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.

翻译：利用人工智能改进教学与学习，能够提升教育的适应性与可扩展性。知识追踪（KT）因其在教育领域卓越的性能与应用潜力，被公认为学生建模任务的关键技术。为此，我们将反事实解释概念化并研究其作为可解释人工智能（XAI）与教育实践之间的桥梁作用。反事实解释提供可操作的干预方案，具有内在的因果性与局部性，且易于通常为非专业人员的教育相关者理解。我们提出KTCF——一种考虑知识概念关联的KT反事实解释生成方法，以及一个将反事实解释转化为教育指导序列的后处理方案。我们在大规模教育数据集上进行实验，结果表明KTCF方法相比现有方法具有更优越且稳健的性能，各项指标提升幅度达5.7%至34%。此外，我们通过定性评估证明，后处理方案生成的教育指导有助于减轻沉重的学习负担。研究表明反事实解释具有推动人工智能在教育领域负责任、实践性应用的潜力。未来面向KT的XAI研究可受益于教育导向的概念构建及以利益相关者为中心的方法开发。