Technology Category
Philosophy and Ethics of AI: Artificial General IntelligenceHumans and AI: User Experience and UsabilityNatural Language Processing: Code Generation / Program Synthesis from Natural Language
On the Role and Impact of GenAI Tools in Software Engineering Education
🤖 AI Summary
This study investigates the current adoption, impact mechanisms, and pedagogical integration of generative AI (e.g., ChatGPT, GitHub Copilot) in undergraduate software engineering education. Employing a mixed-methods approach, we conducted a survey with 130 undergraduate students, incorporating Likert-scale and open-ended questions to empirically examine usage contexts, perceived benefits, practical challenges, ethical awareness, and pedagogical expectations. Results indicate widespread use for incremental learning and advanced coding tasks, with students valuing GenAI’s role in cognitive stimulation and self-efficacy enhancement; however, they report significant technical limitations—including poor output adaptability and opaque reasoning—and strongly advocate for structured instructional scaffolding and explicit ethical guidelines. The study proposes an original three-dimensional framework—comprising *instructional scaffolding*, *ethics-informed policy*, and *adaptive pedagogical strategies*—to support equitable, effective, and responsible GenAI-integrated education, grounded in empirical evidence and actionable implementation pathways.
Technology Category
Application Category
📝 Abstract
Context. The rise of generative AI (GenAI) tools like ChatGPT and GitHub Copilot has transformed how software is learned and written. In software engineering (SE) education, these tools offer new opportunities for support, but also raise concerns about over-reliance, ethical use, and impacts on learning. Objective. This study investigates how undergraduate SE students use GenAI tools, focusing on the benefits, challenges, ethical concerns, and instructional expectations that shape their experiences. Method. We conducted a survey with 130 undergraduate students from two universities. The survey combined structured Likert-scale items and open-ended questions to investigate five dimensions: usage context, perceived benefits, challenges, ethical and instructional perceptions. Results. Students most often use GenAI for incremental learning and advanced implementation, reporting benefits such as brainstorming support and confidence-building. At the same time, they face challenges including unclear rationales and difficulty adapting outputs. Students highlight ethical concerns around fairness and misconduct, and call for clearer instructional guidance. Conclusion. GenAI is reshaping SE education in nuanced ways. Our findings underscore the need for scaffolding, ethical policies, and adaptive instructional strategies to ensure that GenAI supports equitable and effective learning.
Problem
Research questions and friction points this paper is trying to address.
Investigates GenAI tool usage in software engineering education
Examines benefits, challenges, and ethical concerns for students
Calls for instructional strategies to support equitable learning
Innovation
Methods, ideas, or system contributions that make the work stand out.
Surveyed student usage of GenAI tools
Identified benefits and challenges in learning
Proposed scaffolding and ethical instructional strategies
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