Students' Experiences with Generative AI for Programming Tasks and Independent Problem-Solving

Abstract

Generative AI (GenAI) tools such as ChatGPT have become widely used by computer science students for code generation, debugging, and concept clarification. While these tools can improve efficiency in programming tasks, concerns remain about their potential impact on the independent problem-solving skills that computer science education seeks to cultivate. This study investigates how the use of GenAI during programming tasks shapes the way Computer Science students engage with independent problem-solving. Drawing on semi-structured interviews with seven Computer Science students at TU Delft, Through reflexive thematic analysis, the study identifies two themes. First, students framed their reliance on GenAI as a deliberate, self-governed choice rather than a loss of ability: they kept the interpretive work of understanding and planning, and the judgment of whether a solution was correct, for themselves, while delegating execution and The filling of knowledge gaps, and maintained that they could still work unaided if they chose to. Second, students reported a weakening willingness to persist through difficulty without help, both an immediate lowered threshold to begin effortful tasks and a gradual decline of motivation over time. Mapped onto Pólya's four stages of problem-solving, engagement is retained at the early and evaluative stages but surrendered in the middle, Persistence governs whether students return to a problem after a failed attempt. These findings suggest that GenAI reshapes independent problem-solving less by removing ability than by lowering the effort required to avoid it, and contribute to ongoing discussions about the responsible integration of AI tools in computer science education.