Student Research Abstract: Integrating Large Language Models in Software Engineering Education: A Pilot Study through GitHub Repositories Mining
TL;DR
These findings provide initial empirical validation of motivator/demotivator tax-onomies, reveal research–practice gaps, and establish a basis for a comprehensive framework supporting responsible adoption of LLMs in SE education.
Abstract
Context: Large Language Models (LLMs) such as ChatGPT are increasingly used in software engineering (SE) education, creating both opportunities and challenges that require systematic study for responsible curricular integration. Objective: This research seeks to build a validated framework for integrating LLMs into SE education through taxonomy development, empirical studies, and case analyses. This paper reports the first empirical step. Method: We mined 400 GitHub projects, analyzing README files and issue discussions to detect motivator and demotivator themes previously synthesized in our literature review [7]. Results: Key motivators included engagement and motivation (227 hits), software engineering process understanding (133 hits), and programming assistance & debugging support (97 hits). Prominent demotivators were plagiarism & IP concerns (385 hits), security, privacy & data integrity (87 hits), and over-reliance on AI in learning (39 hits). Demotivators such as challenges in evaluating learning outcomes and difficulty in curriculum redesign had no hits. Conclusion: These findings provide initial empirical validation of motivator/demotivator taxonomies, reveal research-practice gaps, and establish a basis for a comprehensive framework supporting responsible adoption of LLMs in SE education.