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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Current plagiarism detection tools fail to recognize original essays produced by ChatGPT.
“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]
The Academic integrity passage (indexed under “Metacognition”; page unavailable) says common plagiarism detection tools are often ineffective against AI-generated solutions in computing coursework. It does not establish that the tools fail to recognize ChatGPT-produced essays specifically, and “often ineffective” is narrower than an unqualified failure. (LLM, conf=0.86)
Experimental — accuracy not yet measuredPopular AI detection tools such as iThenticate, MOSS, and Turnitin are inadequate for the needs of educators.
“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]
The Metacognition section says common plagiarism-detection tools are often ineffective against AI-generated solutions, creating concerns for educators monitoring academic integrity. It does not identify iThenticate, MOSS, or Turnitin or assess those tools individually. (LLM, conf=0.86)
Experimental — accuracy not yet measuredGenerative AI such as GPT-4o CoPilot and OpenAI's GPT-4 enables the generation of sophisticated code from natural-language prompts.
“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]
The Abstract says that tools including GPT-4 and GitHub Copilot can generate source code from natural-language prompts. The sections on Plentiful learning resources and Academic misconduct discuss student overreliance and integrity concerns. Our copy does not mention GPT-4o, so it does not support that specific example. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThe generation of sophisticated code from natural-language prompts raises concerns about student overreliance and potential academic misconduct.
“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]
This model helps reconcile the performance-learning paradox noted in recent empirical and review work.
“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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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× · see citing works
“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× · see citing works
“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× · see citing works
“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× · see citing works
“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× · see citing works
“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 · see citing works
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 · 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 · cited 6× · see citing works
“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× · see citing works
“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× · 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].”
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× · see citing works
“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× · see citing works
“…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× · see citing works
“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× · see citing works
“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× · see citing works
“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× · 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].”
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× · see citing works
“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× · see citing works
“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-citationMacNeil, 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× · see citing works
“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× · see citing works
“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× · see citing works
“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× · see citing works
“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× · see citing works
“Developing computational thinking skills is important for novice programmers as it can foster higher-order thinking and reflection skills [24].”
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 132 · cited 2× · see citing works
“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].”
BackgroundWelsh, M. (2022) · Communications of the ACM · doi:10.1145/3570220 · Cited by 95 · cited 1× · see citing works
“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 · see citing works
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× · see citing works
“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× · 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].”
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× · see citing works
“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× · see citing works
“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-citationReeves, 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× · see citing works
“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× · see citing works
“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 · see citing works
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× · see citing works
“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× · see citing works
“Alongside such challenges come emerging opportunities for students to learn computing skills [2].”
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 33 · cited 1× · see citing works
“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).”
BackgroundGrounded 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
“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× · see citing works
“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× · see citing works
“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× · see citing works
“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
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Cited by
Sorted by citations
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 · see citing works
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 · see citing works
“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 · see citing works
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 · see citing works
Self-citationSun, L., & Zhou, L. (2024) · Journal of Educational Computing Research · doi:10.1177/07356331241277937 · Cited by 129 · see citing works
“As students become more pro fi cient with Gen-AI, they can learn better, thus improving their academic achievement (Denny et al., 2023).”
Prather, J. E., Leinonen, J., Kiesler, N., Benario, J. G., Lau, S., MacNeil, S., Norouzi, N., Opel, S., Pettit, V., Porter, L., et al. (2025) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3689187.3709614 · Cited by 97 · see citing works
“Computing education is undergoing a seismic shift due to the advances in generative AI (GenAI) [22, 36, 111].”
Self-citationDesirable Characteristics for AI Teaching Assistants in Programming Education
Denny, P. C., MacNeil, S., Šavelka, J., Porter, L., & Luxton-Reilly, A. (2024) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3649217.3653574 · Cited by 96 · see citing works
Self-citationGenerative AI in Education: A Study of Educators' Awareness, Sentiments, and Influencing Factors
Ghimire, A., Pather, J., & Edwards, J. (2024) · 2024 IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie61694.2024.10892891 · Cited by 88 · see citing works
“Recent advances in generative AI and natural language processing have enabled the development of large language models (LLMs) that show impressive capabilities in generating and reasoning about code [4].”
