Cheating, Chatbots, and Change: A Bibliometric Mapping of Academic Integrity and Generative AI in Higher Education
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Abstract
Background and Aim: Generative artificial intelligence (GenAI) tools such as ChatGPT have rapidly entered higher education, reshaping how students write, learn, and are assessed, while intensifying concerns about plagiarism, contract cheating, and academic misconduct. Despite fast-growing commentary and empirical work, bibliometric evidence remains limited on how research at the intersection of academic integrity, GenAI, and higher education has organized itself in the post-ChatGPT period. This study maps publication volume, thematic structure, collaboration patterns, and early visibility indicators in this integrity-focused GenAI literature from 2023 to 2025.
Materials and Methods: A bibliometric and science-mapping design was applied to records retrieved from The Lens (Lens.org) using a misconduct-salient query combining academic integrity terms, GenAI terms (e.g., generative AI, ChatGPT), higher education, and misconduct constructs (plagiarism, cheating, misconduct). Data were extracted on October 30, 2025. After screening and cleaning, 303 records were retained. Descriptive indicators were computed, and VOSviewer was used to generate keyword co-occurrence and co-authorship networks; influential publications were identified using Lens scholarly citation counts.
Results: Publication output increased sharply from 2023, with substantial growth through 2025, although 2025 items had less time to accrue citations at extraction. Keyword mapping revealed four dominant clusters: integrity and misconduct, GenAI tool use and academic writing, student perceptions with population descriptors and selected discipline-linked terms, and ethics and governance. Collaboration patterns showed a small connected core among repeat contributors alongside a larger periphery of single or weakly connected authors. Source and citation indicators suggest that early visibility is anchored in a limited set of integrity-focused and education/educational technology outlets, with highly cited contributions dominated by early synthesis and framing work, alongside studies focused on assessment and detection. Country-level patterns should be interpreted cautiously because affiliation metadata were missing for many records; geographic findings, therefore, reflect the known-affiliation subset rather than a complete global distribution.
Conclusion: In the first three years after ChatGPT’s release, integrity-focused GenAI research in higher education expanded rapidly and developed a recognizable thematic and network structure. The field remains strongly shaped by misconduct framings, but the map also shows branching toward student experience and governance, supporting the need for broader collaboration and evidence-informed institutional guidance.
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References
Abdaljaleel, M., Barakat, M., Alsanafi, M., Salim, N. A., Abazid, H., Malaeb, D., Mohammed, A. H., Hassan, B. A. R., Wayyes, A. M., Farhan, S. S., Khatib, S. E., Rahal, M., Sahban, A., Abdelaziz, D. H., Mansour, N. O., & Sallam, M. (2024). A multinational study on the factors influencing university students’ attitudes and usage of ChatGPT. Scientific Reports, 14, Article 1983. https://doi.org/10.1038/s41598-024-52549-8
Ajalo, E., Mukunya, D., Nantale, R., Kayemba, F., Pangholi, K., Babuya, J., Akuu, S. L., Namiiro, A. M., Nsubuga, Y. B., Mpagi, J. L., Musaba, M. W., Oguttu, F., Kuteesa, J., Mubuuke, A. G., Munabi, I. G., & Kiguli, S. (2025). Widespread use of ChatGPT and other artificial intelligence tools among medical students in Uganda: A cross-sectional study. PLOS ONE, 20(1), e0313776. https://doi.org/10.1371/journal.pone.0313776
Balalle, H. M., & Pannilage, U. (2025). Reassessing academic integrity in the age of AI: A systematic literature review on AI and academic integrity. Social Sciences & Humanities Open, 11, 101299. https://doi.org/10.1016/j.ssaho.2025.101299
