It Helps with Crap Lecturers and Their Low Effort: Investigating Computer Science Students’ Perceptions of Using ChatGPT for Learning
Open accessAbstract
This study explores how computing science students (n = 335) use ChatGPT, their trust in its information, their navigation of plagiarism issues, and their confidence in addressing plagiarism and academic integrity. A mixed-methods approach was used, combining quantitative survey data with a qualitative thematic analysis of student comments to provide a comprehensive understanding of these issues. The findings reveal that ChatGPT has become integral to students’ academic routines, with 87.8% using it weekly with variable frequency. Most students (70.3%) believe the university should allow ChatGPT use, and 66.6% think it is fair to use it for academic purposes despite 57.4% distrusting its information. Additionally, 53.8% worry about accidentally plagiarising when using ChatGPT. Overall, students have moderate confidence in addressing these academic integrity issues, with no differences between undergraduate and postgraduate students. Male students reported higher confidence in handling plagiarism and academic integrity issues than female students, suggesting underlying differences in how students perceive and interact with generative AI technologies. A thematic analysis of 74 student comments on their ChatGPT experience revealed four themes: (a) Usage and Role of ChatGPT, (b) Ethical and Responsible Use, (c) Limitations and Accuracy, and (d) Impact on Education and Need for Clear Guidelines. This study contributes to the ongoing debate on accepting and using ChatGPT, highlighting the need for institutions to provide clear guidelines and ethical considerations to ensure responsible use within educational contexts.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Educators are introducing AI ethics education to address bias, transparency, and accountability in algorithmic decision-making.
“educators are introducing AI ethics education to address issues such as bias, transparency, and accountability in algorithmic decision-making (Bikanga Ada, 2024; Haroud & Saqri, 2025; Valdivieso & González, 2025; Z. Wang et al., 2025).”
From Six Institutional Intervention Areas to Support Ethical and Effective Student Use of Generative AI in Higher Education: A Narrative Review (2026) · cites this paper as Bikanga Ada, 2024
Sections 5.6.2 and 5.6.3 recommend AI-literacy workshops in which students assess bias and discuss ethical use of ChatGPT. They do not establish that educators are already introducing such education, or that it addresses transparency and accountability in algorithmic decision-making. Page numbers are unavailable in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredBikanga Ada [43] found that students were aware of these ethical dilemmas posed by GenAI tools and wanted more comprehensive policies and educational programs to help navigate these challenges.
“A study by Bikanga Ada [43] found that students were aware of these ethical dilemmas posed by GenAI tools and wanted more comprehensive policies and educational programs to help navigate these challenges.”
From Navigating the Ethical and Societal Impacts of Generative AI in Higher Computing Education (2025) · cites this paper as [43]
Bikanga Ada [43] found that over half the respondents were concerned about accidentally plagiarizing when using ChatGPT.
“A study by Bikanga Ada [43] found that over half the respondents were concerned about accidentally plagiarizing when using ChatGPT.”
From Navigating the Ethical and Societal Impacts of Generative AI in Higher Computing Education (2025) · cites this paper as [43]
Such practices undermine the intended learning objectives of assessments, as students may pass a course by mastering the use of GenAI tools rather than through understanding the content.
“Such practices undermine the intended learning objectives of assessments, as students may pass a course by mastering the use of GenAI tools rather than through understanding the content [43].”
From Navigating the Ethical and Societal Impacts of Generative AI in Higher Computing Education (2025) · cites this paper as [43]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Using thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414 · see citing works
Cohen, J. (1992) · Current Directions in Psychological Science · doi:10.1111/1467-8721.ep10768783 · Cited by 5,870 · 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
“Artificial Intelligence (AI) has been a focal point of increased interest and research in recent years, leading to its adoption across various institutions for diverse purposes, including administrative tasks, instruction, and learning [1].”
Ray, P. P. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.04.003 · Cited by 2,463 · cited 1× · see citing works
“Ray’s [42] literature review highlighted ethical concerns, data biases, and safety issues despite the widespread acceptance of ChatGPT.”