Margulieux, L. E., Prather, J. E., Reeves, B. N., Becker, B. A., Uzun, G. C., Loksa, D., Leinonen, J., & Denny, P. C. (2024) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3649217.3653621 · Cited by 62 · see citing works
“Denny et al. note that the developers of Codex named over-reliance as a key risk of GenAI and caution that relying too heavily on GenAI tools could hinder the development of crucial metacognitive skills [14], something that Becker et al. name as a critical competency in programming [3].”
BackgroundSelf-citationInteligencia artificial generativa y educación
García‐Peñalvo, F. J. (2024) · Education in the Knowledge Society (EKS) · doi:10.14201/eks.31942 · Cited by 128 · see citing works
The Good and Bad of AI Tools in Novice Programming Education
Zviel-Girshin, R. (2024) · Education Sciences · doi:10.3390/educsci14101089 · Cited by 55 · see citing works
Liu, X., Guo, B., He, W., & Hu, X. (2025) · Journal of Educational Computing Research · doi:10.1177/07356331251329185 · Cited by 78 · see citing works
“Denny et al. (2024) observed that GenAI can improve the performance of students preparing to undertake programming tasks; however, for struggling students, misinformation generated by GenAI may increase their cognitive load.”
Koutcheme, C., Dainese, N., Sarsa, S., Hellas, A., Leinonen, J., & Denny, P. C. (2024) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3649217.3653612 · Cited by 47 · see citing works
Self-citationDenny, P. C., Smith, D. H., Fowler, M., Prather, J. E., Becker, B. A., & Leinonen, J. (2024) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3649217.3653587 · Cited by 47 · see citing works
Self-citationLarge language models meet user interfaces: The case of provisioning feedback
Pozdniakov, S., Brazil, J., Abdi, S., Bakharia, A., Sadiq, S., Gašević, D., Denny, P., & Khosravi, H. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100289 · Cited by 43 · see citing works
Self-citationStudent-AI Interaction: A Case Study of CS1 students
Amoozadeh, M., Nam, D., Prol, D., Alfageeh, A., Prather, J. E., Hilton, M. L., Ragavan, S. S., & Alipour, M. A. (2024) · Koli Calling International Conference on Computing Education Research · doi:10.1145/3699538.3699567 · Cited by 41 · see citing works
Self-citationCould ChatGPT get an engineering degree? Evaluating higher education vulnerability to AI assistants
Borges, B., Foroutan, N., Bayazit, D., Sotnikova, A., Montariol, S., Nazaretsky, T., Banaei, M., Sakhaeirad, A., Servant, P., Neshaei, S. P., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2414955121 · Cited by 47 · see citing works
“While this capability reduces the burden on professional (and hobbyist) software developers, it also poses a risk for learners by potentially offering shortcuts that impede the acquisition of fundamental coding and engineering skills [17].”
Lepp, M., & Kaimre, J. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100642 · Cited by 40 · see citing works
AI-Enhanced Auto-Correction of Programming Exercises: How Effective is GPT-3.5?
Azaiz, I., Deckarm, O., & Strickroth, S. (2023) · International Journal of Engineering Pedagogy (iJEP) · doi:10.3991/ijep.v13i8.45621 · Cited by 38 · see citing works
“Danny et al. [17] explore the impact of LLMs’ on computing education, summarizing recent studies on their performance and pedagogical influence, covering responsible tool use, societal implications, and readiness for dynamic environments.”
BackgroundKim, Y. W., Cha, M. C., Yoon, S. H., & Lee, S. C. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2375701 · Cited by 38 · see citing works
“In line with the empirical findings of Sohn and Kwon (2020) as well as findings from other user studies investigating AI-CA usage patterns (Denny et al., 2024; Prather et al., 2024), this study confirms that perceived enjoyment is a significant factor in developers’ use of AI-CAs, possibly even…”
InfluentialCombining LLM-Generated and Test-Based Feedback in a MOOC for Programming
Gabbay, H., & Cohen, A. (2024) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3657604.3662040 · Cited by 36 · see citing works
Padiyath, A., Hou, X., Pang, A., Viramontes Vargas, D., Gu, X., Nelson-Fromm, T., Wu, Z., Guzdial, M., & Ericson, B. (2024) · Proceedings of the 2024 ACM Conference on International Computing Education Research - Volume 1 · doi:10.1145/3632620.3671098 · Cited by 35 · see citing works
“For example, the belief that the capacity of LLMs to generate code necessitates sweeping changes to programming pedagogy and curricula [24, 32].”
