Explainable Learner Models: Concepts, Classifications, and Datasets
Bo Jiang · 2025
This chapter builds on the foundational concepts presented in the preceding chapter regarding trustworthy AI in education. It intricately examines Explainable Learner Models (XLMs), which are crucial to the functionality of adaptive learning systems. The chapter investigates advanced methodologies and illustrative examples that showcase the effectiveness of XLMs in tailoring personalized learning trajectories. Whereas Chapter 1 underscored the importance of transparency and trustworthiness in AI models, this chapter concentrates on the concrete applications of XLMs, highlighting both their practical advantages and the obstacles they present.