Context: Large Language Models (LLMs) such as ChatGPT are increasingly adopted in software engineering (SE) education, offering both opportunities and challenges. Their adoption requires systematic investigation to ensure responsible integration into curricula. Objective: This doctoral research aims to develop a validated framework for integrating LLMs into SE education through a multi-phase process, including taxonomies development, empirical investigation, and case studies. This paper presents the first empirical step. Method: We conducted a pilot repository mining study of 400 GitHub projects, analyzing README files and issues discussions to identify the presence of motivator and demotivator previously synthesized in our literature review 8 study. Results: Motivators such as engagement and motivation (227 hits), software engineering process understanding (133 hits), and programming assistance and debugging support (97 hits) were strongly represented. Demotivators, including plagiarism and IP concerns (385 hits), security, privacy and data integrity (87 hits), and over-reliance on AI in learning (39 hits), also appeared prominently. In contrast, demotivators such as challenges in evaluating learning outcomes and difficulty in curriculum redesign recorded no hits across the repositories. Conclusion: The study provides early empirical validation of motivators/demotivators taxonomies with respect to their themes, highlights research practice gaps, and lays the foundation for developing a comprehensive framework to guide the responsible adoption of LLMs in SE education.
Khan et al. (Fri,) studied this question.