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Making Pedagogical Agents More Socially Intelligent

Making Pedagogical Agents More Socially Intelligent. Lewis Johnson Director, CARTE USC / ISI ftp://ftp.isi.edu/isd/johnson/si/. Background: Pedagogical Agents (aka Guidebots). Adele Demo. Without social intelligence:. Claims. Such guidebots require Understanding of humans’ activities

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Making Pedagogical Agents More Socially Intelligent

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  1. Making Pedagogical AgentsMore Socially Intelligent Lewis Johnson Director, CARTE USC / ISI ftp://ftp.isi.edu/isd/johnson/si/

  2. Background: Pedagogical Agents (aka Guidebots)

  3. Adele Demo

  4. Without social intelligence: Claims • Such guidebots require • Understanding of humans’ activities • Social interaction skills, i.e., social intelligence • Most tutoring systems understand learner activities, but lack social intelligence • Challenge: to create guidebots with SI

  5. Characteristics of Social Agents • Cognizance of other agents • Aware of their beliefs, attitudes, characteristics • Sensitivity to social relationship, roles • Sensitivity to social context, exchange • Able to manage interactions, taking above into account

  6. Social Intelligence Project • Develop models of social intelligence for educational software • Track learner cognitive and affective states, personality and learning characteristics • Manage interaction to maximize communication effectiveness, persuasiveness • Adapt interaction to the learner • Track learner-agent interaction as a social relationship

  7. Architecture of SI System

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

  9. Conclusions from Videotapes • Dialog consisted of a series of exchanges • On student side: • Differing degrees of understanding, as well as confidence • Differing preferences for social interaction • Differing preferred divisions of roles • On tutor side: • Monitoring learner activity • Sensitivity to understanding and confidence • On both sides: • Use of interaction tactics

  10. Interaction Tactics • Intended to achieve a particular primary goal (communicative, persuasive) • Often address additional subsidiary goals • Listener response monitored to assess primary goal achievement • Tactics revised in response to achievement failure

  11. Example • Tutor: So it’s asking for regression • Student: Right, that wasn’t an option… there’s no place… • Tutor: You want to click on regression here…

  12. Tutor Monitoring of Goal Achievement • Look for student’s verbal acknowledgement (or otherwise) • Look for student actions indicating understanding • Rely on expectations of actions both before and after

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

  14. Some Implications for Guidebots • Need to reduce disruptiveness of human-guidebot communication • Communication should be goal and tactic oriented • Communication should be situated in work context • A tactic-oriented approach could also help prevent and repair communication breakdowns

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

  16. Next Step: Wizard-of-Oz Experiment • Student interacts with agent enhanced interface • Controlled by remote tutor • Questions: • Does tactic model permit appropriate tutorial interaction? • Will subjects interact with the agent the way they interact face to face with tutors?

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

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