Generative AI in Computing Education: Perspectives of Students and Instructors
Abstract
Generative models are now capable of producing natural language text that is, in some cases, comparable in quality to the text produced by people. In the computing education context, these models are being used to generate code, code explanations, and programming exercises. The rapid adoption of these models has prompted multiple position papers and workshops which discuss the implications of these models for computing education, both positive and negative. This paper presents results from a series of semi-structured interviews with 12 students and 6 instructors about their awareness, experiences, and preferences regarding the use of tools powered by generative AI in computing classrooms. The results suggest that Generative AI (GAI) tools will play an increasingly significant role in computing education. However, students and instructors also raised numerous concerns about how these models should be integrated to best support the needs and learning goals of students. We also identified interesting tensions and alignments that emerged between how instructors and students prefer to engage with these models. We discuss these results and provide recommendations related to curriculum development, assessment methods, and pedagogical practice. As GAI tools become increasingly prevalent, it's important to understand educational stakeholders' preferences and values to ensure that these tools can be used for good and that potential harms can be mitigated.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A recent interview study with individual students revealed interest in using GenAI for generating supplementary learning materials and for skipping coursework they do not find engaging enough.
“A recent interview study with individual students revealed interest in using the technology for purposes like generating supplementary learning materials, but also skipping coursework they don't find engaging enough [Zastudil et al. 2023].”
From How Do Programming Students Use Generative AI? (2025) · cites this paper as Zastudil et al. 2023
Students may have thought it more socially acceptable to report slightly lower than true ChatGPT usage numbers due to the perception of such activity as engaging in cheating.
“They may have thought it to be more socially acceptable to report slightly lower than true ChatGPT usage numbers due to the perception of such activity as engaging in cheating [Zastudil et al. 2023].”
From How Do Programming Students Use Generative AI? (2025) · cites this paper as Zastudil et al. 2023
The potential social desirability bias in self-reported ChatGPT usage is not a major limitation.
“However, as the results are fairly consistent with similar surveys around the time [Kazemitabaar et al. 2023; Prather et al. 2023a; Zastudil et al. 2023] and we were commonly able to observe behavior that could be considered unethical in an academic context [Prather et al. 2023a], we do not believe this to be a major limitation.”
From How Do Programming Students Use Generative AI? (2025) · cites this paper as Zastudil et al. 2023
Governance configurations emphasizing academic integrity and evidence requirements demonstrated more pronounced effects in promoting ethical internalization, yet did not necessarily lead to synchronous reductions in anxiety.
“This study further reveals that governance configurations emphasizing academic integrity and evidence requirements demonstrated more pronounced effects in promoting ethical internalization, yet did not necessarily lead to synchronous reductions in anxiety (Zastudil et al., 2023).”
From Multidimensional responses to generative AI in higher education: an integrative framework linking institutional governance, teaching practice, and student psychological adaptation (2026) · cites this paper as Zastudil et al., 2023
GenAI technologies could inadvertently lead to academic laziness and overreliance if not carefully managed.
“These technologies could inadvertently lead to academic laziness and overreliance if not carefully managed [11,14].”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [14]
The advantages of GenAI in computing education are opportunities for higher levels of learning outcomes, personalized learning, timely feedback, and alternative assistance.
“The advantages are opportunities for higher levels of learning outcomes, personalized learning, timely feedback, and alternative assistance [155, 357, 374].”
From Navigating the Ethical and Societal Impacts of Generative AI in Higher Computing Education (2025) · cites this paper as [374]
Zastudil et al. [374] found that all instructors expressed concerns about students using GenAI tools inappropriately.
“A study involving interviews of instructors and students by Zastudil et al. [374] found that all instructors expressed concerns about students using GenAI tools inappropriately”
From Navigating the Ethical and Societal Impacts of Generative AI in Higher Computing Education (2025) · cites this paper as [374]
Zastudil et al. [374] found that students reported willingness to use GenAI to cheat on an assignment when they don't see the value of the assignment.
“while students reported their willingness to use GenAI to cheat on an assignment when they don't see the value of the assignment.”
From Navigating the Ethical and Societal Impacts of Generative AI in Higher Computing Education (2025) · cites this paper as [374]
Students said that AI tools reduced the effort of writing code, helped them to find learning materials, and allowed them to avoid busy work.
“Students said that the AI tools reduced the effort of writing code, helped them to find learning materials, and allowed them to avoid busy work.”
From Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study (2024) · cites this paper as [39]
Zastudil et al. interviewed 12 students and six instructors, all from the same institution, investigating experiences and preferences for AI tools in computing classrooms.
“Zastudil et al. [39] interviewed 12 students and six instructors, all from the same institution, investigating experiences and preferences for AI tools in computing classrooms.”
From Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study (2024) · cites this paper as [39]
Assessment was the area where interviewees perceived the most threats.
