Integrating Generative AI in Software Engineering Education: Practical Strategies
Abstract
The transformative influence of generative artificial intelligence (AI), notably large language models (LLMs), has significantly reshaped the software engineering (SE) landscape, impacting various aspects of software development within industry and academia. The imperative to integrate generative AI into educational programs arises from the necessity to furnish graduates with contemporary methodologies that enhance software quality and streamline development processes. Nevertheless, a research gap exists concerning the systematic integration of established SE education guidelines with specific course contexts to strengthen SE education through incorporating generative AI. In response to this gap, our study presents a vision for integrating generative AI into SE education, with a particular emphasis on practical integration strategies aimed at endowing students with essential competencies tailored for contemporary software development. Aligning our vision with the knowledge domains within SE education, we delineate its application across specific areas such as code generation, auto test case completion, and others. The overall objective of these proposed initiatives is to furnish students in SE with an updated and immersive learning experience, thereby addressing the evolving demands of the field. In support of high-quality SE education, the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE) jointly provide guidance through the "Curriculum Guidelines for Undergraduate Degree Programs in Software Engineering" (SE2014, for short) [9]. This framework delineates ten distinct knowledge areas, including software design, software process, and software quality, which outline specific disciplines within SE deemed essential for graduates to possess proficiency upon completion of their studies. In this paper, our objective is to present a vision discussing the conceptual framework for integrating generative AI into SE education. This involves synthesizing analysis by incorporating SE2014 guidelines and considering contextual relevance within our course structure. The proposed framework emphasizes the practical integr