AI chatbots in programming education: Students’ use in a scientific computing course and consequences for learning
Open accessAbstract
Teaching and learning in higher education require adaptation following students’ inevitable use of AI chatbots. This study contributes to the empirical literature on students’ use of AI chatbots and how they influence learning. The aim of this study is to identify how to adapt programming education in higher engineering education. A mixed-methods case study was conducted of a scientific computing course in a Mechanical Engineering Master’s program at a University of Technology in [blinded for review]. Data consisted of 29 student questionnaires, a semi-structured group interview with three students, a semi-structured interview with the teacher, and 29 students’ grades. Results show that students used ChatGPT for error checking and debugging of code, increasing conceptual understanding, generating, and optimizing solution code, explaining code, and solving mathematical problems. While students reported advantages of using ChatGPT, the teacher expressed concerns over declining code quality and student learning. Furthermore, both students and teacher perceived a negative influence from ChatGPT usage on pair programming, and consequently on student collaboration. The findings suggest that learning objectives should be formulated in more detail, to highlight essential programming skills, and be expanded to include the use of AI tools. Complex programming assignments remain appropriate in programming education, but pair programming as a didactic approach should be reconsidered in l…
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Over-reliance on GenAI tools may lead to dependency, reducing opportunities to cultivate essential skills such as algorithmic thinking, debugging, and independent problem-solving.
“Although these tools can support students in writing and analyzing code, over-reliance on them may lead to dependency, reducing opportunities to cultivate essential skills such as algorithmic thinking, debugging, and independent problem-solving (Bull & Kharrufa, 2024;Groothuijsen et al., 2024).”
From Integrating Generative AI into Programming Education: Student Perceptions and the Challenge of Correcting AI Errors (2025) · cites this paper as Groothuijsen et al., 2024
Students often found that AI-generated fixes were incomplete or unreliable, requiring manual correction and deeper understanding.
“However, students often found that AI-generated fixes were incomplete or unreliable, requiring manual correction and deeper understanding.”
From Integrating Generative AI into Programming Education: Student Perceptions and the Challenge of Correcting AI Errors (2025) · cites this paper as Groothuijsen et al. (2024)
Two distinct patterns emerged: students either asked ChatGPT to guide their own debugging process or used it to identify errors directly.
“Two distinct patterns emerged: students either asked ChatGPT to guide their own debugging process or used it to identify errors directly.”
From Integrating Generative AI into Programming Education: Student Perceptions and the Challenge of Correcting AI Errors (2025) · cites this paper as Groothuijsen et al. (2024)
Groothuijsen et al. (2024) found that students used ChatGPT for code explanation, initial code generation, and debugging support.
“Groothuijsen et al. (2024) found that students used ChatGPT for code explanation, initial code generation, and debugging support.”
From Integrating Generative AI into Programming Education: Student Perceptions and the Challenge of Correcting AI Errors (2025) · cites this paper as Groothuijsen et al. (2024)
When students lean too heavily on AI-generated code, their ability to develop core competencies such as logical reasoning, collaboration, and debugging may be compromised.
“When students lean too heavily on AI-generated code, their ability to develop core competencies -such as logical reasoning, collaboration, and debugging -may be compromised (Groothuijsen et al., 2024;Manorat et al., 2025;Tedre et al., 2021).”
From Integrating Generative AI into Programming Education: Student Perceptions and the Challenge of Correcting AI Errors (2025) · cites this paper as Groothuijsen et al., 2024
Such tools may produce insecure or suboptimal code, requiring users to review and revise outputs carefully to avoid compounding mistakes.
“Research has shown that such tools may produce insecure or suboptimal code, requiring users to review and revise outputs carefully to avoid compounding mistakes (Groothuijsen et al., 2024;Laux, 2024;Nguyen & Nadi, 2022).”
From Integrating Generative AI into Programming Education: Student Perceptions and the Challenge of Correcting AI Errors (2025) · cites this paper as Groothuijsen et al., 2024
The same study reported concerns from instructors about decreased code quality and reduced student collaboration, particularly in pair programming.
“While these tools promoted efficiency and understanding, the same study reported concerns from instructors about decreased code quality and reduced student collaboration, particularly in pair programming.”
From Integrating Generative AI into Programming Education: Student Perceptions and the Challenge of Correcting AI Errors (2025) · cites this paper as Groothuijsen et al. (2024)
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Pedagogical Applications of Generative AI in Higher Education: A Systematic Review of the Field
Qian, Y. (2025) · TechTrends · doi:10.1007/s11528-025-01100-1 · Cited by 206 · see citing works
“Computer Science’s representation (Allen et al., 2024; Groothuijsen et al., 2024; Song et al., 2024; Yilmaz and Yilmaz, 2023) aligns with previous findings by Zawacki-Richter et al. (2019) on pre-GenAI programs, reflecting the discipline’s natural synergy with AI capabilities in programming, data…”
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“…student perspective, GenAI tools are often perceived as helpful learning companions, offering fast, responsive explanations and creative coding suggestions that reduce barriers to experimentation and foster exploratory learning (Groothuijsen et al., 2024; Marrone et al., 2022; Zviel-Girshin, 2024).”
InfluentialZhang, L., Jiang, Q., Xiong, W., & Zhao, W. (2025) · Journal of Educational Computing Research · doi:10.1177/07356331251333874 · Cited by 15 · see citing works
Mapeamento Sistemático sobre o Uso de IAs Generativas no Ensino de Programação
Ferreira de Sousa, J., & Silveira, F. R. D. V. (2026) · Revista Eletrônica de Iniciação Científica em Computação · doi:10.5753/reic.2026.7169
- Authors
- Groothuijsen, S., van den Beemt, A., Remmers, J. C., & van Meeuwen, L. W.
- Affiliations
- Eindhoven University of Technology (NL)
- Corresponding author
- Groothuijsen, Suzanne
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2024-12
- Volume
- 7
- Pages
- 100290
- ISSN
- 2666-920X (ISSN-L)
- OpenAlex
- W4402125515
- Cited by
- 92
- References
- 12
- Citations by year
- 2026: 50 · 2025: 38 · 2024: 4
- FWCI
- 34.59
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc-nd)
- TU/e Research Portal (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Eindhoven University of Technology
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Keywords
- AI chatbots, ChatGPT, programming education, scientific computing, engineering education, pair programming, student use of AI tools, code quality, conceptual understanding, learning outcomes, learning objectives, higher education
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