Theories and technologies are needed to understand and integrate knowledge of student affect (e.g., frustration, motivation, and selfconfidence) into models of learning. One goal of this research is to integrate tools that model student affect into intelligent tutors and enable tutors to elicit, sense, communicate, measure and respond to student affect. This article presents multiple solutions towards this goal and discusses systems that begin to redress the cognitive vs. affective imbalance in teaching. We describe our broad approach, how we will evaluate the impact of affect intervention on student learning and three main development objectives: i) affect recognition, ii) interventions in response to student affect, and iii) emotionally animated agents.

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