AI paper index
Unpacking Human–GenAI Collaboration in Software Engineering Education
One-line summary
An AI research paper on Unpacking Human–GenAI Collaboration in Software Engineering Education.
Engineering notes
Engineering notes will be added by the aipentium editorial team.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。
Original abstract
Generative AI tools such as ChatGPT, Claude, and Gemini are increasingly shaping how students engage with software engineering (SE) problem-solving. However, there is limited understanding of how learners collaborate with GenAI across different stages of the software development lifecycle (SDLC), and how learner agency shifts during this process. This research unpacks the problem-solving strategies, collaboration patterns, and agency shifts underlying human–GenAI collaboration in software engineering education. Using a mixed-methods approach, it draws on multimodal data including screen recordings, GenAI transcripts, task artefacts, and retrospective think-aloud interviews. Preliminary findings suggest that learners engage with GenAI differently across SDLC phases, with variation in reliance, evaluation, and decision-making. Future work will extend this analysis to larger and more diverse learner groups and inform pedagogical supports for effective, critical, and agentic GenAI-supported problem-solving.
Links and sources
Need this topic turned into a technical roadmap?
aipentium can prepare a custom AI literature review, code map, dataset map, and B2B technology assessment.
Request B2B AI research
Comments