InfluentialHumble, N. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00494-x · Cited by 40 · see citing works
“…on GAI technologies (Albdrani & Al-Shargabi, 2023; Amoozadeh et al., 2024; Becker et al., 2023; Budhiraja et al., 2024; Cambaz & Zhang, 2024; Denny et al., 2024c; Fernandez et al., 2024; Finnie-Ansley et al., 2023; Groothuijsen et al., 2024; Gumina et al., 2023; Hellas et al., 2023;…”
InfluentialDickey, E., Bejarano, A., & Garg, C. (2024) · SN Computer Science · doi:10.1007/s42979-024-03074-y · Cited by 33 · see citing works
Evaluating the Application of Large Language Models to Generate Feedback in Programming Education
Jacobs, S., & Jaschke, S. (2024) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon60312.2024.10578838 · Cited by 37 · see citing works
“The development of large language models (LLMs) like GPT-4 [2] has opened up a wide range of new possibilities in this area [3] [4].”
BackgroundFotaris, P., Μάστορας, Θ., & Lameras, P. (2023) · European Conference on Games Based Learning · doi:10.34190/ecgbl.17.1.1870 · Cited by 30 · see citing works
Choi, S., & Kim, H. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-13107-x · Cited by 38 · see citing works
“Thus, when LLMs are used as educational tools, novice learners can find it challenging to comprehend as they lack proficiency in advanced codes (Denny et al., 2024).”
Decoding Debugging Instruction: A Systematic Literature Review of Debugging Interventions
Yang, S., Baird, M., O’Rourke, E., Brennan, K., & Schneider, B. (2024) · ACM Transactions on Computing Education · doi:10.1145/3690652 · Cited by 29 · see citing works
“While fixing one’s own code is more common in introductory courses, debugging foreign code is still important in various contexts, including working with APIs and libraries, collaborating with others [84], and interpreting artificial intelligence (AI) -generated code [38].”
Enhancing Programming Error Messages in Real Time with Generative AI
Kimmel, B., Geisert, A. L., Yaro, L., Gipson, B., Hotchkiss, R., Osae-Asante, S. K., Vaught, H., Wininger, G., & Yamaguchi, C. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3647967 · Cited by 29 · see citing works
“The introduction of artificial intelligence (AI) in the form of large language models (LLMs) is changing many disciplines, including computer science education [5].”
Gaitantzi, A., & Kazanidis, I. (2025) · Applied Sciences · doi:10.3390/app15073960 · Cited by 45 · see citing works
Generative artificial intelligence: implications for biomedical and health professions education
Hersh, W. (2025) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-103123-094756 · Cited by 28 · see citing works
Tan, X., Cheng, G., & Ling, M. H. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100461 · Cited by 27 · see citing works
Six Opportunities for Scientists and Engineers to Learn Programming Using AI Tools Such as ChatGPT
Guo, P. J. (2023) · Computing in Science & Engineering · doi:10.1109/mcse.2023.3308476 · Cited by 27 · see citing works
“However, most of the writing on this topic so far has been about using AI (especially within computer science departments) to teach students who aim to become full-time programmers.(1,2,3) Here, I provide a complementary perspective by addressing the unique needs of scientists and engineers who do not intend to become…”
BackgroundSen, R., & Deng, X. (2025) · Journal of Information Systems Education · doi:10.62273/zlum4022 · Cited by 25 · see citing works
“Because ChatGPT uses NLP and machine learning technologies to understand the user’s needs and respond accordingly, it provides responses without using syntax and concepts specific to programming languages, thus offering a different approach to IS and computing education (Denny et al., 2024).”
InfluentialLarge Language Models in Computer Science Classrooms: Ethical Challenges and Strategic Solutions
Azoulay, R., Hirst, T., & Reches, S. (2025) · Applied Sciences · doi:10.3390/app15041793 · Cited by 34 · see citing works
Will Code Remain a Relevant User Interface for End-User Programming with Generative AI Models?
Sarkar, A. (2023) · ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (Onward!) · doi:10.1145/3622758.3622882 · Cited by 25 · see citing works
“…to provide a significant improvement in degree, where explanations can be generated on an ad hoc basis for arbitrary code with arbitrary amounts of detail [23, 69], and it is likely that such explanations can be tailored with much greater precision to a particular learner in a particular instance.”
