ABSTRACT
With the integration of Artificial Intelligence techniques into the field of education and learning, various tools including intelligent tutoring systems for facilitating learning and improving learning experience have emerged. Affective tutoring systems (ATS) enable recognition of human emotions which could be employed in collaborative learning systems for monitoring and tracking learners. This chapter discusses the efficiency of an ATS – an electronic learning system in detecting and responding to the emotional states of learners. The tutoring system introduced in this chapter utilizes an existing generic affective application model to infer and appropriately respond to the learner’s affective state. This approach brings several advantages, notably the direct support for reuse and retrospective addition of affect sensing functionality into existing e-learning software. The evaluation of the effectiveness of this tutoring system has yielded positive findings and shows that measurable improvements in perceived learning may be obtained with a modest level of software development. The chapter also discusses different developments and implications of ATS and the future directions. It is hoped that these findings will demonstrate the viability of the approach and generate new avenues for real-world implementation and evaluation in the future.
