USC / Information Sciences Institute Making Pedagogical Agents More Socially Intelligent Lewis Johnson Director, CARTE USC / ISI ftp://ftp.isi.edu/isd/johnson/si/

USC / Information Sciences Institute Background: Pedagogical Agents (aka Guidebots)

USC / Information Sciences Institute Adele Demo

USC / Information Sciences Institute Claims n Such guidebots require ä Understanding of humans’ activities ä Social interaction skills, i.e., social intelligence n Most tutoring systems understand learner activities, but lack social intelligence n Challenge: to create guidebots with SI Without social intelligence:

USC / Information Sciences Institute Characteristics of Social Agents n Cognizance of other agents ä Aware of their beliefs, attitudes, characteristics n Sensitivity to social relationship, roles n Sensitivity to social context, exchange n Able to manage interactions, taking above into account

USC / Information Sciences Institute Social Intelligence Project n Develop models of social intelligence for educational software ä Track learner cognitive and affective states, personality and learning characteristics ä Manage interaction to maximize communication effectiveness, persuasiveness ä Adapt interaction to the learner ä Track learner-agent interaction as a social relationship

USC / Information Sciences Institute Architecture of SI System

USC / Information Sciences Institute Experimental Basis n Videotaped sessions of computer- based learning with human tutors n Students read written tutorial on line, completed simulation-based exercises n Tutors sat next to students, observed, engaged in dialog as appropriate n Multiple sessions with each student n Intended to provide a model of appropriate guidebot interaction

USC / Information Sciences Institute Conclusions from Videotapes n Dialog consisted of a series of exchanges n On student side: ä Differing degrees of understanding, as well as confidence ä Differing preferences for social interaction ä Differing preferred divisions of roles n On tutor side: ä Monitoring learner activity ä Sensitivity to understanding and confidence n On both sides: ä Use of interaction tactics

USC / Information Sciences Institute Interaction Tactics n Intended to achieve a particular primary goal (communicative, persuasive) n Often address additional subsidiary goals n Listener response monitored to assess primary goal achievement n Tactics revised in response to achievement failure

USC / Information Sciences Institute Example n n Tutor: So it’s asking for regression n n Student: Right, that wasn’t an option… there’s no place… n n Tutor: You want to click on regression here…

USC / Information Sciences Institute Tutor Monitoring of Goal Achievement n Look for student’s verbal acknowledgement (or otherwise) n Look for student actions indicating understanding n Rely on expectations of actions both before and after

USC / Information Sciences Institute Subsidiary Communicative Goals n Tutor phrased comments in order to reinforce learner control and joint activity. E.g.: ä “Why don’t you go ahead and read your tutorial factory” ä “You want to save the factory” ä “I’d skip this paragraph” ä “So why don’t we do that?”

USC / Information Sciences Institute Some Implications for Guidebots n Need to reduce disruptiveness of human-guidebot communication n Communication should be goal and tactic oriented n Communication should be situated in work context n A tactic-oriented approach could also help prevent and repair communication breakdowns

USC / Information Sciences Institute A Tactic-Oriented Learner- Guidebot Interface n Both tutorial view and simulation interface are instrumented n Learner communicates with guidebot ä Directly using selected questions, typed comments  Encoded as dialog moves using DISCOUNT scheme  Utilizes eDrama Learning’s NL parsing technique ä Indirectly via actions, focus of attention n To be added soon: ä Vision tracking -> focus of attention monitoring ä Dialogs to assess learner confidence, update learner characteristics, assess progress in assessing social roles

USC / Information Sciences Institute Next Step: Wizard-of-Oz Experiment n Student interacts with agent enhanced interface ä Controlled by remote tutor n Questions: ä Does tactic model permit appropriate tutorial interaction? ä Will subjects interact with the agent the way they interact face to face with tutors?

USC / Information Sciences Institute Acknowledgments n Faculty: ä Maged Dessouky, Chistoph v. d. Malsburg, Jeff Rickel (USC) ä Richard Mayer (UCSB) ä Helen Pain (U. of Edinburgh) n Research staff: ä Erin Shaw, Kate LaBore, Larry Kite, Kazunori Okada (USC) n Students: ä Lei Qu, Ning Wang (USC) ä Wauter Bosma, Sander Kole (U. of Twente) ä Jason Finley (UCLA) ä Heather Collins (UCSB)