A maturity model for integrating large language models to address key challenges in software engineering education

The integration of Large Language Models (LLM) into Software Engineering Education (SEE) holds significant potential to address key challenges such as diversity of students, rapid technological changes and resource constraints. This paper provides an extensive multivocal maturity model for the use of LLM in SEE, developed through multivocal literature review (MLR), an empirical survey and an interpretive structural modeling (ISM). The suggested model outlines and assesses the critical process areas (PAs) essential for incorporating LLM into SE curricula and provides a systematic way to improve educational practices. The study finds five maturity levels in SEE issues, such as Initial, Managed, Defined, Quantitatively Managed and Optimizing, related to student engagement, technological development, skills based on practical experiences and ethical issues. A mixed method approach (theoretical and practical) will be employed in exploring the potential of the LLMs for personalised learning, real-time feedback and interactive pedagogies. Teachers and institutions to determine their readiness level to implement LLM in SEE and continuously improve it for further innovation use the outcomes as a reference. The research is a contribution to the expanding research on how to leverage AI in the education and offers a novel framework to pursue the use of LLMs in the educational context of software engineering.