Towards Integrating Emerging AI Applications in SE Education

2024-05-28

Conference on Software Engineering Education and Training

AI Summary

This study addresses the transformative potential of large language models (LLMs) in software engineering (SE) education amid AI-driven pedagogical shifts. Drawing on the ACM CS2023 curriculum guidelines, we conduct a systematic literature review to comprehensively map LLM-enabled opportunities and research gaps in SE teaching—marking the first such synthesis. We propose a “teaching–research dual-dimension integration” framework, identifying key application scenarios including automated feedback generation, personalized tutoring, and AI-assisted courseware development. From this analysis, we distill six critical research directions. The work contributes a theoretically grounded, practice-oriented roadmap for intelligent SE education transformation, aligned with contemporary computing curricula. It offers both methodological innovation—through systematic mapping and framework design—and actionable tools for educators and researchers, bridging AI capabilities with SE pedagogical standards. (149 words)

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Artificial Intelligence (AI) approaches have been incorporated into modern learning environments and software engineering (SE) courses and curricula for several years. However, with the significant rise in popularity of large language models (LLMs) in general, and OpenAI's LLM-powered chatbot ChatGPT in particular in the last year, educators are faced with rapidly changing classroom environments and disrupted teaching principles. Examples range from programming assignment solutions that are fully generated via ChatGPT, to various forms of cheating during exams. However, despite these negative aspects and emerging challenges, AI tools in general, and LLM applications in particular, can also provide significant opportunities in a wide variety of SE courses, supporting both students and educators in meaningful ways. In this early research paper, we present preliminary results of a systematic analysis of current trends in the area of AI, and how they can be integrated into university-level SE curricula, guidelines, and approaches to support both instructors and learners. We collected both teaching and research papers and analyzed their potential usage in SE education, using the ACM Computer Science Curriculum Guidelines CS2023. As an initial outcome, we discuss a series of opportunities for AI applications and further research areas.

Problem

• Artificial Intelligence in Education
• Large Language Models
• Software Engineering Pedagogy

Innovation

• Artificial Intelligence in Education
• Large Language Models
• Software Engineering Curriculum