BackgroundExploring Student Interactions with AI in Programming Training
Fenu, G., Galici, R., Marras, M., & Recupero, D. R. (2024) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3631700.3665227 · Cited by 24 · see citing works
“In particular, AI has gained significant attention in the field of computing education and programming [6, 13].”
BackgroundGenerative AI in Computer Science Education: Challenges and Opportunities
Franklin, D., Denny, P., Gonzalez-Maldonado, D. A., & Tran, M. (2025) · doi:10.1017/9781009581738 · Cited by 20 · see citing works
Self-citationSmith, D. H., Denny, P. C., & Fowler, M. (2024) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3657604.3662039 · Cited by 22 · see citing works
Self-citationHarnessing code domain insights: Enhancing programming Knowledge Tracing with Large Language Models
Sun, X., Liu, Q., Zhang, K., Shen, S., Yang, L., & Li, H. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113396 · Cited by 22 · see citing works
Automating Autograding: Large Language Models as Test Suite Generators for Introductory Programming
Alkafaween, U., Al-Bluwi, I., & Denny, P. C. (2024) · Journal of Computer Assisted Learning · doi:10.1111/jcal.13100 · Cited by 23 · see citing works
Self-citationBunduchi, R., Sitar‐Tăut, D., & Mican, D. (2025) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2025.124095 · Cited by 22 · see citing works
Prather, J. E., Reeves, B. N., Denny, P. C., Leinonen, J., MacNeil, S., Luxton-Reilly, A., Orvalho, J. G., Alipour, M. A., Alfageeh, A., Amarouche, T., et al. (2025) · Australasian Computing Education Conference · doi:10.1145/3716640.3716649 · Cited by 20 · see citing works
Self-citationClear, T., Cajander, Å., Clear, A., McDermott, R., Daniels, M., Divitini, M., Forshaw, M., Humble, N., Kasinidou, M., Kleanthous, S., et al. (2025) · 2024 Working Group Reports on Innovation and Technology in Computer Science Education · doi:10.1145/3689187.3709607 · Cited by 19 · see citing works
Prather, J., Reeves, B. N., Denny, P., Leinonen, J., MacNeil, S., Luxton-Reilly, A., Orvalho, J., Alipour, A., Alfageeh, A., Amarouche, T., et al. (2024) · arXiv · doi:10.48550/arxiv.2412.12800 · Cited by 20 · see citing works
Self-citationFeng, H., Luxton-Reilly, A., Wuensche, B. C., & Denny, P. C. (2025) · Australasian Computing Education Conference · doi:10.1145/3716640.3716658 · Cited by 19 · see citing works
Self-citationExperiences from Integrating Large Language Model Chatbots into the Classroom
Hellas, A., Leinonen, J., & Leppänen, L. (2024) · ACM Virtual Global Computing Education Conference (SIGCSE Virtual) · doi:10.1145/3649165.3690101 · Cited by 19 · see citing works
Self-citationElSayary, A., Kuhail, M. A., & Hojeij, Z. (2025) · Human Behavior and Emerging Technologies · doi:10.1155/hbe2/9986139 · Cited by 25 · see citing works
A comparative study of AI ‐generated and human‐crafted learning objectives in computing education
Doyle, A., Sridhar, P., Agarwal, A., Šavelka, J., & Sakr, M. F. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.13092 · Cited by 20 · see citing works
Chen, Y., Xiao, S., Song, Y., Li, Z., Sun, L., & Chen, L. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2475991 · Cited by 25 · see citing works
“Researchers have expressed considerable concern about the misuse of GAI, especially among novices introduced to programming in the era of such technology (Chen et al., 2024; Denny et al., 2024; Kasneci et al., 2023; Yan et al., 2024).”
Chatbots in Education: A Dual Perspective on Innovation and Ethics
Pappagallo, S. (2024) · Journal of Digital Pedagogy · doi:10.61071/jdp.2420 · Cited by 16 · see citing works
Insights from the Frontline: GenAI Utilization Among Software Engineering Students
Choudhuri, R., Ramakrishnan, A., Chatterjee, A., Trinkenreich, B., Steinmacher, I., Gerosa, M. A., & Sarma, A. (2025) · IEEE International Conference on Software Engineering Education and Training (CSEE&T) · doi:10.1109/cseet66350.2025.00007 · Cited by 20 · see citing works
“The counterargument is that it is not all negative; there are opportunities despite the risks [14, 15, 9, 16] and “it depends” on how these tools are being used.”
