The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Generative AI in Computing Education: Perspectives of Students and Instructors
Abstract
Generative models are now capable of producing natural language text that is, in some cases, comparable in quality to the text produced by people. In the computing education context, these models are being used to generate code, code explanations, and programming exercises. The rapid adoption of these models has prompted multiple position papers and workshops which discuss the implications of these models for computing education, both positive and negative. This paper presents results from a series of semi-structured interviews with 12 students and 6 instructors about their awareness, experiences, and preferences regarding the use of tools powered by generative AI in computing classrooms. The results suggest that Generative AI (GAI) tools will play an increasingly significant role in computing education. However, students and instructors also raised numerous concerns about how these models should be integrated to best support the needs and learning goals of students. We also identified interesting tensions and alignments that emerged between how instructors and students prefer to engage with these models. We discuss these results and provide recommendations related to curriculum development, assessment methods, and pedagogical practice. As GAI tools become increasingly prevalent, it's important to understand educational stakeholders' preferences and values to ensure that these tools can be used for good and that potential harms can be mitigated.
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In the citing paper
“A recent interview study with individual students revealed interest in using the technology for purposes like generating supplementary learning materials, but also skipping coursework they don't find engaging enough [Zastudil et al. 2023].”
From How Do Programming Students Use Generative AI? (2025) · cites this paper as Zastudil et al. 2023
In the citing paper
“They may have thought it to be more socially acceptable to report slightly lower than true ChatGPT usage numbers due to the perception of such activity as engaging in cheating [Zastudil et al. 2023].”
From How Do Programming Students Use Generative AI? (2025) · cites this paper as Zastudil et al. 2023
In the citing paper
“However, as the results are fairly consistent with similar surveys around the time [Kazemitabaar et al. 2023; Prather et al. 2023a; Zastudil et al. 2023] and we were commonly able to observe behavior that could be considered unethical in an academic context [Prather et al. 2023a], we do not believe this to be a major limitation.”
From How Do Programming Students Use Generative AI? (2025) · cites this paper as Zastudil et al. 2023
In the citing paper
“This study further reveals that governance configurations emphasizing academic integrity and evidence requirements demonstrated more pronounced effects in promoting ethical internalization, yet did not necessarily lead to synchronous reductions in anxiety (Zastudil et al., 2023).”
From Multidimensional responses to generative AI in higher education: an integrative framework linking institutional governance, teaching practice, and student psychological adaptation (2026) · cites this paper as Zastudil et al., 2023
In the citing paper
“These technologies could inadvertently lead to academic laziness and overreliance if not carefully managed [11,14].”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [14]
In the citing paper
“The advantages are opportunities for higher levels of learning outcomes, personalized learning, timely feedback, and alternative assistance [155, 357, 374].”
From Navigating the Ethical and Societal Impacts of Generative AI in Higher Computing Education (2025) · cites this paper as [374]
In the citing paper
“A study involving interviews of instructors and students by Zastudil et al. [374] found that all instructors expressed concerns about students using GenAI tools inappropriately”
From Navigating the Ethical and Societal Impacts of Generative AI in Higher Computing Education (2025) · cites this paper as [374]
In the citing paper
“while students reported their willingness to use GenAI to cheat on an assignment when they don't see the value of the assignment.”
From Navigating the Ethical and Societal Impacts of Generative AI in Higher Computing Education (2025) · cites this paper as [374]
In the citing paper
“Students said that the AI tools reduced the effort of writing code, helped them to find learning materials, and allowed them to avoid busy work.”
From Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study (2024) · cites this paper as [39]
In the citing paper
“Zastudil et al. [39] interviewed 12 students and six instructors, all from the same institution, investigating experiences and preferences for AI tools in computing classrooms.”
From Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study (2024) · cites this paper as [39]
In the citing paper
“Assessment was the area where interviewees perceived the most threats, which has also been found in other studies [19,39].”
From Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study (2024) · cites this paper as [39]
In the citing paper
“Instructors saw benefits of the AI tools in providing explanations of code, helping students to find inspiration and to get feedback, and allowing access to high quality learning resources.”
From Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study (2024) · cites this paper as [39]
In the citing paper
“The instructors, too, expressed concerns about over-reliance and plagiarism, and pointed out issues with the trustworthiness of the systems.”
From Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study (2024) · cites this paper as [39]
In the citing paper
“They also voiced concerns about over-reliance on and trustworthiness of the AI tools, and noted that the availability of such tools might increase plagiarism.”
From Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study (2024) · cites this paper as [39]
In the citing paper
“All interviewees expressed concerns about students missing out on learning by using AI tools, a similar finding to the study by Zastudil et al. [39].”
From Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study (2024) · cites this paper as [39]
In the citing paper
“The initial focus of discussion among computing departments has been concerns about academic integrity among students, as well as the trivializing of many of the assignments in our introductory courses, which is reflected both in our interviews and in prior studies [19,39].”
From Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study (2024) · cites this paper as [39]
In the citing paper
“Zastudil et al. [20] interviewed computer science students and instructors to understand their interaction with GenAI and their expectations regarding its use in education.They observed a positive attitude with regard to integrating GenAI into the educational process as well as concerns about over-reliance, trustworthiness and academic integrity.”
From Incorporating Generative AI into Software Development Education (2024) · cites this paper as [20]
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Metadata
- Authors
- Zastudil, C., Rogalska, M., Kapp, C., Vaughn, J., & MacNeil, S.
- Affiliations
- Temple University (US)
- Venue
- 2023 IEEE Frontiers in Education Conference (FIE)
- Publisher
- IEEE
- Published
- 2023-10-18
- Pages
- 1-9
- ISSN
- 0190-5848 (ISSN-L)
- OpenAlex
- W4390621457
- Cited by
- 136
- References
- 47
- Citations by year
- 2026: 32 · 2025: 59 · 2024: 41 · 2023: 4
- FWCI
- 41.13
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Frontiers in Education Conference (FIE) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- generative AI, computing education, student perceptions, teacher perceptions, code generation, programming education, AI integration in classrooms, curriculum development, assessment methods, teaching practice, educational technology adoption
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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