What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature
Lo, C. K. (2023) · Education Sciences · doi:10.3390/educsci13040410 · Cited by 2,024 · see citing works
How to Construct a Mixed Methods Research Design
Schoonenboom, J., & Johnson, R. B. (2017) · KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie · doi:10.1007/s11577-017-0454-1 · Cited by 1,757 · see citing works
Grassini, S. (2023) · Education Sciences · doi:10.3390/educsci13070692 · Cited by 1,048 · cited 6× · see citing works
“Human instructors can adjust their explanations based on student feedback, provide real-time clarification, and engage in dialogue, which AI tools cannot replicate effectively [60–63], at least now.”
InfluentialChatGPT in higher education: considerations for academic integrity and student learning
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.17 · Cited by 853 · cited 4× · see citing works
“Such measures are essential given the growing concerns regarding academic integrity, including plagiarism, in higher education [2–4].”
InfluentialImpact of the Implementation of ChatGPT in Education: A Systematic Review
Montenegro-Rueda, M., Fernández-Cerero, J., Fernández-Batanero, J. M., & López-Meneses, E. (2023) · Computers · doi:10.3390/computers12080153 · Cited by 509 · cited 1× · see citing works
“As highlighted in previous studies, this uncertainty may stem from genAI tools occasionally providing inaccurate or incomplete information [7,71,72].”
Generative AI tools and assessment: Guidelines of the world's top-ranking universities
Moorhouse, B. L., Yeo, M. A., & Wan, Y. (2023) · Computers and Education Open · doi:10.1016/j.caeo.2023.100151 · Cited by 461 · see citing works
Will generative AI replace teachers in higher education? A study of teacher and student perceptions
Chan, C. K. Y., & Tsi, L. H. Y. (2024) · Studies in Educational Evaluation · doi:10.1016/j.stueduc.2024.101395 · Cited by 387 · cited 1× · see citing works
“Human instructors can adjust their explanations based on student feedback, provide real-time clarification, and engage in dialogue, which AI tools cannot replicate effectively [60–63], at least now.”
Academic integrity: A review of the literature
Macfarlane, B., Zhang, J., & Pun, A. (2014) · Studies in Higher Education · doi:10.1080/03075079.2012.709495 · Cited by 359 · see citing works
Malinka, K., Peresíni, M., Firc, A., Hujnák, O., & Janus, F. (2023) · Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1 · doi:10.1145/3587102.3588827 · Cited by 287 · cited 1× · see citing works
“Malinka et al. [34] and research by [35] discuss ethical concerns, with [34] explicitly addressing the risk of cheating and plagiarism in computer security education.”
Mollick, E. R., & Mollick, L. (2023) · doi:10.2139/ssrn.4391243 · Cited by 278 · see citing works
Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education
Smolansky, A., Cram, A., Raduescu, C., Zeivots, S., Huber, E., & Kizilcec, R. F. (2023) · Proceedings of the Tenth ACM Conference on Learning @ Scale · doi:10.1145/3573051.3596191 · Cited by 275 · cited 5× · see citing works
“This aligns with research emphasising the importance of designing assessments that test cognitive processes beyond the capabilities of genAI tools [15,67–70].”
InfluentialSalinas-Navarro, D. E., Vilalta-Perdomo, E., Michel-Villarreal, R., & Montesinos, L. (2024) · Education Sciences · doi:10.3390/educsci14010083 · Cited by 193 · see citing works
Singh, H., Tayarani-Najaran, M.-H., & Yaqoob, M. (2023) · Education Sciences · doi:10.3390/educsci13090924 · Cited by 150 · cited 3× · see citing works
“This finding aligns with previous studies that have emphasised the need for clear guidelines and training on how to use AI tools responsibly [2,39,63].”