InfluentialAnalysis 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
“Furthermore, prior work found that schools are reticent about using GenAI in STEM related activities [22], so it is even less clear how these policies and guidance are being taken up by computing educators, an important concern given the potential of GenAI for impacting computing pedagogy [10].”
Dawson, M. G., Deer, R., & Boguslawski, S. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100724 · Cited by 14 · see citing works
Teaching UML using a RAG-based LLM
Ardimento, P., Bernardi, M., & Cimitile, M. (2024) · IEEE International Joint Conference on Neural Network · doi:10.1109/ijcnn60899.2024.10651492 · Cited by 16 · see citing works
Hersh, W., & Hollis, K. F. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01251-0 · Cited by 14 · see citing works
“LLMs have had comparable strong impact in education in fi elds beyond biomedicine, such as business 16 , computer science 17 – 19 , law 20 , and data science 21 .”
Computer Programming Education in the Age of Generative AI: Insights from Empirical ResearchPreprint
Deriba, F. G., Sanusi, I. T., Campbell, O. O., & Oyelere, S. S. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4891302 · Cited by 19 · see citing works
Artificial intelligence and higher education
Behera, A., Trivedi, P., Patra, S., & Makeni, C. (2025) · American Journal of STEM Education · doi:10.32674/0x43s107 · Cited by 13 · see citing works
Gong, X., Xu, W., & Qiao, A. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00552-y · Cited by 13 · see citing works
“For example, over-reliance on GAI may weaken students’ problem-solving abilities, hinder the development of meta-cognitive skills essential for good programming (Denny et al., 2024a, b), and reduce the satisfaction derived from solving problems independently (Kazemitabaar et al., 2023).”
Bassner, P., Lenk-Ostendorf, B., Beinstingel, R., Wasner, T., & Krusche, S. (2025) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2025.100537 · Cited by 13 · see citing works
Rizun, N., Bordean, O. N., Nikiforova, A., Beleiu, I. N., & Revina, A. (2026) · Computers and Education Open · doi:10.1016/j.caeo.2026.100336 · Cited by 22 · see citing works
Apiola, M., Vartiainen, H., & Tedre, M. (2024) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3649217.3653596 · Cited by 13 · see citing works
Integrating Natural Language Prompting Tasks in Introductory Programming Courses
Kerslake, C., Denny, P. C., Smith, D. H., Prather, J. E., Leinonen, J., Luxton-Reilly, A., & MacNeil, S. (2024) · ACM Virtual Global Computing Education Conference (SIGCSE Virtual) · doi:10.1145/3649165.3690125 · Cited by 13 · see citing works
Self-citationBridging Novice Programmers and LLMs with Interactivity
Yeh, T. Y., Tran, K., Gao, G., Yu, T. T., Fong, W. O., & Chen, T.-Y. (2025) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3641554.3701867 · Cited by 13 · see citing works
Yu, Z., Liu, S., Denny, P. C., Bergen, A., & Liut, M. (2025) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3641554.3701844 · Cited by 12 · see citing works
Self-citationWu, T., & Chang, M. (2023) · International Conference on Engineering and Emerging Technologies (ICEET) · doi:10.1109/iceet60227.2023.10525933 · Cited by 12 · see citing works
“[20,21] Assignment rating Higher education assignments include essays and scientific experimental studies.”
BackgroundInvestigating the Efficacy of ChatGPT-3.5 for Tutoring in Chinese Elementary Education Settings
Bai, Y., Li, J., Shen, J., & Zhao, L. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3464560 · Cited by 8 · see citing works
“With the advancement in AI technologies and cognitive sciences, educational intelligence (EI) and intelligent tutoring systems (ITSs) [2], [3], [4] were developed to provide flexible and customized learning pathways based on learners’ performance and behaviors.”
BackgroundStone, I. (2024) · Conference on United Kingdom & Ireland Computing Education Research (UKICER) · doi:10.1145/3689535.3689553 · Cited by 13 · see citing works
Towards Integrating Emerging AI Applications in SE Education
Vierhauser, M., Groher, I., Antensteiner, T., & Sauerwein, C. (2024) · IEEE International Conference on Software Engineering Education and Training (CSEE&T) · doi:10.1109/cseet62301.2024.10663045 · Cited by 11 · see citing works
“Denny et al. [19] explore the challenges and opportunities presented by recent advances in AI, particularly code generation models, and their potential impact on computing education.”