“Assessment was the area where interviewees perceived the most threats, which has also been found in other studies [19,39].”
From Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study (2024) · cites this paper as [39]
Instructors saw benefits of AI tools in providing explanations of code, helping students to find inspiration and to get feedback, and allowing access to high quality learning resources.
“Instructors saw benefits of the AI tools in providing explanations of code, helping students to find inspiration and to get feedback, and allowing access to high quality learning resources.”
From Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study (2024) · cites this paper as [39]
Instructors expressed concerns about over-reliance and plagiarism, and pointed out issues with the trustworthiness of the systems.
“The instructors, too, expressed concerns about over-reliance and plagiarism, and pointed out issues with the trustworthiness of the systems.”
From Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study (2024) · cites this paper as [39]
Students voiced concerns about over-reliance on and trustworthiness of AI tools, and noted that the availability of such tools might increase plagiarism.
“They also voiced concerns about over-reliance on and trustworthiness of the AI tools, and noted that the availability of such tools might increase plagiarism.”
From Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study (2024) · cites this paper as [39]
All interviewees expressed concerns about students missing out on learning by using AI tools.
“All interviewees expressed concerns about students missing out on learning by using AI tools, a similar finding to the study by Zastudil et al. [39].”
From Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study (2024) · cites this paper as [39]
The initial focus of discussion among computing departments has been concerns about academic integrity among students and the trivializing of many introductory course assignments, as reflected in the interviews and prior studies.
“The initial focus of discussion among computing departments has been concerns about academic integrity among students, as well as the trivializing of many of the assignments in our introductory courses, which is reflected both in our interviews and in prior studies [19,39].”
From Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study (2024) · cites this paper as [39]
Zastudil et al. observed a positive attitude with regard to integrating GenAI into the educational process as well as concerns about over-reliance, trustworthiness and academic integrity.
“Zastudil et al. [20] interviewed computer science students and instructors to understand their interaction with GenAI and their expectations regarding its use in education.They observed a positive attitude with regard to integrating GenAI into the educational process as well as concerns about over-reliance, trustworthiness and academic integrity.”
From Incorporating Generative AI into Software Development Education (2024) · cites this paper as [20]
Cites
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326 · cited 2× · see citing works
“For example, researchers and practitioners are already raising concerns about over-reliance, academic misconduct, and model trustworthiness [7], [12].”
BackgroundTrust in Automation: Designing for Appropriate Reliance
Lee, J. D., & See, K. A. (2004) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/hfes.46.1.50_30392 · Cited by 6,605 · see citing works
Artificial Intelligence in Education: A Review
Chen, L., Chen, P., & Lin, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2988510 · Cited by 4,133 · cited 1× · see citing works
“…which AI in the classroom can be beneficial for instructors such as in improving student reviews, grading, and feedback, utilizing intelligent tutoring systems, and improving pedagogical practices and student experiences using AI-powered VR for experiential or practical learning experiences [24].”
BackgroundWhat is AI Literacy? Competencies and Design Considerations
Long, D., & Magerko, B. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376727 · Cited by 3,241 · see citing works
Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust
Hoff, K. A., & Bashir, M. (2015) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720814547570 · Cited by 2,817 · see citing works
Explainable Artificial Intelligence in education
Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y.-S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S., & Gašević, D. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100074 · Cited by 863 · see citing works
Active Learning: Creating Excitement in the Classroom. 1991 ASHE-ERIC Higher Education Reports.
Bonwell, C. C., & Eison, J. A. (1991) · Cited by 699 · see citing works
Anderson, J. R., Boyle, C., & Reiser, B. (1985) · Science · doi:10.1126/science.228.4698.456 · Cited by 699 · see citing works
Do as AI say: susceptibility in deployment of clinical decision-aids
Gaube, S., Suresh, H., Raue, M., Merritt, A., Berkowitz, S. J., Lermer, E., Coughlin, J. F., Guttag, J. V., Colak, E., & Ghassemi, M. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00385-9 · Cited by 543 · cited 1× · see citing works
“Finally, there can be a tendency to develop an over-reliance on AI systems which is, unfortunately, most common for low-expertise users [30].”
