Joel Rossen Brent was raised in California unti l the age of 13, and then moved to Coral Springs, Florida. In Coral Springs, he graduated with honors from J.P. Taravella High in 2000. That year he received a full scholarship to attend the University of Florida. In 2004 Brent was a University Scholar advised by Dr. Paul Fishwick. In 2005, he graduated Summa Cum Laude with his B.S. in Digital Arts and Sciences He continued at the University of Florida to pursue his Ph.D. in computer engineering under the supervision of Dr. Benjamin Lok and received his Ph.D. in the fall of 2011 Brent was awarded a UF Alumni Fellowship to support his research, which focuses on conversational modeling, virtual h uman interfaces and their emerging use for interpersonal skills training. His Ph.D. work explored novel approaches to creating conversational models for simulating natural language conversations with virtual humans. His work received significant international recognition in both the fields of computer science and medicine with 1 7 publications in journals and conferences, including a journal article in the International Journal of Human Computer Studies. Brent and his collabora tors have applied for patents on the technology described in his dissertation. On June 20, 2009, Brent married his college sweetheart, Elizabeth. Brent and Elizabeth en joy traveling the world together they visited five countries during graduate school and plan to see many m ore After a summer internship, Brent received a position at Microsoft working on ma ssively scalable storage systems Brent and Elizabeth currently reside in Redmond, Washington with their dogs Brandy and Beijing.