Toward Affect-Sensitive Virtual Human Tutors: The Influence of Facial Expressions on Learning and Emotion

Nicholas V. Mudrick

Michelle Taub

Roger Azevedo

Jonathan Rowe

James Lester

Abstract: Affective support can play a central role in adaptive learning environments. Although virtual human tutors hold significant promise for providing affective support, a key open question is how a tutor’s facial expressions can influence learners’ performance. In this paper, we report on a study to examine the influence of a human tutor agent’s facial expressions on learners’ performance and emotions during learning. Results from the study suggest that learners’ performance is significantly better when a human tutor agent facially expresses emotions that are congruent with the content relevancy. Results also suggest that learners facially express significantly more confusion when the human tutor agent provides incongruent facial expressions. These results can inform the design of virtual humans as pedagogical agents can inform the design of virtual humans as pedagogical agents and designing intelligent learner-agent interactions.

This publication uses Facial Expression Analysis which is fully integrated into iMotions Lab

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