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
The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming
Finnie-Ansley, J., Denny, P., Becker, B. A., Luxton-Reilly, A., & Prather, J. (2022) · Proceedings of the 24th Australasian Computing Education Conference · doi:10.1145/3511861.3511863 · Cited by 463 · see citing works
Magaldi, D., & Berler, M. (2020) · Encyclopedia of Personality and Individual Differences · doi:10.1007/978-3-319-24612-3_857 · Cited by 433 · see citing works
Co-Designing a Real-Time Classroom Orchestration Tool to Support Teacher–AI Complementarity
Holstein, K., McLaren, B. M., & Aleven, V. (2019) · Journal of Learning Analytics · doi:10.18608/jla.2019.62.3 · Cited by 429 · see citing works
Becker, B. A., Denny, P., Finnie-Ansley, J., Luxton-Reilly, A., Prather, J., & Santos, E. A. (2023) · Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 · doi:10.1145/3545945.3569759 · Cited by 401 · see citing works
Human-Centered Artificial Intelligence and Machine Learning
Riedl, M. O. (2019) · Human Behavior and Emerging Technologies · doi:10.1002/hbe2.117 · Cited by 352 · see citing works
Educational Data Mining and Learning Analytics in Programming
Ihantola, P., Vihavainen, A., Ahadi, A., Butler, M., Börstler, J., Edwards, S. H., Isohanni, E., Korhonen, A., Petersen, A., Rivers, K., et al. (2015) · doi:10.1145/2858796.2858798 · Cited by 332 · see citing works
Denny, 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 · see citing works
Human-Centered Explainable AI: Towards a Reflective Sociotechnical Approach
Ehsan, U., & Riedl, M. O. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-60117-1_33 · Cited by 274 · see citing works
Learning Analytics in Higher Education—A Literature Review
Leitner, P., Khalil, M., & Ebner, M. (2017) · Studies in systems, decision and control · doi:10.1007/978-3-319-52977-6_1 · Cited by 263 · see citing works
Lau, 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 · see citing works
Student Learning Benefits of a Mixed-Reality Teacher Awareness Tool in AI-Enhanced Classrooms
Holstein, K., McLaren, B. M., & Aleven, V. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-93843-1_12 · Cited by 235 · see citing works
Comparing 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 · see citing works
Self-citationAn Operationalized Understanding of Personalized Learning
Basham, J. D., Hall, T. E., Carter, R. A., & Stahl, W. M. (2016) · Journal of Special Education Technology · doi:10.1177/0162643416660835 · Cited by 207 · see citing works
Generating Diverse Code Explanations using the GPT-3 Large Language Model
MacNeil, S., Tran, A., Mogil, D., Bernstein, S., Ross, E., & Huang, Z. (2022) · Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 2 · doi:10.1145/3501709.3544280 · Cited by 200 · see citing works
Self-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 · see citing works
Self-citationUsing GitHub Copilot to Solve Simple Programming Problems
Wermelinger, M. (2023) · Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 · doi:10.1145/3545945.3569830 · Cited by 191 · see citing works
Intelligent tutoring systems for programming education: a systematic review
Crow, T., Luxton-Reilly, A., & Wuensche, B. (2018) · Proceedings of the 20th Australasian Computing Education Conference · doi:10.1145/3160489.3160492 · Cited by 166 · see citing works
Analysis of research into the teaching and learning of programming
Sheard, J., Simon, S., Hamilton, M., & Lönnberg, J. (2009) · doi:10.1145/1584322.1584334 · Cited by 161 · see citing works
Interviews in qualitative research
Peters, K., & Halcomb, E. (2015) · Nurse Researcher · doi:10.7748/nr.22.4.6.s2 · Cited by 141 · see citing works
Explainable artificial intelligence for education and training
Fiok, K., Farahani, F. V., Karwowski, W., & Ahram, T. (2021) · The Journal of Defense Modeling and Simulation Applications Methodology Technology · doi:10.1177/15485129211028651 · Cited by 119 · see citing works
Rienties, B., Simonsen, H. K., & Herodotou, C. (2020) · Frontiers in Education · doi:10.3389/feduc.2020.00128 · Cited by 98 · see citing works
Cerratto Pargman, T., & McGrath, C. (2021) · Journal of Learning Analytics · doi:10.18608/jla.2021.1 · Cited by 88 · see citing works
Methodological Rigor and Theoretical Foundations of CS Education Research
Lishinski, A., Good, J., Sands, P., & Yadav, A. K. (2016) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/2960310.2960328 · Cited by 78 · see citing works
Unintended Consequences of Introducing AI Systems for Decision Making
Mayer, A.-S., Strich, F., & Fiedler, M. (2020) · MIS Q. Executive · doi:10.17705/2msqe.00036 · Cited by 73 · see citing works
Large Language Models (GPT) Struggle to Answer Multiple-Choice Questions About Code
Šavelka, J., Agarwal, A., Bogart, C., & Sakr, M. F. (2023) · International Conference on Computer Supported Education · doi:10.5220/0011996900003470 · Cited by 56 · see citing works
Large Language Models (GPT) Struggle to Answer Multiple-Choice Questions about Code
Savelka, J., Agarwal, A., Bogart, C., & Sakr, M. (2023) · arXiv · doi:10.48550/arxiv.2303.08033 · Cited by 56 · see citing works
The graphics calculator in mathematics education
Drijvers, P. H. M., & Doorman, M. (1996) · The Journal of Mathematical Behavior · doi:10.1016/s0732-3123(96)90027-9 · Cited by 43 · see citing works
The Implications of Large Language Models for CS Teachers and Students
MacNeil, S., Kim, J., Leinonen, J., Denny, P. C., Bernstein, S., Becker, B. A., Michel Wermelinger, B., Hellas, A., Tran, A., Sarsa, S., et al. (2022) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3545947.3573358 · Cited by 37 · see citing works
Self-citationHeckman, S. S., Carver, J. C., Sherriff, M. S., & Al-Zubidy, A. (2021) · ACM Transactions on Computing Education · doi:10.1145/3470652 · Cited by 34 · see citing works
Schulz, S., Berndt, S., & Hawlitschek, A. (2022) · Computer Science Education · doi:10.1080/08993408.2021.2022361 · Cited by 30 · see citing works
Prather, J. E., Denny, P. C., Leinonen, J., Becker, B. A., Al-Bluwi, I., Caspersen, M. E., Craig, M., Keuning, H., Kiesler, N., Kohn, T., et al. (2023) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3587103.3594206 · Cited by 26 · see citing works
Self-citationWhat Is Your Biggest Pain Point?
