From "Ban It Till We Understand It" to "Resistance is Futile": How University Programming Instructors Plan to Adapt as More Students Use AI Code Generation and Explanation Tools such as ChatGPT and GitHub Copilot
Open accessAbstract
Over the past year (2022–2023), recently-released AI tools such as ChatGPT and GitHub Copilot have gained significant attention from computing educators. Both researchers and practitioners have discovered that these tools can generate correct solutions to a variety of introductory programming assignments and accurately explain the contents of code. Given their current capabilities and likely advances in the coming years, how do university instructors plan to adapt their courses to ensure that students still learn well? To gather a diverse sample of perspectives, we interviewed 20 introductory programming instructors (9 women + 11 men) across 9 countries (Australia, Botswana, Canada, Chile, China, Rwanda, Spain, Switzerland, United States) spanning all 6 populated continents. To our knowledge, this is the first empirical study to gather instructor perspectives about how they plan to adapt to these AI coding tools that more students will likely have access to in the future. We found that, in the short-term, many planned to take immediate measures to discourage AI-assisted cheating. Then opinions diverged about how to work with AI coding tools longer-term, with one side wanting to ban them and continue teaching programming fundamentals, and the other side wanting to integrate them into courses to prepare students for future jobs. Our study findings capture a rare snapshot in time in early 2023 as computing instructors are just starting to form opinions about this fast-growing phenomenon but have not yet converged to any consensus about best practices. Using these findings as inspiration, we synthesized a diverse set of open research questions regarding how to develop, deploy, and evaluate AI coding tools for computing education.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Even educators with a positive sentiment towards GenAI have begun implementing restrictions and bans in the classroom.
“In response, even educators with a positive sentiment towards GenAI have begun implementing restrictions and bans in the classroom.”
From How Do Programming Students Use Generative AI? (2025) · cites this paper as Lau and Guo 2023
Two major approaches appear to emerge: embracing GenAI as a tool for teaching and core software development, or preventing and restricting its use.
“Two major approaches appear to emerge from this development: either a) embracing GenAI as a tool not only for teaching but also as a core software development tool, or b) preventing and restricting its use [Lau and Guo 2023].”
From How Do Programming Students Use Generative AI? (2025) · cites this paper as Lau and Guo 2023
In the long term, instructors are divided between resisting GenAI to maintain traditional programming education and integrating it to prepare students for future industry demands.
“In the long term, instructors are divided between resisting GenAI to maintain traditional programming education and integrating it to prepare students for future industry demands [8].”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [8]
Opinions on how best to integrate GenAI are divided.
“Opinions on how best to integrate GenAI are divided.”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [8]
University instructors have concerns about GenAI-assisted cheating in the short term, prompting reactive measures such as bans and grading adjustments.
“Lau and Guo [8] showed that university instructors have concerns about GenAI-assisted cheating in the short term, prompting reactive measures such as bans and grading adjustments.”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [8]
Introducing AI assistants in pair programming could mitigate several challenges associated with human-human pairing.
“Introducing AI assistants in pair programming could mitigate several challenges associated with human–human pairing (Lau & Guo, 2023).”
Potential resistance from educators and students unfamiliar with AI tools or skeptical about their effectiveness is another barrier.
“Another barrier is the potential resistance from educators and students who are unfamiliar with AI tools or skeptical about their effectiveness (Lau & Guo, 2023).”
Three papers report interviews with students and instructors finding no consensus about the use of LLMs in higher education, its benefits or risks, but general awareness of the problem of academic integrity.
“Three other papers report on interviews with students and instructors about their experiences with, and attitudes towards LLMs [121,164,198], finding that there is no consensus about the use of LLMs in higher education, its benefits or risks, but there is general awareness of the problem of academic integrity in the light of LLMs being used by students.”
From The Robots Are Here: Navigating the Generative AI Revolution in Computing Education (2023) · cites this paper as [121]
Generative AI is reported to perform very well in assessments commonly used in introductory courses, raising concerns that students will submit solutions they have not created themselves.
“Generative AI is reported to perform very well in assessments that are commonly used in introductory courses, raising concerns that students will submit solutions that they have not created themselves [33,70,85,86,121,152,169,176,177].”
