The dynamics of the self-regulation process in student-AI interactions: The case of problem-solving in programming education

Abstract

Generative Artificial Intelligence (AI) has demonstrated significant value in code generation and support for programming tasks, leading to its widespread adoption in both industry and academia. This proliferation has introduced new opportunities and risks for learning, teaching, and the broader landscape of computer science education. Despite this rapid integration, there remains limited understanding of how students engage with these tools, particularly in terms of collaboration, dependence, and delegation. More critically, little is known about how students regulate their interactions with AI tools over time, and how such processes contribute to problem-solving. In this case study, we coded 2,376 interactions from 120 undergraduate students with ChatGPT in a web programming course using a theoretically grounded self-regulated learning (SRL) in problem-solving coding scheme. To move beyond static counts and capture the temporal and structural dynamics of these interactions, we employed novel process-oriented learning analytics: transition network analysis and Sequence Analysis. Our findings reveal that while students frequently employed regulatory prompts aimed at process monitoring and problem-solving, they rarely engaged in deeper metacognitive strategies such as reflection or evaluation. This suggests a prevailing focus on surface-level regulation over deeper learning processes in student-AI interactions. Our results serve as a critical warning, highlighting a tendency towards ’cognitive offloading’ that could undermine the development of independent, lifelong learners in computer science.