Race with the machines: assessing the capability of generative AI in solving authentic assessments
Nguyen Thanh, B., Vo, D. T. H., Nguyen Nhat, M., Pham, T. T. T., Thai Trung, H., & Ha Xuan, S. (2023) · Australasian Journal of Educational Technology · doi:10.14742/ajet.8902 · Cited by 87 · see citing works
Investigating and Designing for Trust in AI-powered Code Generation Tools
Wang, R., Cheng, R., Ford, D., & Zimmermann, T. (2024) · The 2024 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3630106.3658984 · Cited by 87 · see citing works
Flagged first, then by citations
Research works printed but not linked: 53
- Jarrah, A.M.; Wardat, Y.; Fidalgo, P. Using ChatGPT in academic writing is (not) a form of plagiarism: What does the literature say? Online J. Commun. Media Technol. 2023, 13, e202346. [CrossRef] · DOI · Paper page
- Gorichanaz, T. Accused: How students respond to allegations of using ChatGPT on assessments. Learn. Res. Pract. 2023, 9, 183-196. [CrossRef] · DOI · Paper page
- Roberts, T.S. (Ed.) Student Plagiarism in an Online World: Problems and Solutions; IGI Global: Hershey, PA, USA, 2008. [CrossRef] · DOI
- Dawson, M.M.; Overfield, J.A. Plagiarism: Do Students Know What It Is? Biosci. Educ. 2006, 8, 1-15. [CrossRef] · DOI
- Farazouli, A.; Cerratto-Pargman, T.; Bolander-Laksov, K.; McGrath, C. Hello GPT! Goodbye Home Examination? An Exploratory Study of AI Chatbots' Impact on University Teachers' Assessment Practices. Assess. Eval. High. Educ. 2023, 49, 363-375. [CrossRef] · DOI · Paper page
- De Maio, C.; Dixon, K.; Yeo, S. Academic Staff Responses to Student Plagiarism in Universities: A Literature Review from 1990 to 2019. Issues Educ. Res. 2019, 29, 1131-1142. Available online: http://www.iier.org.au/iier29/demaio.pdf (accessed on 26 September 2024).
- Xie, Y.; Wu, S.; Chakravarty, S. AI meets AI: Artificial Intelligence and Academic Integrity-A Survey on Mitigating AI-Assisted Cheating in Computing Education. In Proceedings of the 24th Annual Conference on Information Technology Education (SIGITE' 23), Marietta, GA, USA, 11-14 October 2023; Association for Computing Machinery: New York, NY, USA, 2023; pp. 79-83. [CrossRef] · DOI
- Cojocariu, V.M.; Mareş, G. Academic Integrity in the Technology-Driven Education Era. In Ethical Use of Information Technology in Higher Education; Mât , ă, L., Ed.; EAI/Springer Innovations in Communication and Computing; Springer: Singapore, 2022. [CrossRef] · DOI
- McHaney, R.; Cronan, T.P.; Douglas, D.E. Academic Integrity: Information Systems Education Perspective. J. Inf. Syst. Educ. 2016, 27, 153-158. Available online: https://aisel.aisnet.org/jise/vol27/iss3/1/ (accessed on 26 September 2024).
- Fenu, G.; Galici, R.; Marras, M.; Reforgiato, D. Exploring Student Interactions with AI in Programming Training. In Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (UMAP Adjunct' 24), Cagliari, Italy, 1-4 July 2024; Association for Computing Machinery: New York, NY, USA, 2024; pp. 555-560. [CrossRef] · DOI
- Lim, J.W.Z.; Thing, V.L.L. Building confidence in a world of eroding trust. Digit. Gov. Res. Pract. 2024, 5, 1-17. [CrossRef] · DOI
- Xu, L. The Dilemma and Countermeasures of AI in Educational Application. In Proceedings of the 2020 4th International Conference on Computer Science and Artificial Intelligence (CSAI' 20), Zhuhai, China, 11-13 December 2021; Association for Computing Machinery: New York, NY, USA, 2021; pp. 289-294. [CrossRef] · DOI
- Dorton, S. Supradyadic Trust in Artificial Intelligence. Artif. Intell. Soc. Comput. 2022, 28, 92-100. [CrossRef] · DOI
- Kim, T.; Song, H. Communicating the Limitations of AI: The Effect of Message Framing and Ownership on Trust in Artificial Intelligence. Int. J. Hum. Comput. Interact. 2022, 39, 790-800. [CrossRef] · DOI
