Recent advances in artificial intelligence for education leverage generative large language models, including using them to predict open-ended student responses rather than their correctness only. However, the black-box nature of these models limits the interpretability of the learned student knowledge representations. In this paper, we conduct a first exploration into interpreting latent student knowledge representations by presenting InfoOIRT, an Information regularized Open-ended Item Response Theory model, which encourages the latent student knowledge states to be interpretable while being able to generate student-written code for open-ended programming questions. InfoOIRT maximizes the mutual information between a fixed subset of latent knowledge states enforced with simple prior distributions and generated student code, which encourages the model to learn disentangled representations of salient syntactic and semantic code features including syntactic styles, mastery of programming skills, and code structures. Through experiments on a real-world programming education dataset, we show that InfoOIRT can both accurately generate student code and lead to interpretable student knowledge representations.

翻译：近年来，人工智能教育领域的进展利用了生成式大语言模型，包括用其预测学生对开放式问题的回答（而不仅是预测其正确性）。然而，这些模型的黑箱特性限制了其学习到的学生知识表征的可解释性。本文通过提出InfoOIRT（一种信息正则化开放式项目反应理论模型），首次探索了对潜在学生知识表征的解读。该模型在保持生成开放式编程问题中学生所写代码能力的同时，推动潜在学生知识状态具备可解释性。InfoOIRT通过最大化受简单先验分布约束的固定子集潜在知识状态与生成学生代码之间的互信息，促使模型学习关于显著句法及语义代码特征（包括句法风格、编程技能掌握程度与代码结构）的解耦表征。在真实编程教育数据集上的实验表明，InfoOIRT既能准确生成学生代码，又能获得可解释的学生知识表征。