Moody agents: Affect and discourse during learning in a serious game


The current study investigated teacher emotions, student emotions, and discourse features in relation to learning in a serious game. The experiment consisted of 48 subjects participating in a 4-condition within-subjects counterbalanced pretest-interaction-posttest design. Participants interacted with a serious game teaching research methodology with natural language conversations between the human student and two artificial pedagogical agents. The discourse of the artificial pedagogical agents was manipulated to evoke student affective states. Student emotion was measured via affect grids and discourse features were measured with computational linguistics techniques. Results indicated that learner’s arousal levels impacted learning and that language use is correlated with learning.

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)