BackgroundStudent's Use of Generative AI as a Support Tool in an Advanced Web Development Course
Alpizar-Chacón, I., & Keuning, H. (2025) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3724363.3729106 · Cited by 11 · see citing works
Solving Proof Block Problems Using Large Language Models
Poulsen, S., Sarsa, S., Prather, J. E., Leinonen, J., Becker, B. A., Hellas, A., Denny, P. C., & Reeves, B. N. (2024) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3626252.3630928 · Cited by 12 · see citing works
Self-citationPotencialidades do uso de Inteligência Artificial Generativa como apoio ao Ensino de Programação
da Silva, T. L., Vidotto, K. N. S., Tarouco, L. M. R., & da Silva, P. F. (2024) · Anais do ... Simpósio Brasileiro de Informática na Educação/Anais do Simpósio Brasileiro de Informática na Educação · doi:10.5753/sbie.2024.242711 · Cited by 10 · see citing works
“(2023) e (Denny et al ., 2024) destacam em seus trabalhos diversas oportunidades que as ferramentas de IA generativa oferecem para auxiliar no ensino de programação: • Exemplos de código: Ferramentas de IA podem gerar exemplos e testes, permitindo que os alunos verifiquem seu trabalho durante a…”
Kohn, T. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70043 · Cited by 13 · see citing works
Kharrufa, A., Alghamdi, S. S., Aziz, A., & Bull, C. (2025) · ACM Transactions on Computing Education · doi:10.1145/3779296 · Cited by 16 · see citing works
Automating Personalized Parsons Problems with Customized Contexts and Concepts
Gutierrez, A. D. C., Denny, P. C., & Luxton-Reilly, A. (2024) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3649217.3653568 · Cited by 9 · see citing works
Self-citationCipriano, B. P., Alves, P., & Denny, P. C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72315-5_6 · Cited by 9 · see citing works
Self-citationFeng, H., Denny, P. C., Wuensche, B. C., Luxton-Reilly, A., & Whalley, J. L. (2024) · doi:10.1145/3680533.3697064 · Cited by 10 · see citing works
“Additionally, LLMs are capable of assisting CS educators by generating educational material [Leinonen et al. 2023; Liffiton et al. 2023; MacNeil et al. 2023], providing many new opportunities [Bernstein et al. 2024; Denny et al. 2024a].”
Self-citationKorpimies, K., Laaksonen, A., & Luukkainen, M. (2024) · Koli Calling International Conference on Computing Education Research · doi:10.1145/3699538.3699541 · Cited by 11 · see citing works
A Pedagogical Framework for Developing Abstraction Skills
Begum, M., Crossley, J., Strömbäck, F., Akrida, E. C., Alpizar-Chacón, I., Evans, A. C., Gross, J. B., Haglund, P., Lonati, V., Satyavolu, C. J., et al. (2025) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3689187.3709613 · Cited by 9 · see citing works
A Data Science Course Utilizing GenAI
Browning, J. W., Bustard, J. D., Anderson, N., & Galway, L. (2024) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie61694.2024.10893452 · Cited by 9 · see citing works
Large Language Models (LLMs) in Programming Learning: The Current Research State and Agenda
Fu, Q., Zhao, Y., Jia, Z., & Zheng, Y. (2025) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2025.3622043 · Cited by 10 · see citing works
Exploring Student-AI Interactions in Vibe Coding
Geng, F., Shah, A., Li, H., Mulla, N., Swanson, S., Raj, G. S., Zingaro, D., & Porter, L. (2026) · Australasian Computing Education Conference · doi:10.1145/3786228.3786236 · Cited by 9 · see citing works
Poitras, E., Crane, B., & Siegel, A. (2024) · Proceedings of the 2024 on ACM Virtual Global Computing Education Conference V. 1 · doi:10.1145/3649165.3690111 · Cited by 9 · see citing works
“Novel pedagogical approaches are emerging to teach students how to use generative AI, emphasizing problem decomposition skills [3].”
