On The Role And Impact Of Genai Tools In Software Engineering Education

Qiaolin Qin, Polytechnique Montréal

Ronnie de Souza Santos, University of Calgary

Rodrigo Spinola, Virginia Commonwealth University

Date

12/2025

AI Overview

Why it matters

Generative AI tools offer new learning support in software engineering education but also create concerns about over-reliance, ethical use, and learning impacts, pressing educators and institutions to reevaluate pedagogical approaches, assessment methods, and policy frameworks.

Study at a glance

Question: How do undergraduate software engineering students use and perceive generative AI tools regarding benefits, challenges, ethics, and instruction?
Setting and data: Two universities (Virginia Commonwealth University and University of Calgary), 130 undergraduate software engineering students, surveyed during weeks 2–4 of the semester.
Method: Mixed methods survey combining Likert-scale items and open-ended questions with descriptive analysis.
AI tools: ChatGPT, GitHub Copilot, Gemini, GPT-4

Key findings

Over 75% of students agreed they use generative AI to learn basic and deeper concepts, and 58% agreed they use it to improve or optimize code.
Students valued generative AI for brainstorming and examples, but 86 students reported experiencing confusion or frustration from issues like hallucinations and lack of explanations.
While 77% considered acknowledging generative AI ethical, 78% found using outputs without understanding unacceptable, and 79% agreed assignments should adapt to AI tools.

Summary drafted with AI from the full paper and may contain errors. See the full paper for details.

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.