Self-regulation refers to the ability to plan, monitor, control and reflect on one’s problem-solving process. Prior research has shown that self-regulated learning (SRL) strategies help improve novice performance in solving programming problems. However, with the advent of LLM tools like ChatGPT, novices can generate fairly accurate code by just providing the problem prompt, and hence may forego applying essential self-regulation strategies such as planning and reflection to solve the problem.

In this position paper, we discuss challenges and opportunities that generative AI technologies pose for novices’ self-regulation strategies in the context of programming problem solving. We believe that the key challenge facing educators is that such technologies can hamper novices’ ability to regulate their programming problem solving process.

On the other hand, they also open up the possibility to design new interventions that can promote better SRL strategies in learners. We draw on domain-specific self-regulated learning theories as the basis of our work, and propose an SRL framework that considers usage of generative AI tools in programming problem solving. We also discuss how the proposed framework can serve as a guideline for CS educators to integrate generative AI features in learning interventions for fostering self-regulation in students.