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AutoTutor Emotions

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AutoTutor simulates human tutorial dialog with an animated conversational agent that helps students learn qualitative physics or computer literary by holding conversations in natural language. This project tracks the emotions and knowledge of the learner by dialogue patterns, speech intonation, facial expressions, and body movements. It integrates advances in discourse processes, education, multimedia, psycholinguistics, computational linguistics, and artificial intelligence. The project investigates strategies, processes, practices, and environments that are likely to assist the learners in interactive knowledge construction, particularly at deeper levels of comprehension and problem solving.

PI: Art Graesser

Co-PIs: Stan Franklin, Rosalind Picard, Robert Reilly, Barry Kort

Grant Name: Inducing, Tracking, and Regulating Confusion and Cognitive Disequilibrium During Complex Learning

Grant Number: 0325428

Funding Agency: National Science Foundation

Dates: 2003-2009

Amount: $1,256,000

AutoTutor in the news!

ITSs Using Emotions to Increase Learning in the news!

Significant Publications:

Craig, S., D'Mello, S., Witherspoon, A., & Graesser, A. (2007). Emote aloud during learning with AutoTutor: Applying the Facial Action Coding System to cognitive-affective states during learning. Cognition and Emotion, 22, 777-788.

D'Mello, S. K., Craig, S. D., Sullins, J., & Graesser, A. C. (2006). Predicting affective states through an emote-aloud procedure from AutoTutor's mixed-initiative dialogue. International Journal of Artificial Intelligence in Education, 16, 3-28.

D'Mello, S. K., Picard, R., & Graesser, A. C. (2007). Toward an affect-sensitive AutoTutor. IEEE Intelligent Systems, 22, 53-61.

Graesser, A. C., Jackson, G. T., & McDaniel, B. (2007). AutoTutor holds conversations with learners that are responsive to their cognitive and emotional states. Educational Technology, 47, 19-22.

D'Mello, S. K., Craig, S. D., Witherspoon, A., McDaniel, B., & Graesser, A. C. (2008). Automatic detection of learner's affect from conversational cues. User Modeling and User-Adapted Interaction, 18, 45-80.

Graesser, A. C., D'Mello, S. K., Craig, S. D., Witherspoon, A., Sullins, J., McDaniel, B., & Gholson, B. (2008). The relationship between affect states and dialogue patterns during interactions with AutoTutor. Journal of Interactive Learning Research, 19, 293-312.

D'Mello, S. K., & Graesser, A. C. (in press). Emotions during learning with AutoTutor. In P. J. Durlach, & A. Lesgold (Eds.), Adaptive technologies for training and education. Cambridge, U.K.: Cambridge University Press.

D'Mello, S. K., Lehman, B., & Graesser, A. C. (in press). An affect-sensitive AutoTutor. In R. Calvo and S. K. D'Mello (Eds.). New perspectives on affect and learning technologies. New York: Springer.
Jonathan Wood,
Dec 9, 2009, 2:11 PM