- Beauxis-Aussalet, E.; Behrish, M.; Borgo, R.; Chau, D.H.; Collins, C.; Ebert, D.; El-Assady, M.; Endert, A.; Keim, D.A.; Kohlhammer, J.; et al. The Role of Interactive Visualization in Fostering Trust in AI. IEEE Comput. Graph. Appl. 2021, 41, 7-12. [CrossRef] · DOI
- Amoozadeh, M.; Daniels, D.; Nam, D.; Kumar, A.; Chen, S.; Hilton, M.; Ragavan, S.S.; Alipour, M.A. Trust in Generative AI among Students: An exploratory study. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 24), Portland, OR, USA, 20-23 March 2024; Association for Computing Machinery: New York, NY, USA, 2024; pp. 67-73. [CrossRef] · DOI · Paper page
- Scholl, A.; Schiffner, D.; Kiesler, N. Analysing Chat Protocols of Novice Programmers Solving Introductory Programming Tasks with ChatGPT. arXiv 2024, arXiv:2405.19132. [CrossRef] · DOI
- McIntire, A.; Calvert, I.; Ashcraft, J. Pressure to Plagiarize and the Choice to Cheat: Toward a Pragmatic Reframing of the Ethics of Academic Integrity. Educ. Sci. 2024, 14, 244. [CrossRef] · DOI
- Maryon, T.; Dubre, V.; Elliott, K.; Escareno, J.; Fagan, M.H.; Standridge, E.; Lieneck, C. COVID-19 Academic Integrity Violations and Trends: A Rapid Review. Educ. Sci. 2022, 12, 901. [CrossRef] · DOI
- Šprajc, P.; Urh, M.; Jerebic, J.; Trivan, D.; Jereb, E. Reasons for Plagiarism in Higher Education. Organizacija 2017, 50, 33-45. [CrossRef] · DOI
- Kampa, R.K.; Padhan, D.K.; Karna, N.; Gouda, J. Identifying the Factors Influencing Plagiarism in Higher Education: An Evidence-Based Review of the Literature. Account. Res. 2024, 1-16. [CrossRef] · DOI
- Bretag, T. Chapter 1: Introduction to A Research Agenda for Academic Integrity: Emerging Issues in Academic Integrity Research. In A Research Agenda for Academic Integrity; Edward Elgar Publishing: Cheltenham, UK, 2020. [CrossRef] · DOI
- Yu, C.; Yan, J.; Cai, N. ChatGPT in higher education: Factors influencing ChatGPT user satisfaction and continued use intention. Front. Educ. 2024, 9, 1354929. [CrossRef] · DOI
- Zaineldeen, S.; Hongbo, L.; Koffi, A.L.; Hassan, B.M.A. Technology Acceptance Model Concepts, Contribution, Limitation, and Adoption in Education. Univ. J. Educ. Res. 2020, 8, 5061-5071. [CrossRef] · DOI
- Granić, A.; Marangunić, N. Technology Acceptance Model in Educational Context: A Systematic Literature Review. Br. J. Educ. Technol. 2019, 50, 2572-2593. [CrossRef] · DOI · Paper page
- Saif, N.; Khan, S.U.; Shaheen, I.; ALotaibi, F.A.; Alnfiai, M.M.; Arif, M. Chat-GPT: Validating Technology Acceptance Model (TAM) in Education Sector via Ubiquitous Learning Mechanism. Comput. Hum. Behav. 2024, 154, 108097. [CrossRef] · DOI · Paper page
- Park, N.; Lee, K.M.; Cheong, P.H. University Instructors' Acceptance of Electronic Courseware: An Application of the Technology Acceptance Model. J. Comput.-Mediat. Commun. 2007, 13, 163-186. [CrossRef] · DOI
- Qureshi, B. ChatGPT in computer science curriculum assessment: An analysis of its successes and shortcomings. In Proceedings of the 2023 9th International Conference on e-Society, e-Learning and e-Technologies (ICSLT' 23), Portsmouth, UK, 9-11 June 2023; Association for Computing Machinery: New York, NY, USA, 2023; pp. 7-13. [CrossRef] · DOI
- Rajala, J.; Hukkanen, J.; Hartikainen, M.; Niemelä, P. "\Call me Kiran\"-ChatGPT as a Tutoring Chatbot in a Computer Science Course. In Proceedings of the 26th International Academic Mindtrek Conference (Mindtrek' 23), Tampere, Finland, 3-6 October 2023; Association for Computing Machinery: New York, NY, USA, 2023; pp. 83-94. [CrossRef] · DOI
- Hassani, H.; Silva, E.S. The Role of ChatGPT in Data Science: How AI-Assisted Conversational Interfaces Are Revolutionising the Field. Big Data Cogn. Comput. 2023, 7, 62. [CrossRef] · DOI · Paper page