Denny, P. C., Kumar, V., MacNeil, S., Prather, J. E., & Leinonen, J. (2025) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3724363.3729093 · Cited by 8 · see citing works
Self-citationUsing Generative AI to Support UX Design Students in Web Development Courses
Buendía, F., & Piris-Ruano, J. (2025) · Applied Sciences · doi:10.3390/app15137389 · Cited by 8 · see citing works
Yu, Y., Krantz, A., & Lobczowski, N. G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98462-4_52 · Cited by 9 · see citing works
Beimel, D., Amzalag, M., Zviel-Girshin, R., & Voloch, N. (2025) · Education Sciences · doi:10.3390/educsci15111480 · Cited by 8 · see citing works
AI and engineering careers: recent graduates’ outlook on opportunities and challenges
Martin, J. P., Brown, J. S., Dunmoye, I. D., Smith, L. T., Suresh, D. E., & Jensen, P. A. (2025) · International Journal of STEM Education · doi:10.1186/s40594-025-00583-x · Cited by 13 · see citing works
“…are using GenAI models for assistance with reading and writing tasks, including proofreading, summarization, and drafting assignments (Cooper, 2023) as well as to produce functional code and solve problems in many introductory computing courses (Denny et al., 2024; Kazemitabaar et al., 2023).”
Liu, D., Fan, G., & Pan, L. (2026) · International Journal of STEM Education · doi:10.1186/s40594-025-00592-w · Cited by 16 · see citing works
Bridging the Gap: Exploring the Revolutionary Application of GenAI in Language Teaching and Learning
Jadhav, S. (2024) · Journal of Electrical Systems · doi:10.52783/jes.2325 · Cited by 7 · see citing works
Generative AI in CS Education: Literature Review through a SWOT Lens
Roberts, J., & Mohamed, A. (2024) · Western Canadian Conference on Computing Education · doi:10.1145/3660650.3660657 · Cited by 9 · see citing works
“This issue has motivated a lot of literature on remedies and strategies to support academic integrity [12, 19, 23, 26–29].”
BackgroundMethodInfluentialBrown, N. C. C., Weill-Tessier, P., Leinonen, J., Denny, P. C., & Kölling, M. (2025) · ACM Transactions on Computing Education · doi:10.1145/3737885 · Cited by 8 · see citing works
Self-citationFostering Responsible AI Use Through Negative Expertise: A Contextualized Autocompletion Quiz
MacNeil, S., Prather, J. E., Nabid, R. A., Gutierrez, S., Carvalho, S., Shrestha, S., Denny, P. C., Reeves, B. N., Leinonen, J., & Rossetti, R. L. (2025) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3724363.3729067 · Cited by 7 · see citing works
Self-citationSamarakoon, P., Asanka, D., Jayalal, S., & Jayalath, N. (2024) · International Research Conference on Smart Computing and Systems Engineering (SCSE) · doi:10.1109/scse61872.2024.10550837 · Cited by 7 · see citing works
“Evaluated by Denny et al. [11], FLAG identifies 101 defects, narrowing the search space to 12-17% of the source code.”
BackgroundLeinonen, J., Denny, P. C., Kiljunen, O., MacNeil, S., Sarsa, S., & Hellas, A. (2025) · Australasian Computing Education Conference · doi:10.1145/3716640.3716647 · Cited by 7 · see citing works
Self-citationVadaparty, A., Geng, F., Smith, D. H., Benario, J. G., Zingaro, D., & Porter, L. (2025) · Australasian Computing Education Conference · doi:10.1145/3716640.3716656 · Cited by 7 · 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 6 · see citing works
Explain in Plain Language Questions with Indic Languages: Drawbacks, Affordances, and Opportunities
Smith, D. H., Kumar, V., & Denny, P. C. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-84391-4_1 · Cited by 6 · see citing works
Self-citationSerafini, R., Yardım, A., & Naiakshina, A. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713989 · Cited by 6 · see citing works
Exploring Implementations of GenAI in Teaching IS Subjects and Student Perceptions
Jiang, Y., & Nakatani, K. (2025) · Journal of Information Systems Education · doi:10.62273/wfho1011 · Cited by 8 · see citing works
“…et al., 2023; Van Slyke et al., 2023; Yu, 2024 Support educators in developing teaching materials, exercises, and assignments AlAfnan et al., 2023; Denny et al., 2024; Dwivedi et al., 2023; Kakhki, et al., 2024; Labadze et al., 2023; Peres et al., 2023; Van Slyke et al., 2023; Yu, 2024 Table 1.”
Influential
- 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 · 2023: 8
- 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
- Physical Sciences › Computer Science › Information Systems › 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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