Design of AI-Powered Tool for Self-Regulation Support in Programming Education
This work addresses the problem of isolated AI tools for educators and students in programming education, offering an incremental improvement by integrating with existing systems like Moodle.
The researchers tackled the disconnect between LLM tools and institutional Learning Management Systems in programming education by developing CodeRunner Agent, an integrated assistant that provides context-aware, AI-generated feedback and enhances self-regulated learning skills.
Large Language Model (LLM) tools have demonstrated their potential to deliver high-quality assistance by providing instant, personalized feedback that is crucial for effective programming education. However, many of these tools operate independently from institutional Learning Management Systems, which creates a significant disconnect. This isolation limits the ability to leverage learning materials and exercise context for generating tailored, context-aware feedback. Furthermore, previous research on self-regulated learning and LLM support mainly focused on knowledge acquisition, not the development of important self-regulation skills. To address these challenges, we developed CodeRunner Agent, an LLM-based programming assistant that integrates the CodeRunner, a student-submitted code executing and automated grading plugin in Moodle. CodeRunner Agent empowers educators to customize AI-generated feedback by incorporating detailed context from lecture materials, programming questions, student answers, and execution results. Additionally, it enhances students' self-regulated learning by providing strategy-based AI responses. This integrated, context-aware, and skill-focused approach offers promising avenues for data-driven improvements in programming education.