- Budhiraja, R.; Joshi, I.; Challa, J.S.; Akolekar, H.D.; Kumar, D. "It's not like Jarvis, but it's pretty close!"-Examining ChatGPT's Usage among Undergraduate Students in Computer Science. In Proceedings of the 26th Australasian Computing Education Conference (ACE '24), Sydney, Australia, 29 January-2 February 2024; Association for Computing Machinery: New York, NY, USA, 2024; pp. 124-133. [CrossRef] · DOI · Paper page
- Kim, Y.; Lee, J.; Kim, S.; Park, J.; Kim, J. Understanding Users' Dissatisfaction with ChatGPT Responses: Types, Resolving Tactics, and the Effect of Knowledge Level. In Proceedings of the 29th International Conference on Intelligent User Interfaces (IUI' 24), Greenville, SC, USA, 18-21 March 2024; Association for Computing Machinery: New York, NY, USA, 2024; pp. 385-404. [CrossRef] · DOI
- Joshi, I.; Budhiraja, R.; Dev, H.; Kadia, J.; Ataullah, M.O.; Mitra, S.; Akolekar, H.D.; Kumar, D. ChatGPT in the classroom: An analysis of its strengths and weaknesses for solving undergraduate computer science questions. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2024), Portland, OR, USA, 20-23 March 2024; Association for Computing Machinery: New York, NY, USA, 2024; pp. 625-631. [CrossRef] · DOI
- Yılmaz, R.; Yilmaz, F.G.K. The effect of generative artificial intelligence (AI)-based tool use on students' computational thinking skills, programming self-efficacy and motivation. Comput. Educ. Artif. Intell. 2023, 4, 100147. [CrossRef] · DOI · Paper page
- Gill, S.S.; Xu, M.; Patros, P.; Wu, H.; Kaur, R.; Kaur, K.; Fuller, S.; Singh, M.; Arora, P.; Parlikad, A.K.; et al. Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots. Internet Things Cyber-Phys. Syst. 2024, 4, 19-23. [CrossRef] · DOI
- Hariri, W. Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processing. arXiv 2023, arXiv:2304.02017. [CrossRef] · DOI
- Acosta-Enriquez, B.G.; Arbulú Ballesteros, M.A.; Huamaní Jordan, O.; Roca, C.L.; Tirado, K.S. Analysis of college students' attitudes toward the use of ChatGPT in their academic activities: Effect of intent to use, verification of information and responsible use. BMC Psych. 2024, 12, 255. [CrossRef] [PubMed] · DOI · Paper page
- Vargas-Murillo, A.R.; Pari-Bedoya, I.N.M.A.; Guevara-Soto, F.J. The Ethics of AI Assisted Learning: A Systematic Literature Review on the Impacts of ChatGPT Usage in Education. In Proceedings of the 2023 8th International Conference on Distance Education and Learning (ICDEL '23), Beijing, China, 9-12 June 2023; Association for Computing Machinery: New York, NY, USA, 2023; pp. 8-13. [CrossRef] · DOI
- Long, C.; Niedbala, E.; Kelley, A. Developing Human Trust in Automated Systems: The Central Role of Personal Control. In Academy of Management Proceedings; Academy of Management: Briarcliff Manor, NY, USA, 2021. [CrossRef] · DOI
- Lacity, M.C.; Schuetz, S.W.; Kuai, L.; Steelman, Z.R. IT's a Matter of Trust: Literature Reviews and Analyses of Human Trust in Information Technology. J. Inf. Technol. 2024, 2683962231226397. [CrossRef] · DOI
- Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Poniszewska-Mara ńda, A., Eds.; Lecture Notes in Networks and Systems; Springer: Cham, Switzerland, 2024; Volume 987, pp. 214-227. [CrossRef] · DOI
- Papadaki, M., Themistocleous, M., Al Marri, K., Al Zarouni, M., Eds.; Lecture Notes in Business Information Processing; Springer: Cham, Switzerland, 2024; Volume 501, pp. 291-307. [CrossRef] · DOI
- Kosar, T.; Ostojić, D.; Liu, Y.D.; Mernik, M. Computer Science Education in ChatGPT Era: Experiences from an Experiment in a Programming Course for Novice Programmers. Mathematics 2024, 12, 629. [CrossRef] · DOI
- Lester, J.N.; Cho, Y.; Lochmiller, C.R. Learning to Do Qualitative Data Analysis: A Starting Point. Hum. Resour. Dev. Rev. 2020, 19, 94-106. [CrossRef] · DOI
- Sharma, A. Neoliberal Etiology and Educational Failure: A Critical Exploration. Curric. Inq. 2021, 51, 542-561. [CrossRef] · DOI
