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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.
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“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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“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-citation
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Cited by
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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).”
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 168
“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 134
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
“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 82
“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 179
The Good and Bad of AI Tools in Novice Programming Education
Zviel-Girshin, R. (2024) · Education Sciences · doi:10.3390/educsci14101089 · Cited by 55
Liu, X., Guo, B., He, W., & Hu, X. (2025) · Journal of Educational Computing Research · doi:10.1177/07356331251329185 · Cited by 88
“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 82
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 66
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
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 68
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 56
“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 73
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
“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 43
“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 47
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
“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 48
“…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
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
“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 44
Choi, S., & Kim, H. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-13107-x · Cited by 44
“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
“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 38
“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 53
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
Tan, X., Cheng, G., & Ling, M. H. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100461 · Cited by 27
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 31
“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 40
“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 40
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 32
“…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 36
“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
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Self-citationHarnessing code domain insights: Enhancing programming Knowledge Tracing with Large Language Models
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Automating Autograding: Large Language Models as Test Suite Generators for Introductory Programming
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Self-citationBunduchi, R., Sitar‐Tăut, D., & Mican, D. (2025) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2025.124095 · Cited by 27
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 34
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
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
Self-citationFeng, H., Luxton-Reilly, A., Wuensche, B. C., & Denny, P. C. (2025) · Australasian Computing Education Conference · doi:10.1145/3716640.3716658 · Cited by 34
Self-citationExperiences from Integrating Large Language Model Chatbots into the Classroom
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Self-citationElSayary, A., Kuhail, M. A., & Hojeij, Z. (2025) · Human Behavior and Emerging Technologies · doi:10.1155/hbe2/9986139 · Cited by 25
A comparative study of AI ‐generated and human‐crafted learning objectives in computing education
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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 28
“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).”
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“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
“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 24
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Hersh, W., & Hollis, K. F. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01251-0 · Cited by 20
“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
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“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 33
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 26
Apiola, M., Vartiainen, H., & Tedre, M. (2024) · Annual Conference on Innovation and Technology in Computer Science Education · doi:10.1145/3649217.3653596 · Cited by 13
Integrating Natural Language Prompting Tasks in Introductory Programming Courses
Kerslake, C. P., 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 26
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
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 25
Self-citationWu, T., & Chang, M. (2023) · International Conference on Engineering and Emerging Technologies (ICEET) · doi:10.1109/iceet60227.2023.10525933 · Cited by 14
“[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 14
“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 20
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
“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 16
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 14
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
“(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 14
Kharrufa, A., Alghamdi, S. S., Aziz, A., & Bull, C. (2025) · ACM Transactions on Computing Education · doi:10.1145/3779296 · Cited by 26
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 18
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 10
Self-citationFeng, H., Denny, P. C., Wuensche, B. C., Luxton-Reilly, A., & Whalley, J. L. (2024) · doi:10.1145/3680533.3697064 · Cited by 11
“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
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
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
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 11
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 41
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
“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
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
Yu, Y., Krantz, A., & Lobczowski, N. G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98462-4_52 · Cited by 12
Beimel, D., Amzalag, M., Zviel-Girshin, R., & Voloch, N. (2025) · Education Sciences · doi:10.3390/educsci15111480 · Cited by 13
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
“…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
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
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 10
“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 11
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 14
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
“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 15
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
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
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 13
Self-citationSerafini, R., Yardım, A., & Naiakshina, A. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713989 · Cited by 6
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 10
“…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
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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