From The Robots Are Here: Navigating the Generative AI Revolution in Computing Education (2023) · cites this paper as [121]
The present work differs from Lau and Guo [121] in that it includes educators with concrete plans or views toward changing their course structure, assessment, or classroom practices in light of LLMs.
“This is one of the main ways the present work differs from that of Lau and Guo [121] discussed in Section 3.1.”
From The Robots Are Here: Navigating the Generative AI Revolution in Computing Education (2023) · cites this paper as [121]
Some of these findings support those of Lau and Guo, notably the increased emphasis on invigilated exams, oral exams, and process-based assessments.
“It is worth noting that some of these findings support those of Lau and Guo [121], notably the increased emphasis on invigilated exams, oral exams, and process-based assessments.”
From The Robots Are Here: Navigating the Generative AI Revolution in Computing Education (2023) · cites this paper as [121]
Cites
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445922 · Cited by 6,988 · see citing works
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208 · see citing works
Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts
Zamfirescu-Pereira, J. D., Wong, R. Y., Hartmann, B., & Yang, Q. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581388 · Cited by 893 · cited 1× · see citing works
“• Learning curve: Novices may have a hard time producing high-quality results with simple prompts [115].”
BackgroundVaithilingam, P., Zhang, T., & Glassman, E. L. (2022) · CHI Conference on Human Factors in Computing Systems Extended Abstracts · doi:10.1145/3491101.3519665 · Cited by 660 · see citing works
The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming
Finnie-Ansley, J., Denny, P., Becker, B. A., Luxton-Reilly, A., & Prather, J. (2022) · Proceedings of the 24th Australasian Computing Education Conference · doi:10.1145/3511861.3511863 · Cited by 463 · see citing works
Grounded Copilot: How Programmers Interact with Code-Generating Models
Barke, S., James, M. B., & Polikarpova, N. (2023) · Proceedings of the ACM on Programming Languages · doi:10.1145/3586030 · Cited by 436 · cited 1× · see citing works
“found that developers used it in two ways: to help them explore options and to accelerate their path toward a known goal [13].”
BackgroundStudying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming
Kazemitabaar, M., Chow, J., Ma, C. K. T., Ericson, B. J., Weintrop, D., & Grossman, T. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580919 · Cited by 366 · cited 1× · see citing works
“ran a controlled study with 69 pre-college students where half used Codex to learn Python and the other half did not [53].”
BackgroundMirowski, P., Mathewson, K. W., Pittman, J., & Evans, R. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581225 · Cited by 240 · cited 1× · see citing works
“, creative writing [77]), and broader societal implications of LLMs [17].”
BackgroundInvestigating Explainability of Generative AI for Code through Scenario-based Design
Sun, J., Liao, Q. V., Muller, M., Agarwal, M., Houde, S., Talamadupula, K., & Weisz, J. D. (2022) · 27th International Conference on Intelligent User Interfaces · doi:10.1145/3490099.3511119 · Cited by 219 · see citing works
Using GitHub Copilot to Solve Simple Programming Problems
Wermelinger, M. (2023) · Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 · doi:10.1145/3545945.3569830 · Cited by 191 · see citing works
Taking Flight with Copilot: Early insights and opportunities of AI-powered pair-programming tools
Bird, C., Ford, D., Zimmermann, T., Forsgren, N., Kalliamvakou, E., Lowdermilk, T., & Gazit, I. (2022) · Queue · doi:10.1145/3582083 · Cited by 158 · see citing works
Grounded Copilot: How Programmers Interact with Code-Generating Models
Barke, S., James, M. B., & Polikarpova, N. (2022) · arXiv · doi:10.48550/arxiv.2206.15000 · Cited by 23 · cited 1× · see citing works
“found that developers used it in two ways: to help them explore options and to accelerate their path toward a known goal [13].”
Background"It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers
Prather, J., Reeves, B. N., Denny, P., Becker, B. A., Leinonen, J., Luxton-Reilly, A., Powell, G., Finnie-Ansley, J., & Santos, E. A. (2023) · arXiv · cited 1×
“observed how students used Copilot on CS1 assignments in a lab study and then interviewed them about their first impressions [92].”
