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

• URL: http://arxiv.org/abs/2512.04256v1
• Date: Wed, 03 Dec 2025 20:51:16 GMT
• Title: On the Role and Impact of GenAI Tools in Software Engineering Education
• Authors: Qiaolin Qin, Ronnie de Souza Santos, Rodrigo Spinola,
• Abstract summary: generative AI (GenAI) tools like ChatGPT and GitHub Copilot have transformed how software is learned and written.<n>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.<n>This study investigates how undergraduate SE students use GenAI tools, focusing on the benefits, challenges, ethical concerns, and instructional expectations that shape their experiences.
• Score: 2.867517731896504
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Generative AI Practices, Literacy, and Divides: An Empirical Analysis in the Italian Context [32.495879271249414]
  This study presents the first comprehensive empirical mapping of GenAI adoption, usage patterns, and literacy in Italy.<n>Our findings reveal widespread adoption for both work and personal use, including sensitive tasks like emotional support and medical advice.<n>We identify a significant gender divide where women are half as likely to adopt GenAI and use it less frequently than men.
  arXiv  Detail & Related papers  (2025-12-03T11:01:28Z)
• Examining the Usage of Generative AI Models in Student Learning Activities for Software Programming [3.2383459424229835]
  This study investigates how GenAI assistance compares to conventional online resources in supporting knowledge gains across different proficiency levels.<n>We conducted a controlled user experiment with 24 undergraduate students of two different levels of programming experience.<n>Our findings reveal that generating complete solutions with GenAI significantly improves task performance, especially for beginners, but does not consistently result in knowledge gains.
  arXiv  Detail & Related papers  (2025-11-17T11:42:24Z)
• Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities [44.18936398140735]
  Article explores the use of AI tools to facilitate and enhance education.<n> Primers are provided on several core technical issues that arise when using AI in educational settings.<n>The article serves as a resource for researchers and educators seeking to advance AI's role in engineering education.
  arXiv  Detail & Related papers  (2025-09-10T19:19:26Z)
• Encouraging Students' Responsible Use of GenAI in Software Engineering Education: A Causal Model and Two Institutional Applications [0.4306143768014156]
  As generative AI (GenAI) tools become pervasive in education, concerns are rising about students using them to complete rather than learn.<n>This paper proposes and empirically applies a causal model to help educators scaffold responsible GenAI use in Software Engineering (SE) education.
  arXiv  Detail & Related papers  (2025-05-31T19:27:40Z)
• From Recall to Reasoning: Automated Question Generation for Deeper Math Learning through Large Language Models [44.99833362998488]
  We investigated the first steps for optimizing content creation for advanced math.<n>We looked at the ability of GenAI to produce high-quality practice problems that are relevant to the course content.
  arXiv  Detail & Related papers  (2025-05-17T08:30:10Z)
• Insights from the Frontline: GenAI Utilization Among Software Engineering Students [19.31786879151898]
  Generative AI (genAI) tools have become ubiquitous in software engineering (SE)<n>We explore the academic experiences of using genAI tools to complement SE learning and implementations.<n>We uncover the contexts where these tools are helpful and where they pose challenges, along with examining why these challenges arise and how they impact students.
  arXiv  Detail & Related papers  (2024-12-20T07:30:51Z)
• Early Adoption of Generative Artificial Intelligence in Computing Education: Emergent Student Use Cases and Perspectives in 2023 [38.83649319653387]
  There is limited prior research on computing students' use and perceptions of GenAI. We surveyed all computer science majors in a small engineering-focused R1 university. We discuss the impact of our findings on the emerging conversation around GenAI and education.
  arXiv  Detail & Related papers  (2024-11-17T20:17:47Z)
• Misconceptions, Pragmatism, and Value Tensions: Evaluating Students' Understanding and Perception of Generative AI for Education [0.11704154007740832]
  Students are early adopters of the technology, utilizing it in atypical ways. Students were asked to describe 1) their understanding of GenAI; 2) their use of GenAI; 3) their opinions on the benefits, downsides, and ethical issues pertaining to its use in education.
  arXiv  Detail & Related papers  (2024-10-29T17:41:06Z)
• Hey GPT, Can You be More Racist? Analysis from Crowdsourced Attempts to Elicit Biased Content from Generative AI [41.96102438774773]
  This work presents the findings from a university-level competition, which challenged participants to design prompts for eliciting biased outputs from GenAI tools. We quantitatively and qualitatively analyze the competition submissions and identify a diverse set of biases in GenAI and strategies employed by participants to induce bias in GenAI.
  arXiv  Detail & Related papers  (2024-10-20T18:44:45Z)
• Innovating Computer Programming Pedagogy: The AI-Lab Framework for Generative AI Adoption [0.0]
  We introduce "AI-Lab," a framework for guiding students in effectively leveraging GenAI within core programming courses. By identifying and rectifying GenAI's errors, students enrich their learning process. For educators, AI-Lab provides mechanisms to explore students' perceptions of GenAI's role in their learning experience.
  arXiv  Detail & Related papers  (2023-08-23T17:20:37Z)
• Reinforcement Learning Tutor Better Supported Lower Performers in a Math Task [32.6507926764587]
  Reinforcement learning could be a key tool to reduce the development cost and improve the effectiveness of intelligent tutoring software. We show that deep reinforcement learning can be used to provide adaptive pedagogical support to students learning about the concept of volume.
  arXiv  Detail & Related papers  (2023-04-11T02:11:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.