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Affective reasoning holds significant potential for intelligent tutoring systems. Incorporating affective reasoning into pedagogical decision-making capabilities could enable learning environments to create customised experiences that are dynamically tailored to individual students' ever-changing levels of engagement, interest, motivation and self-efficacy. Because physiological responses are directly triggered by changes in affect, biofeedback data such as heart rate and galvanic skin response can be used to infer affective changes in conjunction with the situational context. This article explores an approach to inducing affect models for a learning environment. The inductive approach is examined for the task of modelling students' self-efficacy and empathy for companion agents. Together, these studies on affect in a narrative learning environment suggest that it is possible to build models of affective constructs from observations of the situational context and students' physiological response.
McQuiggan et al. (2009) studied this question.