This study analyzes how university students use AI for help in programming, finding they often use it for reactive troubleshooting rather than planned self-regulated learning, which impacts their efficiency.

In plain terms

Generative AI tools, like ChatGPT, offer instant support to students learning to program, but researchers question if this truly helps students learn deeply or just gives them quick answers. This paper addresses this by looking at how students ask for help from AI, defining specific ways students prompt AI (called 'help-seeking trajectories'). They used a framework that linked these prompts to different types of support, like understanding concepts or debugging code. The researchers analyzed over a thousand prompts and thousands of code submissions from 71 students in introductory Python courses. They found that many students primarily used AI for 'reactive troubleshooting' – fixing problems as they arose – instead of engaging in more planned, 'self-regulated problem-solving'. Although different help-seeking patterns didn't significantly change final task scores, they did lead to substantial differences in how many times students submitted their code, suggesting varying levels of efficiency or struggle.