The media equation: how people treat computers, television, and new media like real people and places
Affective Learning — A Manifesto
BT Technology Journal
Establishing and maintaining long-term human-computer relationships
ACM Transactions on Computer-Human Interaction (TOCHI)
Toward an Affect-Sensitive AutoTutor
IEEE Intelligent Systems
Modeling self-efficacy in intelligent tutoring systems: An inductive approach
User Modeling and User-Adapted Interaction
Mind and Body: Dialogue and Posture for Affect Detection in Learning Environments
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Emotions and Learning with AutoTutor
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Sensors Model Student Self Concept in the Classroom
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Affective Gendered Learning Companions
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Recognizing dialogue content in student collaborative conversation
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
Coaching within a domain independent inquiry environment
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Diagnosing self-efficacy in intelligent tutoring systems: an empirical study
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
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If computers are to interact naturally with humans, they must express social competencies and recognize human emotion. This talk describes the role of technology in responding to both affect and cognition and examines research to identify student emotions (frustration, boredom and interest) with around 80% accuracy using hardware sensors and student self-reports. We also discuss “caring” computers that use animated learning companions to talk about the malleability of intelligence and importance of effort and perseverance. Gender differences were noted in the impact of these companions on student affect as were differences for students with learning disabilities. In both cases, students who used companions showed improved math attitudes, increased motivation and reduced frustration and anxiety over the long term. We also describe social tutors that scaffold collaborative problem solving in ill-defined domains. These tutors use deep domain understanding of students' dialogue to recognize (with over 85% accuracy) students who are engaged in useful learning activities. Finally, we describe tutors that help online participants engaged in situations involving differing opinions, e.g., in online dispute mediation, bargaining, and civic deliberation processes.