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Advancing Educational Software with Socially Intelligent Guidebots

Explore the development of pedagogical agents with social intelligence for enhanced communication in educational software. Discover the architecture and experimental basis of socially intelligent systems and how interaction tactics are essential in achieving communication goals. Learn about implications for guidebots and a future wizard-of-oz experiment to assess interaction effectiveness.

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Advancing Educational Software with Socially Intelligent Guidebots

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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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