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This presentation describes the importance of detecting and responding to users emotion while they work with online environments. Emotion is vital to learning and using technology to recognize users’ emotion has led to powerful performance results. First, we describe how to detect emotion, using sensors (camera, wrist band, pressure mouse, seat sensors). Computational tutors dynamically collected data streams of students’ physiological activity and self-reports of emotions. Summaries of student physiological activity helped predict more than 80% of the variance of students’ emotional states. Second, we describe responses or interventions that we used once emotion was detected, i.e., we evaluated the impact of animated embodied agents on user motivation and achievement. Results showed that women and students with disabilities, while using agents reported increased math value, self-concept and mastery orientation and reduced frustration. Third, we describe the integration of computer vision techniques to improve detection of emotion.