Mirhosseini, S., Henley, A. Z., & Parnin, C. (2023) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3545945.3569816 · Cited by 17 · see citing works
Using Large Language Models to Automatically Identify Programming Concepts in Code Snippets
Tran, A., Li, L., Rama, E., Angelikas, K., & MacNeil, S. (2023) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3568812.3603482 · Cited by 13 · see citing works
Self-citationIncluding Students and Teachers in the Co-Design of the Enacted Curriculum
Gunckel, K. L., & Moore, F. M. (2005) · Cited by 10 · see citing works
Copying Can Be Good: How Students View Imitation as a Tool in Learning to Program
Zander, C., Thomas, L., Moström, J. E., & Eckerdal, A. (2020) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie44824.2020.9274064 · Cited by 2 · see citing works
Student Perspectives on On-site versus Online Teaching throughout the Covid-19 Pandemic
Jónsson, B. Þ., Pischetola, M., Inie, N., Daniels, M., & Brabrand, C. (2022) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie56618.2022.9962707 · Cited by 2 · see citing works
van Beek, L., van den Bogaard, M., & de Vries, M. J. (2021) · 2022 IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie49875.2021.9637295 · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Wang, H., Dang, A., Wu, Z., & Mac, S. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100326 · Cited by 272 · 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
“These technologies could inadvertently lead to academic laziness and overreliance if not carefully managed [11,14].”
The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers
Prather, J., Reeves, B. N., Leinonen, J., MacNeil, S., Randrianasolo, A. S., Becker, B. A., Kimmel, B., Wright, J., & Briggs, B. (2024) · Proceedings of the 2024 ACM Conference on International Computing Education Research - Volume 1 · doi:10.1145/3632620.3671116 · Cited by 196 · see citing works
“This has raised concerns from educators about potential student over-reliance [6, 29, 61, 72].”
Self-citationLarge Language Models in Computer Science Education: A Systematic Literature Review
Raihan, M. N., Siddiq, M. L., Santos, J. C. S., & Zampieri, M. (2025) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3641554.3701863 · Cited by 125 · see citing works
Sergeeva, O. V., Zheltukhina, M. R., Shoustikova, T., Tukhvatullina, L. R., Dobrokhotov, D. A., & Kondrashev, S. V. (2025) · Contemporary Educational Technology · doi:10.30935/cedtech/16039 · Cited by 131 · see citing works
Incorporating Generative AI into Software Development Education
Petrovska, O., Clift, L., Moller, F., & Pearsall, R. (2024) · Proceedings of the 8th Conference on Computing Education Practice · doi:10.1145/3633053.3633057 · Cited by 75 · see citing works
Yakubu, M. N., David, N., & Abubakar, N. H. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13441-8 · Cited by 99 · see citing works
Generating Multiple Choice Questions for Computing Courses Using Large Language Models
Tran, A., Angelikas, K., Rama, E., Okechukwu, C., Smith, D. H., & MacNeil, S. (2023) · 2023 IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie58773.2023.10342898 · Cited by 60 · see citing works
Self-citationAn Introduction to Generative AI Tools for Education 2030
Sandhu, R., Channi, H. K., Ghai, D., Cheema, G. S., & Kaur, M. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-2440-0.ch001 · Cited by 55 · see citing works
Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study
Sheard, J., Denny, P., Hellas, A., Leinonen, J., Malmi, L., & Simon (2024) · Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 · doi:10.1145/3626252.3630880 · Cited by 54 · see citing works
Chen, A., Jia, J., Li, Y., & Fu, L. (2024) · Journal of Educational Computing Research · doi:10.1177/07356331241299058 · Cited by 53 · see citing works
The role of generative AI in education: Perceptions of Saudi students
Aldossary, A., Aljindi, A. A., & Alamri, J. M. (2024) · Contemporary Educational Technology · doi:10.30935/cedtech/15496 · Cited by 63 · see citing works
Ghimire, A., & Edwards, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-64299-9_20 · Cited by 42 · see citing works
