Affective computing
Explanation Patterns: Understanding Mechanical and Creatively
Explanation Patterns: Understanding Mechanical and Creatively
Predicting student emotions in computer-human tutoring dialogues
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Toward an Affect-Sensitive AutoTutor
IEEE Intelligent Systems
Gender-Specific Approaches to Developing Emotionally Intelligent Learning Companions
IEEE Intelligent Systems
Automatic detection of learner's affect from conversational cues
User Modeling and User-Adapted Interaction
Modeling self-efficacy in intelligent tutoring systems: An inductive approach
User Modeling and User-Adapted Interaction
International Journal of Artificial Intelligence in Education
Automatic Detection of Learner's Affect From Gross Body Language
Applied Artificial Intelligence
Emotions and Learning with AutoTutor
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Achieving rapport with turn-by-turn, user-responsive emotional coloring
Speech Communication
The intricate dance between cognition and emotion during expert tutoring
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
A time for emoting: when affect-sensitivity is and isn't effective at promoting deep learning
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
Modelling empathy in social robotic companions
UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling
Monitoring affect states during effortful problem solving activities
International Journal of Artificial Intelligence in Education
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on highlights of the decade in interactive intelligent systems
eAssessment for 21st century learning and skills
EC-TEL'12 Proceedings of the 7th European conference on Technology Enhanced Learning
Toward Exploiting EEG Input in a Reading Tutor
International Journal of Artificial Intelligence in Education - Best of AIED 2011
Inducing and Tracking Confusion with Contradictions during Complex Learning
International Journal of Artificial Intelligence in Education - Best of AIED 2011
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This paper describes two affect-sensitive variants of an existing intelligent tutoring system called AutoTutor. The new versions of AutoTutor detect learners' boredom, confusion, and frustration by monitoring conversational cues, gross body language, and facial features. The sensed cognitive-affective states are used to select AutoTutor's pedagogical and motivational dialogue moves and to drive the behavior of an embodied pedagogical agent that expresses emotions through verbal content, facial expressions, and affective speech. The first version, called the Supportive AutoTutor, addresses the presence of the negative states by providing empathetic and encouraging responses. The Supportive AutoTutor attributes the source of the learners' emotions to the material or itself, but never directly to the learner. In contrast, the second version, called the Shakeup AutoTutor, takes students to task by directly attributing the source of the emotions to the learners themselves and responding with witty, skeptical, and enthusiastic responses. This paper provides an overview of our theoretical framework, and the design of the Supportive and Shakeup tutors.