Leveraging Large Language Models to Enhance Self-Regulated Learning in Programming Education with Explainable AI
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| Title: | Leveraging Large Language Models to Enhance Self-Regulated Learning in Programming Education with Explainable AI |
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| Language: | English |
| Authors: | Christopher C. Y. Yang (ORCID |
| Source: | Journal of Computer Assisted Learning. 2026 42(2). |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 15 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Artificial Intelligence, Self Management, Programming, Models, Feedback (Response), Student Behavior, Academic Achievement, Technology Uses in Education, Coding |
| DOI: | 10.1002/jcal.70206 |
| ISSN: | 0266-4909 1365-2729 |
| 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. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1500467 |
| Database: | ERIC |
| 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. |
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| ISSN: | 0266-4909 1365-2729 |
| DOI: | 10.1002/jcal.70206 |
