Generative artificial intelligence assistants in software development education: A vision for integrating generative artificial intelligence into educational practice, not instinctively defending against it
Abstract
The use of Generative AI in software development is gaining traction. But what are the potentials and implications on software development education? We gathered insights on the use of Generative AI from professional software developers and make some pedagogical recommendations.
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As LLMs increasingly automate routine and syntactic coding tasks, educators face the challenge of preserving essential programming competencies such as algorithmic thinking, debugging, and independent problem-solving.
“As LLMs increasingly automate routine and syntactic coding tasks, educators face the challenge of preserving essential programming competencies, such as algorithmic thinking, debugging, and independent problem-solving (Valový & Buchalcevova, 2023;Bull & Kharrufa, 2024;French et al., 2023).”
From Integrating Generative AI into Programming Education: Student Perceptions and the Challenge of Correcting AI Errors (2025) · cites this paper as Bull & Kharrufa, 2024
Students who rely too heavily on automated outputs may struggle when confronted with unfamiliar programming problems that demand creative reasoning and in-depth debugging.
“While LLMs support learners through code generation and suggestions, students who rely too heavily on automated outputs may struggle when confronted with unfamiliar programming problems that demand creative reasoning and in-depth debugging (Bull & Kharrufa, 2024).”
From Integrating Generative AI into Programming Education: Student Perceptions and the Challenge of Correcting AI Errors (2025) · cites this paper as Bull & Kharrufa, 2024
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 Bull & Kharrufa, 2024
Resource requirements for incorporating advanced AI tools (financial costs, infrastructure) are a major challenge to widespread adoption.
“One major challenge is the resource requirement for incorporating advanced AI tools, which may include financial costs for software licenses or subscriptions, as well as the need for robust technological infrastructure (Bull & Kharrufa, 2024).”
From The impact of AI-assisted pair programming on student motivation, programming anxiety, collaborative learning, and programming performance: a comparative study with traditional pair programming and individual approaches (2025) · cites this paper as Bull & Kharrufa, 2024
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- Authors
- Bull, C., & Kharrufa, A.
- Affiliations
- Newcastle University (GB)
- Venue
- IEEE Software
- Published
- 2024-03
- Volume
- 41
- Issue
- 2
- Pages
- 52-59
- ISSN
- 0740-7459 (ISSN-L)
- 1937-4194
- OpenAlex
- W4385656600
- Cited by
- 82
- References
- 13
- Citations by year
- 2026: 16 · 2025: 40 · 2024: 24 · 2023: 2
- FWCI
- 19.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Software (publishedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/T022582/1)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Keywords
- generative AI, software engineering education, AI assistants, pedagogical recommendations, artificial intelligence in education
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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