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)
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Adele Demo
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Claims
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Such guidebots require
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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
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Without social intelligence:
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Characteristics of Social
Agents
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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
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Social Intelligence Project

Develop models of social intelligence
for educational software
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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
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Architecture of SI System
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Experimental Basis
Videotaped sessions of computerbased 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

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Conclusions from Videotapes
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Dialog consisted of a series of exchanges
On student side:
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On tutor side:
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Differing degrees of understanding, as well as confidence
Differing preferences for social interaction
Differing preferred divisions of roles
Monitoring learner activity
Sensitivity to understanding and confidence
On both sides:
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Use of interaction tactics
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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

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Example
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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…
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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

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Subsidiary Communicative
Goals

Tutor phrased comments in order to
reinforce learner control and joint
activity. E.g.:
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“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?”
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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

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A Tactic-Oriented LearnerGuidebot Interface
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Both tutorial view and simulation interface are
instrumented
Learner communicates with guidebot
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Directly using selected questions, typed comments
 Encoded as dialog moves using DISCOUNT scheme
 Utilizes eDrama Learning’s NL parsing technique
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Indirectly via actions, focus of attention
To be added soon:
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Vision tracking -> focus of attention monitoring
Dialogs to assess learner confidence, update learner
characteristics, assess progress in assessing social
roles
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Next Step: Wizard-of-Oz
Experiment
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Student interacts with agent enhanced
interface
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Controlled by remote tutor
Questions:
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Does tactic model permit appropriate tutorial
interaction?
Will subjects interact with the agent the way they
interact face to face with tutors?
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Acknowledgments
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Faculty:
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Research staff:
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Maged Dessouky, Chistoph v. d. Malsburg, Jeff Rickel
(USC)
Richard Mayer (UCSB)
Helen Pain (U. of Edinburgh)
Erin Shaw, Kate LaBore, Larry Kite, Kazunori Okada
(USC)
Students:
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Lei Qu, Ning Wang (USC)
Wauter Bosma, Sander Kole (U. of Twente)
Jason Finley (UCLA)
Heather Collins (UCSB)
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