Chugh, R., Turnbull, D., Morshed, A., Sabrina, F., Azad, S., Rashid, M. M., Kaisar, S., & Subramani, S. (2025) · Computer Applications in Engineering Education · doi:10.1002/cae.70002 · Cited by 50 · see citing works
The Mediating Effect of AI Trust on AI Self-Efficacy and Attitude Toward AI of College Students
Obenza, B. N., Baguio, J. S. I. E., Bardago, K. M. W., Granado, L. B., Loreco, K. C. A., Matugas, L. P., Talaboc, D. J., Zayas, R. K. D. D., Caballo, J. H. S., & Caangay, R. B. R. (2023) · International Journal of Metaverse · doi:10.54536/ijm.v2i1.2286 · Cited by 49 · see citing works
Hou, I., Man, O., Hamilton, K., Muthusekaran, S., Johnykutty, J., Zadeh, L., & MacNeil, S. (2025) · Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 1 · doi:10.1145/3724363.3729024 · Cited by 37 · see citing works
Self-citationComputing education using generative artificial intelligence tools: A systematic literature review
Agbo, F. J., Olivia, C., Oguibe, G. F., Sanusi, I. T., & Sani, G. (2025) · Computers and Education Open · doi:10.1016/j.caeo.2025.100266 · Cited by 48 · see citing works
How Do Programming Students Use Generative AI?
Rahe, C., & Maalej, W. (2025) · Proceedings of the ACM on Software Engineering · doi:10.1145/3715762 · Cited by 45 · see citing works
“These concerns have been echoed by a smaller interview study by Zastudil et al. [2023], where all instructors and a majority of the students reported anticipating an increase in academic dishonesty due to GenAI tools.”
InfluentialScholl, A., & Kiesler, N. (2024) · 2024 IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie61694.2024.10893442 · Cited by 35 · see citing works
Kazemitabaar, M., Huang, O., Suh, S., Henley, A. Z., & Grossman, T. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712104 · Cited by 48 · 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
Faculty acceptance and use of generative artificial intelligence in their practice
Montes, J. N., & Elizondo-García, J. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1427450 · Cited by 33 · see citing works
Humble, N. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00494-x · Cited by 40 · see citing works
Chiu, M.-L. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13612-7 · Cited by 29 · see citing works
Eden, C. A., Chisom, O. N., & Adeniyi, I. S. (2024) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.21.3.0693 · Cited by 29 · see citing works
Integrating Generative AI in Hackathons: Opportunities, Challenges, and Educational Implications
Sajja, R., Ramirez, C. E., Li, Z., Demiray, B. Z., Sermet, Y., & Demir, I. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8120188 · Cited by 28 · see citing works
Chugh, R., Turnbull, D., Kutty, S., Sabrina, F., Rashid, M. M., Morshed, A., Azad, S., Kaisar, S., & Subramani, S. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13686-3 · Cited by 37 · see citing works
Bernstein, S., Denny, P. C., Leinonen, J., Kan, L., Hellas, A., Littlefield, M., Sarsa, S., & MacNeil, S. (2024) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3649217.3653533 · Cited by 24 · see citing works
Self-citationIshmuradova, I. I., Zhdanov, S. P., Kondrashev, S. V., Erokhova, N. S., Grishnova, E. E., & Volosova, N. Y. (2025) · Contemporary Educational Technology · doi:10.30935/cedtech/16207 · Cited by 22 · see citing works
Examining Student and Teacher Perspectives on Undisclosed Use of Generative AI in Academic Work
Adnin, R., Pandkar, A., Yao, B., Wang, D., & Das, M. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713393 · Cited by 16 · see citing works
Lyu, W., Wang, Y., Sun, Y., & Zhang, Y. (2025) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3698205.3729544 · Cited by 19 · 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 Role of Generative AI in Software Student CollaborAItion
Kiesler, N., Smith, J., Leinonen, J., Fox, A., MacNeil, S., & Ihantola, P. (2025) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3724363.3729040 · Cited by 14 · see citing works
Self-citationCucuiat, V., & Waite, J. (2024) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3649217.3653595 · Cited by 14 · 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