Background
Flagged first, then by citations
Cited by
Sorted by citations
The Robots Are Here: Navigating the Generative AI Revolution in Computing Education
Prather, J., Denny, P., Leinonen, J., Becker, B. A., Albluwi, I., Craig, M., Keuning, H., Kiesler, N., Kohn, T., Luxton-Reilly, A., et al. (2023) · Proceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education · doi:10.1145/3623762.3633499 · Cited by 396 · see citing works
Computing Education in the Era of Generative AI
Denny, P., Prather, J., Becker, B. A., Finnie-Ansley, J., Hellas, A., Leinonen, J., Luxton-Reilly, A., Reeves, B. N., Santos, E. A., & Sarsa, S. (2024) · Communications of the ACM · doi:10.1145/3624720 · Cited by 316 · see citing works
“For a more technical overview of the historical developments and future trends of language models, we direct the reader to the CACM article by Li [20].”
MethodGenerative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda
Bittle, K., & El-Gayar, O. (2025) · Information · doi:10.3390/info16040296 · Cited by 305 · see citing works
“In the long term, instructors are divided between resisting GenAI to maintain traditional programming education and integrating it to prepare students for future industry demands [8].”
The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers
Prather, J., Reeves, B. N., Leinonen, J., MacNeil, S., Randrianasolo, A. S., Becker, B. A., Kimmel, B., Wright, J., & Briggs, B. (2024) · Proceedings of the 2024 ACM Conference on International Computing Education Research - Volume 1 · doi:10.1145/3632620.3671116 · Cited by 196 · see citing works
“This has raised concerns from educators about potential student over-reliance [6, 29, 61, 72].”
Prompt Problems: A New Programming Exercise for the Generative AI Era
Denny, P., Leinonen, J., Prather, J., Luxton-Reilly, A., Amarouche, T., Becker, B. A., & Reeves, B. N. (2024) · Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 · doi:10.1145/3626252.3630909 · Cited by 187 · see citing works
Generative AI in Computing Education: Perspectives of Students and Instructors
Zastudil, C., Rogalska, M., Kapp, C., Vaughn, J., & MacNeil, S. (2023) · 2023 IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie58773.2023.10343467 · Cited by 136 · see citing works
Fan, G., Liu, D., Zhang, R., & Pan, L. (2025) · International Journal of STEM Education · doi:10.1186/s40594-025-00537-3 · Cited by 109 · see citing works
“Further research is needed to examine the effectiveness of AI-assisted programming across diverse student populations and educational settings (Lau & Guo, 2023).”
InfluentialAI Literacy in K-12 and Higher Education in the Wake of Generative AI: An Integrative Review
Gu, X., & Ericson, B. J. (2025) · arXiv · doi:10.48550/arxiv.2503.00079 · Cited by 73
Boguslawski, S., Deer, R., & Dawson, M. G. (2025) · Information and Learning Sciences · doi:10.1108/ils-10-2023-0163 · Cited by 66 · see citing works
Charting the Future of AI in Project-Based Learning: A Co-Design Exploration with Students
Zheng, C., Yuan, K., Guo, B., Hadi Mogavi, R., Peng, Z., Ma, S., & Ma, X. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642807 · Cited by 70 · see citing works
Lyu, W., Zhang, S., Chung, T., Sun, Y., & Zhang, Y. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100383 · Cited by 68 · see citing works
“Furthermore, much of this work remains confined to single disciplines [10, 70], leaving a gap in understanding how instructors across a broad range of fields relate to GenAI.”
Kohen-Vacs, D., Usher, M., & Jansen, M. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-025-00496-4 · Cited by 39 · see citing works
“…discussion about their role in education and the workplace, offering opportunities to enhance efficiency and creativity while raising questions about their impact on foundational coding skills and traditional teaching methodologies (Jacques, 2023; Lau & Guo, 2023; Manorat et al., 2025).”
Jordan, M., Ly, K., & Soosai Raj, A. G. (2024) · Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 · doi:10.1145/3626252.3630897 · Cited by 39 · see citing works
How Do Programming Students Use Generative AI?
