Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Implementation of motivational tactics in tutoring systems
Journal of Artificial Intelligence in Education
Interaction tactics for socially intelligent pedagogical agents
Proceedings of the 8th international conference on Intelligent user interfaces
Empathic tutoring software agents using real-time eye tracking
Proceedings of the 2006 symposium on Eye tracking research & applications
Detecting the Learner's Motivational States in An Interactive Learning Environment
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
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In this paper, we describe a method for pedagogical agents to choose when to interact with learners in interactive learning environments. This method is based on observations of human tutors coaching students in on-line learning tasks. It takes into account the focus of attention of the learner, the learner's current task, and expected time required to perform the task. A Bayesian network model combines evidence from eye gaze and interface actions to infer learner focus of attention. The attention model is combined with a plan recognizer to detect different types of learner difficulties such as confusion and indecision which warrant intervention. We plan to incorporate this capability into a pedagogical agent able to interact with learners in socially appropriate ways.