The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Computing Education in the Era of Generative AI
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Challenges and opportunities faced by computing educators and students adapting to LLMs capable of generating accurate source code from natural-language problem descriptions.
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“Researchers such as Denny et al. [4], Khalil and Er [10], and Susnjak [13] also address the limitations of current plagiarism detection tools, which fail to recognize the original essays produced by ChatGPT, indicating a need for advancements in detection technologies and updated classroom policies.”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [4]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Several authors lament the current state of popular AI detection tools, such as iThenticate, MOSS, and Turnitin, stating they are inadequate for the needs of educators [4,9,10].”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [4]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Generative AI, such as GPT-4o CoPilot and OpenAI's GPT-4, enables the generation of sophisticated code from natural-language prompts, raising concerns about student overreliance and potential academic misconduct. This issue is reported by Denny et al. [4].”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [4]
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In the citing paper
“Generative AI, such as GPT-4o CoPilot and OpenAI's GPT-4, enables the generation of sophisticated code from natural-language prompts, raising concerns about student overreliance and potential academic misconduct. This issue is reported by Denny et al. [4].”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [4]
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“This model helps reconcile the “performance–learning paradox” noted in recent empirical and review work (Yan et al., 2025; Denny et al., 2024).”
From Tool, tutor, or crutch?: A grounded theory of cognitive scaffolding and offloading in AI-assisted programming education (2026) · cites this paper as Denny et al., 2024
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Citations
Cites
On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676 · cited 2×
“For example, in a deep discussion of LLMs, Bommasani et al. highlight that it will become “much more complex for teachers to understand the extent of a student’s contribution” and to “regulate ineffective collaborations and detect plagiarism” [7].”
BackgroundEvaluating Large Language Models Trained on Code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. D. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al. (2021) · arXiv · doi:10.48550/arxiv.2107.03374 · Cited by 1,436 · cited 2×
“Can a student with no programming knowledge, but who is armed with a code-generating LLM, pass typical programming assessments?”
BackgroundCompetition-level code generation with AlphaCode
Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Dal Lago, A., et al. (2022) · Science · doi:10.1126/science.abq1158 · Cited by 1,043 · cited 4×
“Similarly, the developers of AlphaCode suggest such tools have “the potential for a positive, transformative impact on society, with a wide range of applications including computer science education” [21].”
BackgroundInfluentialPrompt Programming for Large Language Models: Beyond the Few-Shot Paradigm
Reynolds, L., & McDonell, K. (2021) · Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411763.3451760 · Cited by 952 · cited 1×
“It is well known that the outputs produced by large language models are very sensitive to their inputs [32].”
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 · cited 1×
“Depending on the prompt, the resulting programs used varied programmatic structures, while ultimately favoring expected methods for each problem variation (i.e., for-loops for processing lists, and while-loops for processing standard input).”
BackgroundAutomatic Generation of Programming Exercises and Code Explanations Using Large Language Models
Sarsa, S., Denny, P., Hellas, A., & Leinonen, J. (2022) · Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 1 · doi:10.1145/3501385.3543957 · Cited by 497
Self-citationAsleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions
Pearce, H., Ahmad, B., Tan, B., Dolan-Gavitt, B., & Karri, R. (2022) · 2022 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp46214.2022.9833571 · Cited by 476
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 · cited 6×
“This can facilitate discussions about different approaches and the quality of solutions, and provide opportunities for refactoring exercises [15].”
BackgroundResultInfluentialSelf-citationGrounded 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×
“Other recent work has shown that developers are more successful working with Copilot when they decompose larger programming statements into smaller tasks and then explicitly prompt Copilot for each of the subtasks [1, 18].”
BackgroundThe 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 · cited 1×
“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].”
MethodSelf-citationStudying 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 2×
“Depending on the prompt, the resulting programs used varied programmatic structures, while ultimately favoring expected methods for each problem variation (i.e., for-loops for processing lists, and while-loops for processing standard input).”