- Denny, P.; MacKellar, C.; Amir, L.; Yang, L.; Lino, A.; Johnson, C. Can We Trust AI-Generated Educational Content? Comparative Analysis of Human and AI-Generated Learning Resources. arXiv 2023, arXiv:2306.10509. · arXiv
- Farrokhnia, M.; Banihashem, S.K.; Noroozi, O.; Wals, A. A SWOT Analysis of ChatGPT: Implications for Educational Practice and Research. Innov. Educ. Teach. Int. 2023, 61, 460-474. [CrossRef] · DOI · Paper page
- Triberti, S.; Di Fuccio, R.; Scuotto, C.; Marsico, E.; Limone, P. "Better than My Professor?" How to Develop Artificial Intelligence Tools for Higher Education. Front. Artif. Intell. 2024, 7, 1329605. [CrossRef] [PubMed] · DOI
- Botchu, B.; Iyengar, K.P.; Botchu, R. Can ChatGPT Empower People with Dyslexia? Disabil. Rehabil. Assist. Technol. 2023, 19, 2131-2132. [CrossRef] · DOI · Paper page
- Kazemitabaar, M.; Ye, R.; Wang, X.; Henley, A.Z.; Denny, P.; Craig, M.; Grossman, T. CodeAid: Evaluating a Classroom Deployment of an LLM-Based Programming Assistant that Balances Student and Educator Needs. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CHI '24), Association for Computing Machinery, New York, NY, USA, 11-16 May 2024; pp. 1-20. [CrossRef] · DOI
- Megahed, F.M.; Chen, Y.J.; Ferris, J.A.; Knoth, S.; Jones-Farmer, L.A. How Generative AI Models Such as ChatGPT Can Be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study. Qual. Eng. 2023, 36, 287-315. [CrossRef] · DOI
- Barrett, A.; Pack, A. Not Quite Eye to A.I.: Student and Teacher Perspectives on the Use of Generative Artificial Intelligence in the Writing Process. Int. J. Educ. Technol. High. Educ. 2023, 20, 59. [CrossRef] · DOI · Paper page
- Dickey, E.; Bejarano, A.; Garg, C. AI-Lab: A Framework for Introducing Generative Artificial Intelligence Tools in Computer Programming Courses. SN Comput. Sci. 2024, 5, 720. [CrossRef · DOI · Paper page
Cited by
Sorted by citations
Nelson, A. S., Santamaría, P. V., Javens, J. S., & Ricaurte, M. (2025) · Education Sciences · doi:10.3390/educsci15050611 · Cited by 79 · see citing works
Jeilani, A., & Abubakar, S. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1548900 · Cited by 64 · see citing works
Ursavaş, Ö. F., Yalçın, Y., İslamoğlu, H., Bakır-Yalçın, E., & Cukurova, M. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00535-z · Cited by 69 · see citing works
Mohammadi, E., Thelwall, M., Cai, Y., Collier, T., Tahamtan, I., & Eftekhar, A. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2025.104350 · Cited by 47 · see citing works
Kangwa, D., Msafiri, M. M., & Fute, A. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70109 · Cited by 22 · see citing works
Jayasinghe, S., Gamage, K. A. A., Yang, D., Cheng, C., Disanayake, C., & Apeji, U. D. (2026) · Education Sciences · doi:10.3390/educsci16010137 · Cited by 23 · 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
- Authors
- Bikanga Ada, M.
- Affiliations
- School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK
- Venue
- Education Sciences
- Publisher
- MDPI AG
- Published
- 2024-10-11
- Volume
- 14
- Issue
- 10
- Pages
- 1106
- ISSN
- 2227-7102 (ISSN-L)
- OpenAlex
- W4403339441
- Cited by
- 40
- References
- 19
- Citations by year
- 2026: 19 · 2025: 21
- FWCI
- 15.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Education Sciences (publishedVersion, cc-by)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (submittedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- University of Glasgow
- College of Science and Engineering, University of Minnesota
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- ChatGPT, generative AI in education, computer science students, student perceptions, academic integrity, plagiarism, responsible AI use, gender differences, undergraduate and graduate students
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
