KTCF: Actionable Recourse in Knowledge Tracing via Counterfactual Explanations for Education

This paper proposes KTCF, a counterfactual explanation method for Knowledge Tracing models that generates actionable educational instructions by identifying which prior learning activities a student should revise to improve their predicted mastery on future questions. The method converts AI predictions into pedagogically meaningful feedback that guides students toward desired learning outcomes.

Using Artificial Intelligence to improve teaching and learning benefits greater adaptivity and scalability in education. Knowledge Tracing (KT) is recognized for student modeling task due to its superior performance and application potential in education. To this end, we conceptualize and investigate counterfactual explanation as the connection from XAI for KT to education. Counterfactual explanations offer actionable recourse, are inherently causal and local, and easy for educational stakeholde

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