BackgroundThe Impact of AI on Developer Productivity: Evidence from GitHub Copilot
Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023) · arXiv · doi:10.48550/arxiv.2302.06590 · Cited by 287 · cited 1×
“…source code automatically from natural language descriptions of problems is likely to greatly improve the productivity of professional developers [28], and is being actively explored by well-funded entities such as OpenAI (ChatGPT, Codex, GPT-x ), Amazon (CodeWhisperer), and Google (AlphaCode,…”
BackgroundDenny, P., Kumar, V., & Giacaman, N. (2023) · Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 · doi:10.1145/3545945.3569823 · Cited by 287 · cited 3×
“Thus, AI-generated solutions can be a time-efficient option [11].”
BackgroundInfluentialSelf-citationDo Users Write More Insecure Code with AI Assistants?
Perry, N., Srivastava, M., Kumar, D., & Boneh, D. (2023) · Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3576915.3623157 · Cited by 259 · cited 1×
“Perry et al. recently examined whether novices using AI code generation tools wrote more secure code, finding that novices consistently wrote insecure code with specific high vulnerabilities in string encryption and SQL injection [29].”
BackgroundPrather, J., Reeves, B. N., Denny, P., Becker, B. A., Leinonen, J., Luxton-Reilly, A., Powell, G., Finnie-Ansley, J., & Santos, E. A. (2024) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3617367 · Cited by 256 · cited 2×
“Novices may be intimidated by such code generation [19] or may spend too much time reading code that does not further their goals [31].”
BackgroundSelf-citationLau, S., & Guo, P. (2023) · Proceedings of the 2023 ACM Conference on International Computing Education Research V.1 · doi:10.1145/3568813.3600138 · Cited by 241 · cited 1×
“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].”
MethodUsing Large Language Models to Enhance Programming Error Messages
Leinonen, J., Hellas, A., Sarsa, S., Reeves, B., Denny, P., Prather, J., & Becker, B. A. (2023) · Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 · doi:10.1145/3545945.3569770 · Cited by 235 · cited 1×
“Leinonen et al. recently took a different approach and used LLMs to try to improve PEMs [21].”
MethodSelf-citationComparing Code Explanations Created by Students and Large Language Models
Leinonen, J., Denny, P., MacNeil, S., Sarsa, S., Bernstein, S., Kim, J., Tran, A., & Hellas, A. (2023) · Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1 · doi:10.1145/3587102.3588785 · Cited by 210 · cited 1×
“Recent work has also found that LLM-created code explanations are rated more highly on average by students than code explanations created by their peers [20].”
BackgroundSelf-citationKazemitabaar, M., Hou, X., Henley, A. Z., Ericson, B. J., Weintrop, D., & Grossman, T. (2023) · Koli Calling International Conference on Computing Education Research · doi:10.1145/3631802.3631806 · Cited by 202 · cited 2×
“Students who have become overly reliant on model outputs may find it especially challenging to proceed when the suggested code is incorrect and cannot be resolved through natural language prompting [15].”
BackgroundMacNeil, S., Tran, A., Hellas, A., Kim, J., Sarsa, S., Denny, P., Bernstein, S., & Leinonen, J. (2023) · Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 · doi:10.1145/3545945.3569785 · Cited by 195 · cited 1×
“This follows a similar evaluation approach employed by the Codex developers [5].”
MethodSelf-citationCodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes
Liffiton, M., Sheese, B. E., Savelka, J., & Denny, P. (2023) · Proceedings of the 23rd Koli Calling International Conference on Computing Education Research · doi:10.1145/3631802.3631830 · Cited by 192 · cited 1×
“Depending on the prompt, the resulting programs used varied programmatic structures, while ultimately favoring expected methods for each problem variation (i.e., for-loops for processing lists, and while-loops for processing standard input).”
BackgroundSelf-citationPrompt 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 · cited 1×
“Depending on the prompt, the resulting programs used varied programmatic structures, while ultimately favoring expected methods for each problem variation (i.e., for-loops for processing lists, and while-loops for processing standard input).”
BackgroundSelf-citationMy AI Wants to Know if This Will Be on the Exam: Testing OpenAI’s Codex on CS2 Programming Exercises
Finnie-Ansley, J., Denny, P., Luxton-Reilly, A., Santos, E. A., Prather, J., & Becker, B. A. (2023) · Proceedings of the 25th Australasian Computing Education Conference · doi:10.1145/3576123.3576134 · Cited by 186 · cited 2×
“Often, all that is required of a student is to accept an auto-generated suggestion by an IDE plugin [10, 11].”