Chan, C. K. Y. (2024). Students’ perceptions of “AI-giarism”: Investigating changes in understandings of academic misconduct. Education and Information Technologies, 30, 8087–8108. https://doi.org/10.1007/s10639-024-13151-7
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating? Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International. Advance online publication. https://doi.org/10.1080/14703297.2023.2190148
Crawford, J., Cowling, M., & Allen, K.-A. (2023). Leadership is needed for ethical ChatGPT: Character, assessment, and learning using artificial intelligence (AI). Journal of University Teaching and Learning Practice, 20(3), Article 2. https://doi.org/10.53761/1.20.3.02
Črček, N., & Patekar, J. (2023). ChatGPT in education: Methods, potentials, and limitations. Journal of Contemporary Educational Research, 7(2), 1–7. https://doi.org/10.26689/jcer.v7i2.5543
Elkhatat, A. M., Elsaid, K., & Almeer, S. (2023). Evaluating the efficacy of AI content detection tools in differentiating between human and AI-generated text. International Journal for Educational Integrity, 19, Article 17. https://doi.org/10.1007/s40979-023-00140-5
Elom, C. O., Ayanwale, M. A., Ukeje, I. O., Offiah, G. A., Umoke, C. C., & Ogbonnaya, C. E. (2025). Does AI knowledge encourage cheating? Investigating student perceptions, ethical engagement, and academic integrity in the digital age. International Journal of Learning, Teaching and Educational Research. Advance online publication. https://www.ijlter.org/index.php/ijlter/article/view/12896
Espinoza Vidaurre, S. M., Velásquez Rodríguez, N. C., Gambetta Quelopana, R. L., Martínez Valdivia, A. N., Leo Rossi, E. A., & Nolasco-Mamani, M. A. (2024). Perceptions of artificial intelligence and its impact on academic integrity among university students in Peru and Chile: An approach to sustainable education. Sustainability, 16(20), 9005. https://doi.org/10.3390/su16209005
Estaphan, S., Witchel, H. J., & Kramer, D. (2025). Navigating the frontier of AI-assisted student assignments: Challenges, skills, and solutions. American Journal of Physiology – Advances in Physiology Education, 49(3). https://doi.org/10.1152/advan.00253.2024
Evangelista, E. (2025). Ensuring academic integrity in the age of ChatGPT: Rethinking exam design, assessment strategies, and ethical AI policies in higher education. Contemporary Educational Technology, 17, ep539. https://doi.org/10.30935/cedtech/15775
Farinosi, M., & Melchior, C. (2025). To adopt or to ban? Student perceptions and use of generative AI in higher education. Humanities and Social Sciences Communications, 12, Article 1684. https://doi.org/10.1057/s41599-025-05982-7
Farrelly, T., & Baker, N. (2023). Generative artificial intelligence: Implications and considerations for higher education practice. Education Sciences, 13(11), 1109. https://doi.org/10.3390/educsci13111109
Imran, M., & Almusharraf, N. (2023). Analyzing the role of ChatGPT as a writing assistant at higher education level: A systematic review of the literature. Contemporary Educational Technology, 15(4), ep464. https://doi.org/10.30935/cedtech/13605
Johnston, H., Wells, R., Shanks, E., Boey, T., & Parsons, B. (2024). Student perspectives on the use of generative artificial intelligence technologies in higher education. International Journal for Educational Integrity, 20, Article 14. https://doi.org/10.1007/s40979-024-00149-4
King, M. R., & chatGPT. (2023). A conversation on artificial intelligence, chatbots, and plagiarism in higher education. Cellular and Molecular Bioengineering, 16(1), 1–2. https://doi.org/10.1007/s12195-022-00754-8
Kofinas, A., Tsay, C., & Pike, D. (2025). The impact of generative AI on the academic integrity of authentic assessments within a higher education context. British Journal of Educational Technology. Advance online publication. https://doi.org/10.1111/bjet.13585
Li, M., Enkhtur, A., Cheng, F., & Yamamoto, B. (2023). Ethical implications of ChatGPT in higher education: A scoping review [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2311.14378
Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410. https://doi.org/10.3390/educsci13040410
Michel-Villarreal, R., Vilalta-Perdomo, E., Salinas-Navarro, D. E., Thierry-Aguilera, R., & Gerardou, F. S. (2023). Challenges and opportunities of generative AI for higher education as explained by ChatGPT. Education Sciences, 13(9), Article 856. https://doi.org/10.3390/educsci13090856
Moorhouse, B. L., Wong, K. M., & Walsh, S. (2023). Generative AI tools and assessment: Guidelines of the world’s top-ranking universities. Computers and Education: Open, 4, 100110. https://doi.org/10.1016/j.caeo.2023.100110
Moya Figueroa, B., Eaton, S., Pethrick, H., Hayden, A., Brennan, R., Wiens, J., & McDermott, B. (2024). Academic integrity and artificial intelligence in higher education contexts: A rapid scoping review. Canadian Perspectives on Academic Integrity, 7, Article 3. https://doi.org/10.55016/ojs/cpai.v7i3.78123
Nadim, M. A., & Di Fuccio, R. (2025). Unveiling the potential: Artificial intelligence’s negative impact on teaching and research, considering ethics in higher education. European Journal of Education, 60(1), e12929. https://doi.org/10.1111/ejed.12929
Ng, S., & Ho, C. S. (2025). Generative AI in education: Mapping the research landscape through bibliometric analysis. Information, 16(8), 413. https://doi.org/10.3390/info16080657
Perkins, M. (2023). Academic integrity considerations of AI large language models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching and Learning Practice, 20(2), Article 7. https://doi.org/10.53761/1.20.02.07
Perkins, M., Roe, J., Postma, D., McGaughran, J., & Hickerson, D. (2023). Detection of GPT-4 generated text in higher education: Combining academic judgement and software to identify generative AI tool misuse. Journal of Academic Ethics, 22, 89–113. https://doi.org/10.1007/s10805-023-09492-6
Preiksaitis, C., & Rose, C. (2023). Opportunities, challenges, and future directions of generative artificial intelligence in medical education: Scoping review. JMIR Medical Education, 9(1), e48785. https://doi.org/10.2196/48785
Premat, C., & Farazouli, A. (2025). Academic integrity vs. artificial intelligence: A tale of two AIs. Praxis Educativa, 20, e1670. https://doi.org/10.5212/praxeduc.v.20.24871.016
Sadasivan, V. S., Kumar, A., Balasubramanian, S., Wang, W., & Feizi, S. (2023). Can AI-generated text be reliably detected? [Preprint]. arXiv. https://arxiv.org/abs/2303.11156
Song, N. (2024). Higher education crisis: Academic misconduct with generative AI. Journal of Contingencies and Crisis Management, 32(1), 58–68. https://doi.org/10.1111/1468-5973.12532
Symeou, L., Louca, L., Kavadella, A., Mackay, J., Danidou, Y., & Raffay, V. (2025). Development of evidence-based guidelines for the integration of generative AI in university education through a multidisciplinary, consensus-based approach. European Journal of Dental Education, 29(2), 285–303. https://doi.org/10.1111/eje.13010
Ventayen, R. J. M. (2023). OpenAI ChatGPT generated results: Similarity index of artificial intelligence-based contents [Preprint]. SSRN. https://doi.org/10.2139/ssrn.4332664
Von Garrel, J., & Mayer, J. (2023). Artificial intelligence in studies—Use of ChatGPT and AI-based tools among students in Germany. Humanities and Social Sciences Communications, 10, 799. https://doi.org/10.1057/s41599-023-02304-7
Wan, Y., & Moorhouse, B. L. (2025). Challenges of AI in language education. In B. L. Moorhouse & Y. Wan (Eds.), Insights into AI and language teaching and learning (pp. 32–52). Castledown Publishers.
Weber-Wulff, D., Möller, C., & Rösner, F. (2023). Testing of detection tools for AI-generated text. International Journal for Educational Integrity, 19, Article 9. https://doi.org/10.1007/s40979-023-00130-5
Yusra, S. R., & Hanifa, R. (2025). Unveiling Indonesian higher education students’ English academic writing misconduct in the era of technology and AI: Comprehension vs. practice. World Journal of English Language, 15(5), 220–220. https://doi.org/10.5430/wjel.v15n5p220
