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

arXiv 2601.09156 ↗First posted 14 Jan 2026cs.LG cs.AI cs.CYcited by 0

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Abstract (arXiv)

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.

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References · 22

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  10. 2020Context-Aware Attentive Knowledge Tracingcited by 172
  11. 2021What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Researchcited by 119
  12. 2020Instance-based Counterfactual Explanations for Time Series Classificationcited by 64
10 more
  1. 2022pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Modelscited by 50
  2. 2022Rethinking Attention-Model Explainability through Faithfulness Violation Testcited by 43
  3. 2020Why Attentions May Not Be Interpretable?cited by 41
  4. 2023Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representationscited by 32
  5. 2023Dear XAI Community, We Need to Talk! Fundamental Misconceptions in Current XAI Researchcited by 22
  6. 2022Counterfactual Explanations for Predictive Business Process Monitoringcited by 12
  7. 2022Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Boxcited by 7
  8. 2024A Survey of Explainable Knowledge Tracingcited by 7
  9. 2023Counterfactual Monotonic Knowledge Tracing for Assessing Students' Dynamic Mastery of Knowledge Conceptscited by 5
  10. 2023Interpretable Knowledge Tracing via Response Influence-based Counterfactual Reasoningcited by 4

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