BackgroundSelf-citationMetacognition and Self-Regulation in Programming Education: Theories and Exemplars of Use
Loksa, D., Margulieux, L., Becker, B. A., Craig, M., Denny, P., Pettit, R., & Prather, J. (2022) · ACM Transactions on Computing Education · doi:10.1145/3487050 · Cited by 140 · cited 1×
“Developing computational thinking skills is important for novice programmers as it can foster higher-order thinking and reflection skills [24].”
BackgroundSelf-citationWelsh, M. (2022) · Communications of the ACM · doi:10.1145/3570220 · Cited by 110 · cited 1×
“Nevertheless, individual work is still commonplace in computing courses, and it is an expectation for students working on individual projects to produce their own code rather than copying code written by someone else.”
BackgroundA Review of Peer Code Review in Higher Education
Indriasari, T. D., Luxton-Reilly, A., & Denny, P. C. (2020) · ACM Transactions on Computing Education · doi:10.1145/3403935 · Cited by 80
Self-citationJiang, E., Toh, E., Molina, A., Olson, K., Kayacik, C., Donsbach, A., Cai, C. J., & Terry, M. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501870 · Cited by 79 · cited 1×
“Other recent work has shown that developers are more successful working with Copilot when they decompose larger programming statements into smaller tasks and then explicitly prompt Copilot for each of the subtasks [1, 18].”
BackgroundNegotiating the Maze of Academic Integrity in Computing Education
Simon, Sheard, J., Morgan, M., Petersen, A., Settle, A., Sinclair, J., Cross, G., & Riedesel, C. (2016) · Proceedings of the 2016 ITiCSE Working Group Reports · doi:10.1145/3024906.3024910 · Cited by 76 · cited 1×
“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].”
MethodProcess oriented guided inquiry learning (POGIL) for computer science
Kussmaul, C. (2012) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/2157136.2157246 · Cited by 61 · cited 1×
“Restricting the use of generative AI tools is likely (at least in the short term) to shift practice towards increased use of secure testing environments [43], and a greater focus on the development and assessment of process skills [16].”
BackgroundOn Designing Programming Error Messages for Novices: Readability and its Constituent Factors
Denny, P., Prather, J., Becker, B. A., Mooney, C., Homer, J., Albrecht, Z. C., & Powell, G. B. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445696 · Cited by 60 · cited 1×
“Recent work has attempted to put error messages into more natural language by focusing on readability, which has been shown to improve student understanding of error messages and the number of successful code corrections [12].”
BackgroundSelf-citationTaylor, A., Vassar, A., Renzella, J., & Pearce, H. A. (2024) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3626252.3630822 · Cited by 59 · cited 1×
“Depending on the prompt, the resulting programs used varied programmatic structures, while ultimately favoring expected methods for each problem variation (i.e., for-loops for processing lists, and while-loops for processing standard input).”
BackgroundReeves, B., Sarsa, S., Prather, J., Denny, P., Becker, B. A., Hellas, A., Kimmel, B., Powell, G., & Leinonen, J. (2023) · Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1 · doi:10.1145/3587102.3588805 · Cited by 56 · cited 1×
“Newer models can also solve other types of programming exercises, like Parsons Problems, with decent accuracy that is likely to only increase over time [31].”
BackgroundSelf-citationEvery University Should Have a Computer-Based Testing Facility
Zilles, C. B., West, M., Herman, G. L., & Bretl, T. W. (2019) · International Conference on Computer Supported Education · doi:10.5220/0007753304140420 · Cited by 47 · cited 1×
“Restricting the use of generative AI tools is likely (at least in the short term) to shift practice towards increased use of secure testing environments [43], and a greater focus on the development and assessment of process skills [16].”
BackgroundLanguage models: past, present, and future
Li, H. (2022) · Communications of the ACM · doi:10.1145/3490443 · Cited by 45
Fooling MOSS Detection with Pretrained Language Models
Biderman, S., & Raff, E. (2022) · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · doi:10.1145/3511808.3557079 · Cited by 37 · cited 1×
“However, recent work has shown that common plagiarism detection tools are often ineffective against AI-generated solutions [6].”
BackgroundBecker, B. A., Denny, P., Finnie-Ansley, J., Luxton-Reilly, A., Prather, J., & Santos, E. A. (2022) · arXiv · doi:10.48550/arxiv.2212.01020 · Cited by 36 · cited 2×
“Alongside such challenges come emerging opportunities for students to learn computing skills [2].”