Reihanian, I., Hou, Y., Chen, Y., & Zheng, Y. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-94943-2_11 · Cited by 17 · see citing works
Garcia, R., Csizmadia, A. P., Pearce, J. L., Alshaigy, B., Glebova, O., Harrington, B., Liaskos, K. B., Lunn, S. J., MacKellar, B., Nasir, U., et al. (2025) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3689187.3709610 · Cited by 11 · see citing works
A Bibliometric Exposition and Review on Leveraging LLMs for Programming Education
Amos, J. P., Amodu, O. A., Mahmood, R. A. R., AbdulQudus, A. B., Zakaria, A. F., Iyanda, A. R., Bukar, U. A., & Hanapi, Z. M. (2025) · IEEE Access · doi:10.1109/access.2025.3554627 · Cited by 13 · see citing works
Overviewing Biases in Generative AI-Powered Models in the Arabic Language
Abubakari, M. S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2530-9.ch013 · Cited by 12 · see citing works
The Nexus between Cognitive Absorption and AI Literacy of College Students as Moderated by Sex
Obenza, B. N., Go, L. E., Francisco, J. A. M., Buit, E. E. T., Mariano, F. V. B., Cuizon, H. L., Cagabhion, A. J. D., & Agbulos, K. A. J. L. (2024) · American Journal of Smart Technology and Solutions · doi:10.54536/ajsts.v3i1.2603 · Cited by 19 · see citing works
Kohn, T. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70043 · Cited by 13 · see citing works
Korpimies, 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
Bridging the Gap in AI-Driven Workflows: The Case for Domain-Specific Generative Bots
Kumar, A., Devi, M. S. L., & Saltz, J. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386894 · Cited by 9 · see citing works
Anderson, O. S., Laubepin, F. A., & August, E. T. (2024) · Pedagogy in Health Promotion · doi:10.1177/23733799241246954 · Cited by 8 · see citing works
University Students' Perception and Expectations of Generative AI Tools for Software Engineering
Yabaku, M., & Ouhbi, S. (2024) · IEEE International Conference on Software Engineering Education and Training (CSEE&T) · doi:10.1109/cseet62301.2024.10663035 · Cited by 8 · see citing works
MacArthur, M. J., Minnillo, S., Sperber, L., Whithaus, C., & Stillman, N. (2025) · Frontiers in Communication · doi:10.3389/fcomm.2025.1615752 · Cited by 8 · see citing works
Gutierrez, S., Hou, I., Lee, J., Angelikas, K., Man, O., Mettille, S., Prather, J. E., Denny, P. C., & MacNeil, S. (2025) · Australasian Computing Education Conference · doi:10.1145/3716640.3716654 · Cited by 7 · see citing works
Self-citationTransformative Impact of Predictive and Generative AI on Education Workforce in Developing Countries
Weligodapola, M., & Kumarapperuma, C. (2025) · International Conference on Advanced Research in Computing (ICARC) · doi:10.1109/icarc64760.2025.10963062 · 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 12 · see citing works
Self-citationEvolution of Programmers' Trust in Generative AI Programming Assistants
Shah, A., Rexin, T., Tomson, E., Griswold, W. G., Porter, L., & Raj, A. G. S. (2025) · Koli Calling International Conference on Computing Education Research · doi:10.1145/3769994.3770029 · Cited by 7 · see citing works
Alpizar-Chacón, I., Keuning, H., de Jong, I., Lykourentzou, I., & Rings, S. (2025) · Koli Calling International Conference on Computing Education Research · doi:10.1145/3769994.3770008 · Cited by 6 · see citing works
Wu, C., & Carroll, J. M. (2025) · TechTrends · doi:10.1007/s11528-025-01150-5 · Cited by 6 · see citing works
El-Khalili, N. H., Al-Nashashibi, M. Y., & Al-E’mari, S. (2024) · International Conference on Cyber Resilience (ICCR) · doi:10.1109/iccr61006.2024.10532824 · Cited by 6 · see citing works
Yoo, K., & Ahn, S. (2024) · The Journal of Korean Association of Computer Education · doi:10.32431/kace.2024.27.4.002 · Cited by 6 · see citing works
Large language models and the problem of rhetorical debt
MacArthur, M. (2025) · AI & SOCIETY · doi:10.1007/s00146-025-02403-w · Cited by 8 · see citing works
The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers
Prather, J., Reeves, B., Leinonen, J., MacNeil, S., Randrianasolo, A. S., Becker, B., Kimmel, B., Wright, J., & Briggs, B. (2024) · arXiv · Cited by 196 · see citing works
“This has raised concerns from educators about potential student over-reliance [6, 29, 61, 72].”
