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On the Role and Impact of GenAI Tools in Software Engineering Education

Qiaolin Qin, Ronnie de Souza Santos, Rodrigo Spinola

TL;DR

This study investigates how undergraduate software engineering students use generative AI (GenAI) tools, examining contexts of use, perceived benefits, challenges, ethical concerns, and instructional expectations across two universities. Using a mixed-methods survey grounded in the Usage/Challenge Framework, the authors quantify patterns in concept learning, code-related help, and brainstorming, while identifying issues such as output rationales, misalignment with learning styles, and fairness. The findings support scaffolding, clear ethical policies, and adaptive course design to harness GenAI’s benefits while mitigating risks, emphasizing equitable and responsible integration in SE education. The discussion offers actionable implications for pedagogy, policy, and future research, including longitudinal and cross-cultural studies.

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.

On the Role and Impact of GenAI Tools in Software Engineering Education

TL;DR

This study investigates how undergraduate software engineering students use generative AI (GenAI) tools, examining contexts of use, perceived benefits, challenges, ethical concerns, and instructional expectations across two universities. Using a mixed-methods survey grounded in the Usage/Challenge Framework, the authors quantify patterns in concept learning, code-related help, and brainstorming, while identifying issues such as output rationales, misalignment with learning styles, and fairness. The findings support scaffolding, clear ethical policies, and adaptive course design to harness GenAI’s benefits while mitigating risks, emphasizing equitable and responsible integration in SE education. The discussion offers actionable implications for pedagogy, policy, and future research, including longitudinal and cross-cultural studies.

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.

Paper Structure

This paper contains 19 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. GenAI in Software Engineering Education
  4. The Usage/Challenge Framework
  5. Method
  6. Research Questions
  7. Survey Design
  8. Data Collection
  9. Data Analysis
  10. Results
  11. Discussion
  12. Patterns of Use and the Usage/Challenge Framework
  13. Perceived Benefits and Pedagogical Opportunities
  14. Challenges and Limitations of GenAI in SE Learning
  15. Ethical Perceptions and Academic Integrity
  16. ...and 4 more sections

Figures (2)

  • Figure 1: The participants' background information collected in Q1-Q5.
  • Figure 2: Likert chart of our closed-ended questions results, the number of responses is labeled next to each question title. The questions are segmented according to the five sections introduced in Sec. \ref{['sec:survey_design']}: context of use, perceived benefits, perceived challenges, ethical perspectives, and instructional expectations. Minor responses (i.e., response ratios of less than 5%) are hidden in the caption for improved visualizations.