Leveraging Large Language Models to Enhance Self‐Regulated Learning in Programming Education With Explainable AI

ABSTRACT Background While prior research has shown that timely and personalised feedback improves students' learning outcomes and self‐regulation, most existing systems fail to provide actionable, individualised explanations at scale, especially in programming education. Manual feedback is resource‐intensive, and traditional Artificial Intelligence (AI) systems often lack transparency, limiting their pedagogical value. Objectives This study addresses these gaps by leveraging Large Language Models (LLMs) and Explainable AI (XAI)—specifically, the SHapley Additive exPlanations (SHAP) method—to generate interpretable, scalable feedback that enhances self‐regulated learning (SRL) in the context of programming education. Methods In the present study, behavioural data were collected from BookRoll, an e‐reading system that tracks interactions like highlighting and note‐taking, and VisCode, a coding platform that records compile attempts, error types and code execution behaviour. Combined with self‐reported strategy data, these formed the LBLS dataset used to train a predictive model. SHAP was used to identify key learning features, which were then input into the GPT‐4 model to generate personalised mid‐semester reports. Results Results showed that students receiving LLM‐generated suggestions improved in SRL behaviours and final performance. Most found the feedback understandable and useful, though some questioned its accuracy. Conclusion This study demonstrates the potential of combining LLMs and XAI to deliver meaningful, scalable feedback, but also highlights the need for human oversight.