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

2026-01-14

AI Summary

This work addresses the limited interpretability of existing knowledge tracing models, which hinders their ability to provide educators with actionable instructional guidance. To bridge this gap, the authors propose KTCF, a novel approach that introduces counterfactual explanations into educational settings for the first time. By modeling interdependencies among knowledge concepts, KTCF generates counterfactual explanations and translates them into concrete, executable teaching instructions through natural language post-processing. Integrating knowledge tracing, concept relationship modeling, and linguistic refinement, the method significantly outperforms state-of-the-art baselines across multiple large-scale educational datasets, achieving performance gains of 5.7% to 34%. The resulting teaching instructions effectively reduce students’ cognitive load, offering interpretable and actionable intelligent support for real-world educational practice.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Machine Learning: Transparent, Interpretable, Explainable ML

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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 stakeholders to understand who are often non-experts. We propose KTCF, a counterfactual explanation generation method for KT that accounts for knowledge concept relationships, and a post-processing scheme that converts a counterfactual explanation into a sequence of educational instructions. We experiment on a large-scale educational dataset and show our KTCF method achieves superior and robust performance over existing methods, with improvements ranging from 5.7% to 34% across metrics. Additionally, we provide a qualitative evaluation of our post-processing scheme, demonstrating that the resulting educational instructions help in reducing large study burden. We show that counterfactuals have the potential to advance the responsible and practical use of AI in education. Future works on XAI for KT may benefit from educationally grounded conceptualization and developing stakeholder-centered methods.

Problem

• Knowledge Tracing
• Counterfactual Explanations
• Actionable Recourse
• Explainable AI
• Education

Innovation

• Counterfactual Explanation
• Knowledge Tracing
• Actionable Recourse
• Explainable AI (XAI)
• Educational Intervention