Self-citationStudents' Attitudes Towards Cheating Before and After ChatGPT
Kann, V. (2025) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3724363.3729108 · Cited by 5 · see citing works
Zastudil, C., Holyfield, C., Kapp, C., Hamilton, K., Baru, K., Newsam, L., Smith, J. A., & MacNeil, S. (2025) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3663547.3746384 · Cited by 5 · see citing works
Self-citationUnderstanding Programming Students' Help-Seeking Preferences in the Era of Generative AI
Penney, J., Acharya, P., Hilbert, P., Parekh, P., Sarma, A., Steinmacher, I., & Gerosa, M. A. (2025) · ACM Global Computing Education Conference (CompEd) · doi:10.1145/3736181.3747165 · Cited by 7 · see citing works
How Does UML Look and Sound? Using AI to Interpret UML Diagrams Through Multimodal Evidence
Gavric, A., Bork, D., & Proper, H. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-75599-6_14 · Cited by 5 · see citing works
Gumabay, C. A. N., & Gumabay, M. V. N. (2024) · International Journal of Management and Humanities · doi:10.35940/ijmh.d1769.11041224 · Cited by 5 · see citing works
Mohammed, C., & Sarjusingh, W. (2024) · 2024 IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie61694.2024.10893028 · Cited by 5 · see citing works
Leveraging Undergraduate Perspectives to Redefine AI Literacy
Ebert, J., & Kramarczuk, K. (2025) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3641554.3701916 · Cited by 6 · see citing works
Oh, S.-Y., Cao, Y. L., Katz, A., & Zhao, J. (2024) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie61694.2024.10893102 · Cited by 5 · see citing works
Maximizing Student Engagement in Coding Education with Explanatory AI
Adhikari, B., Dhakal, S., & Jha, A. (2024) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie61694.2024.10893108 · Cited by 4 · see citing works
Kamau, J. N. (2025) · African Journal of Business and Development Studies · doi:10.70641/ajbds.v1i2.135 · Cited by 3 · see citing works
Enhancing Educational Methodology: Generative AI within the Project-Based Learning
Kaloyanova, K. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-11509-6_9 · Cited by 3 · see citing works
Kapoor, A., Denny, P. C., Porter, L., MacNeil, S., & Diaz, M. (2026) · Australasian Computing Education Conference · doi:10.1145/3786228.3786233 · Cited by 3 · see citing works
Self-citationReeping, D. P., & Shah, A. (2024) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--47047 · Cited by 3 · see citing works
Comparing lesson plan-driven and ask-me-anything chatbots: teaching a UNIX Shell course
Berengueres, J. O. L. (2025) · Discover Education · doi:10.1007/s44217-025-00492-9 · Cited by 3 · see citing works
Saari, M., Rantanen, P., Nurminen, M., Kilamo, T., Systä, K., & Abrahamsson, P. (2024) · Springer eBooks · doi:10.1007/978-3-031-55642-5_16 · Cited by 3 · see citing works
Characterizing Computing Students' Use of Generative AI
Lyons, M., Deitrick, E., & Ball, J. (2024) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--48453 · Cited by 3 · see citing works
Customizing ChatGPT to Help Computer Science Principles Students Learn Through Conversation
Frazier, M., Damevski, K., & Pollock, L. (2024) · Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1 · doi:10.1145/3649217.3653570 · Cited by 2 · see citing works
Mohammed, C. (2025) · IEEE World Engineering Education Conference (EDUNINE) · doi:10.1109/edunine62377.2025.10980845 · Cited by 3 · see citing works
Mohammed, C., Sarjusingh, W., & Rocke, S. (2025) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon62633.2025.11016366 · Cited by 3 · see citing works
Generative AI for Code Development: Advancements, Limitations, and Prospects
Krichen, M., & Abdalzaher, M. S. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-90573-5_7 · Cited by 3 · see citing works
Ivković, R., Vučković, D., Ječmenić, M., Ananieva, D., Stanković, M., Petković, M., & Janković, N. (2025) · International Journal of Cognitive Research in Science Engineering and Education · doi:10.23947/2334-8496-2025-13-2-551-562 · Cited by 2 · see citing works
Gao, H., Kumar, J., Kumari, J., Huang, J., & Kumar, D. (2026) · Scientific Reports · doi:10.1038/s41598-026-46175-9 · Cited by 3 · see citing works
GPT AI in Computer Science Education: A Systematic Mapping Study
Strik, B., Menolli, A., & Brancher, J. D. (2024) · Anais do ... Simpósio Brasileiro de Informática na Educação/Anais do Simpósio Brasileiro de Informática na Educação · doi:10.5753/sbie.2024.242103 · Cited by 2 · see citing works
Kasinidou, M., Kleanthous, S., & Otterbacher, J. (2024) · ACM Virtual Global Computing Education Conference (SIGCSE Virtual) · doi:10.1145/3649165.3690113 · Cited by 2 · see citing works