BackgroundSelf-citationGrounded 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×
“Other recent work has shown that developers are more successful working with Copilot when they decompose larger programming statements into smaller tasks and then explicitly prompt Copilot for each of the subtasks [1, 18].”
BackgroundThe Premature Obituary of Programming
Yellin, D. M. (2023) · Communications of the ACM · doi:10.1145/3555367 · Cited by 17 · cited 1×
“This aligns with Yellin’s recent viewpoint that as programs increase in complexity, natural language becomes too imprecise an instrument with which to specify them [40].”
BackgroundKazemitabaar, M., Hou, X., Henley, A. Z., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2309.14049 · Cited by 9 · cited 2×
“Students who have become overly reliant on model outputs may find it especially challenging to proceed when the suggested code is incorrect and cannot be resolved through natural language prompting [15].”
BackgroundTang, L., Ke, E., Singh, N., Feng, B., Austin, D. W., Verma, N., & Drori, I. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-11647-6_127 · Cited by 7 · cited 1×
“For example, when using Codex to solve probability and statistics problems, engineering the prompt to include explicit hints on the strategy for solving a problem is extremely effective [35].”
MethodDo Users Write More Insecure Code with AI Assistants?
Perry, N., Srivastava, M., Kumar, D., & Boneh, D. (2022) · arXiv · cited 1×
“Perry et al. recently examined whether novices using AI code generation tools wrote more secure code, finding that novices consistently wrote insecure code with specific high vulnerabilities in string encryption and SQL injection [29].”
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 2×
“Novices may be intimidated by such code generation [19] or may spend too much time reading code that does not further their goals [31].”
BackgroundSelf-citation
Research works printed but not linked: 4
- Porter, L. and Zingaro, D. Learn AI-Assisted Python Programming With GitHub Copilot and ChatGPT. Manning, Shelter Island, NY, USA (2023); https://bit.ly/4aE7CRa
- Sarkar A. et al. What is it like to program with artificial intelligence? (2022); arXiv:cs.HC/2208.06213
- Thompson, E., Whalley, J., Lister, R., and Simon, B. Code classification as a learning and assessment exercise for novice programmers. In Proceedings of the 19th Annual Conf. of the National Advisory Committee on Computing Qualifications (2006), 291--298.
- Wang, S., Piech, C., and Mitchell, J.C. A large scale RCT on effective error messages in CS1. In Proceedings of the 55th ACM Technical Symp. on Computer Science Education 1, Association for Computing Machinery (2023), 7.
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Artificial intelligence in teaching and teacher professional development: A systematic review
Tan, X., Cheng, G., & Ling, M. H. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100355 · Cited by 420
Generative 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
“Several authors lament the current state of popular AI detection tools, such as iThenti-cate, MOSS, and Turnitin, stating they are inadequate for the needs of educators [4,9,10].”
InfluentialComputer Science Curricula 2023
Kumar, A. N., Raj, R. K., Aly, S. G., Anderson, M., Becker, B. A., Blumenthal, R. L., Eaton, E., Epstein, S. L., Goldweber, M., Jalote, P., et al. (2024) · ACM eBooks · doi:10.1145/3664191 · Cited by 235
Self-citationThe 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
Self-citationSun, L., & Zhou, L. (2024) · Journal of Educational Computing Research · doi:10.1177/07356331241277937 · Cited by 129
“As students become more pro fi cient with Gen-AI, they can learn better, thus improving their academic achievement (Denny et al., 2023).”
Metadata
- Authors
- 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.
- Affiliations
- University of Auckland (NZ)
- Abilene Christian University (US)
- University College Dublin (IE)
- Aalto University (FI)
- ORCID
- Venue
- Communications of the ACM
- Published
- 2024-01-25
- Volume
- 67
- Issue
- 2
- Pages
- 56-67
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W4390985631
- Cited by
- 316
- References
- 38
- Citations by year
- 2026: 136 · 2025: 102 · 2024: 70
- FWCI
- 116.20
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- Aaltodoc (Aalto University) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Keywords
- generative AI, large language models, computing education, code generation, natural language programming, programming education, artificial intelligence in education
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