Exploratory case study reveals diverse help-seeking and self-regulation patterns in programming students using ChatGPT, highlighting the need for critical evaluation of AI responses.
Artificial intelligence (AI) technologies, including intelligent robots and adaptive learning systems, are increasingly integrated into education. However, educators often lack pedagogical guidance on their effective use in higher education. This study explores students’ self-regulatory behaviours and academic help-seeking practices while using ChatGPT during a web-programming task. An exploratory qualitative case study was conducted with 10 s-year computer programming students. Data from open-ended questionnaires and ChatGPT interaction records were analysed using content analysis. Findings indicated self-regulatory behaviours associated with foresight, performance, and self-reflection. Students mainly sought support for web-programming syntax, solution evaluation, and feedback. They used different help-seeking approaches and formulated prompts with varying detail and contextual information. Although several students expressed concerns about the accuracy of ChatGPT's responses, explicit verification was limited. The findings highlight ChatGPT's potential as a source of academic help during web-programming tasks while emphasising the importance of critically evaluating and adapting AI-generated responses.
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Keskinkılıç et al. (2026) studied this question.
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