Rahe, C., & Maalej, W. (2025) · Proceedings of the ACM on Software Engineering · doi:10.1145/3715762 · Cited by 45 · see citing works
“Two major approaches appear to emerge from this development: either a) embracing GenAI as a tool not only for teaching but also as a core software development tool, or b) preventing and restricting its use [Lau and Guo 2023].”
InfluentialStyve, A., Virkki, O. T., & Naeem, U. (2024) · 2024 IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon60312.2024.10578746 · Cited by 32 · see citing works
Recommendations to Create Programming Exercises to Overcome ChatGPT
Berrezueta-Guzman, J., & Krusche, S. (2023) · 2023 IEEE 35th International Conference on Software Engineering Education and Training (CSEE&T) · doi:10.1109/cseet58097.2023.00031 · Cited by 27 · see citing works
Examining Student and Teacher Perspectives on Undisclosed Use of Generative AI in Academic Work
Adnin, R., Pandkar, A., Yao, B., Wang, D., & Das, M. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713393 · Cited by 16 · see citing works
Analysis of Generative AI Policies in Computing Course Syllabi
Ali, A., Collier, A. H., Dewan, U., McDonald, N., & Johri, A. (2025) · Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 1 · doi:10.1145/3641554.3701823 · Cited by 14 · see citing works
Chang, C. I., Choi, W. C., & Choi, I. C. (2024) · Proceedings of the 2024 7th Artificial Intelligence and Cloud Computing Conference · doi:10.1145/3719384.3719439 · Cited by 11 · see citing works
“Lau and Guo [9] highlighted the need for a substantial overhaul of the types of questions and tasks assigned to students.”
InfluentialRecommendations to Create Programming Exercises to Overcome ChatGPT
Berrezueta-Guzman, J., & Krusche, S. (2023) · doi:10.35542/osf.io/expuq · Cited by 27 · see citing works
Navigating the Ethical and Societal Impacts of Generative AI in Higher Computing Education
Mak, J., Nakatumba-Nabende, J., Clear, T., Clear, A., Abluwi, I., Andrei, O., Angeli, L., MacNeil, S., Oyelere, S. S., Hale Rattigan, M., et al. (2025) · Proceedings of the 2025 Working Group Reports on Innovation and Technology in Computer Science Education · doi:10.1145/3760545.3783975 · Cited by 12 · see citing works
The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers
Prather, J., Reeves, B., Leinonen, J., MacNeil, S., Randrianasolo, A. S., Becker, B., Kimmel, B., Wright, J., & Briggs, B. (2024) · arXiv · Cited by 196 · see citing works
“This has raised concerns from educators about potential student over-reliance [6, 29, 61, 72].”
A survey of American public health faculty AI competency & readiness
Lord, E., Heboyan, V., Huang, H., Tracy, D., & De Leo, G. (2026) · Cogent Education · doi:10.1080/2331186x.2026.2654902 · Cited by 1 · see citing works
A Scaffolded GenAI Lab in Early Undergraduate CS: A Mixed-Methods, Multi-Course Evaluation
Dickey, E., Bejarano, A., Kuperus, R., & Fagundes, B. (2025) · arXiv
“Accordingly, GenAI literacy for programming also includes managing uncertainty and making principled decisions about when not to use GenAI, aligning tool use with course norms and academic integrity expectations [15].”
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
- Lau, S., & Guo, P.
- Affiliations
- University of California San Diego (US)
- ORCID
- Venue
- Proceedings of the 2023 ACM Conference on International Computing Education Research V.1
- Publisher
- ACM
- Published
- 2023-08-07
- Pages
- 106-121
- OpenAlex
- W4386584937
- Cited by
- 241
- References
- 13
- Citations by year
- 2026: 56 · 2025: 102 · 2024: 74 · 2023: 9
- FWCI
- 72.88
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Conference on International Computing Education Research (ICER) (publishedVersion, cc-by)
- Funders
- National Science Foundation (IIS-1845900)
- 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-assisted code generation, ChatGPT, GitHub Copilot, introductory programming, teacher perceptions, AI-assisted cheating, course adaptation, programming 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 ›
Are you an author, or do you see a problem with this page? Request a review
