A Human-Centric Approach to Explainable AI for Personalized Education

Deep neural networks form the backbone of artificial intelligence (AI) research, with potential to transform the human experience in areas ranging from autonomous driving to personal assistants, healthcare to education. However, their integration into the daily routines of real-world classrooms remains limited. It is not yet common for a teacher to assign students individualized homework targeting their specific weaknesses, provide students with instant feedback, or simulate student responses to a new exam question. While these models excel in predictive performance, this lack of adoption can be attributed to a significant weakness: the lack of explainability of model decisions, leading to a lack of trust from students, parents, and teachers.

This thesis aims to bring human needs to the forefront of eXplainable AI (XAI) research, grounded in the concrete use case of personalized learning and teaching. We frame the contributions along two verticals: technical advances in XAI and their aligned human studies.

We begin with a generalizable approach to student modeling evaluated at scale across 26 online courses with over 100,000 students and millions of student interactions. To enable personalized learning interventions, we evaluate five state-of-the-art explainability methods, finding systematic disagreement between explainers when they are evaluated for the same students and the same models. We then turn to expert educators for ground truth evaluation; they find strong actionability value in explanations, but disagree regarding which explanations are trustworthy.

This thesis therefore presents a shift away from popular approximation-based explainability methods towards model architectures that are inherently interpretable. We propose four complementary technical contributions to enhance interpretability:

• MultiModN, an interpretable, modular, multimodal model which offers accurate predictions and explanations even with missing data, at the fraction of the number of parameters.
• InterpretCC, an interpretable mixture-of-experts model that uses adaptive sparsity to produce concise explanations without sacrificing performance.
• An exploration of adversarial training, to improve the consistency and stability of post-hoc explainers in educational settings.
• iLLuMinaTE, an LLM-XAI pipeline that generates user-friendly, actionable, zero-shot explanations as a communication layer for XAI.

This thesis also places a strong emphasis on measuring the human perception, actionability, and real-world usefulness of new technical contributions. We conducted four human-AI user studies:

• 26 university professors participate in semi-structured interviews to validate post-hoc explanations for course design across LIME, SHAP, and confounder explanations.
• 56 teachers evaluate hybrid (visual and text) explanations in the first known perception study of intrinsically interpretable models.
• 20 expert learning scientists participate in semi-structured interviews t