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.
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A few studies used versions of the California Critical Thinking Disposition Inventory (CCTDI), including its Chinese adaptation (CTDI-CV), to evaluate learners' dispositions toward critical thinking.
“Similarly, a few studies used versions of the California Critical Thinking Disposition Inventory (CCTDI), including its Chinese adaptation (CTDI-CV), to evaluate learners' dispositions toward critical thinking [49,61,83].”
From A Systematic Literature Review on the Pedagogical Implications and Impact of GenAI on Students' Critical Thinking (2026) · cites this paper as [83]
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- Authors
- Li, Y., Keung, J., & Ma, X.
- Affiliations
- City University of Hong Kong (HK)
- Venue
- 2024 International Symposium on Educational Technology (ISET)
- Publisher
- IEEE
- Published
- 2024-07-29
- Pages
- 49-53
- OpenAlex
- W4402897129
- Cited by
- 8
- References
- 20
- Citations by year
- 2026: 3 · 2025: 5
- FWCI
- 3.51
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Symposium on Educational Technology (publishedVersion)
- Funders
- University of Hong Kong
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Keywords
- generative AI, software engineering education, large language models, code generation, AI integration strategies, student competencies, immersive learning, software quality
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