Glantz, E. J., Peca, J. C., Nasereddin, M., Stager, S. J., & Bartolacci, M. R. (2024) · Conference on Information Technology Education · doi:10.1145/3686852.3687066 · Cited by 2 · see citing works
Cho, C., & Ofosu-Anim, D. O. (2024) · Open Journal of Leadership · doi:10.4236/ojl.2024.134027 · Cited by 2 · see citing works
Exam reform: an opportunity for the redistribution of academic power
Crowther, G. J., & Wiggins, B. L. (2024) · Journal of Microbiology and Biology Education · doi:10.1128/jmbe.00119-24 · Cited by 2 · see citing works
Pang, A., Padiyath, A., Vargas, D. V., & Ericson, B. J. (2024) · ACM Virtual Global Computing Education Conference (SIGCSE Virtual) · doi:10.1145/3649165.3690099 · Cited by 1 · see citing works
Frydenberg, M., Xu, A., & Xu, J. (2025) · Information Systems Education Journal · doi:10.62273/dvnq9288 · Cited by 1 · see citing works
Kapur, I., Sharkey, C. N., & Ren, C. (2025) · Journal of Evidence-Based Social Work · doi:10.1080/26408066.2025.2517073 · Cited by 3 · see citing works
Game-Based Learning in the AI Era
Furwana, D., Wisran, W., Husnaini, H., & Asrifan, A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1062-6.ch009 · Cited by 1 · see citing works
Bego, C. R., Trytten, D., & Nwokeji, J. C. (2025) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie63693.2025.11328287 · Cited by 1 · see citing works
One Line at a Time: Scaffolding Reflective Code Evaluation through Structured AI Assistance
Qiu, V., Parker, L., Riegel, K., Giacaman, N., Denny, P. C., MacNeil, S., & Prather, J. E. (2026) · Australasian Computing Education Conference · doi:10.1145/3786228.3786243 · Cited by 1 · see citing works
Self-citationDeveloping Computing Teacher Guidance on GenAI
Sentance, S., Watson, S., Addo, S. A., Shi, S., Waite, J., & Yu, B. (2024) · Conference on United Kingdom & Ireland Computing Education Research (UKICER) · doi:10.1145/3689535.3689538 · Cited by 1 · see citing works
Research on the Application of Large Language Models in Programming Tutoring
Zhu, Z., Du, S., & Qiu, Y. (2024) · IEEE International Conference on Advanced Infocomm Technology · doi:10.1109/icait62580.2024.10807982 · Cited by 1 · see citing works
Enhancing Student Learning in Scrum Projects with Generative AI Assistance
Boubakri, M., & Nafil, K. (2024) · International Conference on Information Technology Based Higher Education and Training (ITHET) · doi:10.1109/ithet61869.2024.10837605 · Cited by 3 · see citing works
Incremental Repair Feedback on Automated Assessment of Programming Assignments
Paiva, J. C., Leal, J. P., & Figueira, Á. (2025) · Electronics · doi:10.3390/electronics14040819 · Cited by 1 · see citing works
Gleeson, M. (2025) · European Journal of Open Distance and E-Learning · doi:10.2478/eurodl-2025-0004 · Cited by 1 · see citing works
WIP: Generative AI as an Instructional Resource in a Computer Science Ethics Course
Barlowe, S., Aoulou, D., & Ponce-Castillo, A. (2024) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie61694.2024.10892995 · Cited by 1 · see citing works
Namanya, D., & Talibong, M. P. (2025) · Pan-African journal of education and social sciences · doi:10.56893/pajes2025v06i01.03 · Cited by 1 · see citing works
ChatGPT Calls for Self Reflection: Student Perceptions of Evaluation Activities in Video
Hayashi, V. T., & Paiva, H. M. (2024) · IEEE International Conference on Teaching, Assessment, and Learning for Engineering · doi:10.1109/tale62452.2024.10834346
A Field Study on the Use of Gen AI to Support Computing Education
Ocay, A. B., & Rodrigo, M. M. T. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93724-8_17 · Cited by 1 · see citing works
Electrical and Computer Engineering Freshmen and Generative AI: Awareness, Attitudes, and Ethics
Mohammed, C., & Rocke, S. (2025) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon62633.2025.11016545
- Authors
- Zastudil, C., Rogalska, M., Kapp, C., Vaughn, J., & MacNeil, S.
- Affiliations
- Temple University (US)
- Venue
- 2023 IEEE Frontiers in Education Conference (FIE)
- Publisher
- IEEE
- Published
- 2023-10-18
- Pages
- 1-9
- ISSN
- 0190-5848 (ISSN-L)
- OpenAlex
- W4390621457
- Cited by
- 136
- References
- 47
- Citations by year
- 2026: 32 · 2025: 59 · 2024: 41 · 2023: 4
- FWCI
- 41.13
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Frontiers in Education Conference (FIE) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- generative AI, computing education, student perceptions, teacher perceptions, code generation, programming education, AI integration in classrooms, curriculum development, assessment methods, teaching practice, educational technology adoption
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
