Rethinking Plagiarism in the Era of Generative AI

Generative AI


Hutson, J. (2024). Rethinking Plagiarism in the Era of Generative AI. Journal of Intelligent Communication, 4(1), 20–31.


  • James Hutson
    Department of Art History and Visual Culture, Lindenwood University, Saint Charles, MO 63301, USA

The emergence of generative artificial intelligence (AI) technologies, such as large language models (LLMs) like ChatGPT, has precipitated a paradigm shift in the realms of academic writing, plagiarism, and intellectual property. This article explores the evolving landscape of English composition courses, traditionally designed to develop critical thinking through writing. As AI becomes increasingly integrated into the academic sphere, it necessitates a reevaluation of originality in writing, the purpose of learning research and writing, and the frameworks governing intellectual property (IP) and plagiarism. The paper commences with a statistical analysis contrasting the actual use of LLMs in academic dishonesty with educator perceptions. It then examines the repercussions of AI-enabled content proliferation, referencing the limitation of three books self-published per day in September 2023 by Amazon due to a suspected influx of AI-generated material. The discourse extends to the potential of AI in accelerating research akin to the contributions of digital humanities and computational linguistics, highlighting its accessibility to the general public. The article further delves into the implications of AI on pedagogical approaches to research and writing, contemplating its impact on communication and critical thinking skills, while also considering its role in bridging the digital divide and socio-economic disparities. Finally, it proposes revisions to writing curricula, adapting to the transformative influence of AI in academic contexts. 


plagiarism academic integrity generative artificial intelligence large language models originality digital humanities copyright intellectual property rights


  1. Nabee, S.G.; Mageto, J.; Pisa, N. Investigating Predictors of Academic Plagiarism among University Students. Int. J. Learning, Teaching Edu. Res. 2020, 19, 264–280.
  2. Bouville, M. Plagiarism: Words and Ideas. Sci. Eng. Eth. 2008, 14, 311–322.
  3. Ansorge, L.; Ansorgeová, K.; Sixsmith, M. Plagiarism through Paraphrasing Tools—the Story of One Plagiarized Text. Pub. 2021, 9, 48.
  4. Fenton, A.L.; Gralla C. Student plagiarism in higher education: A typology and remedial framework for a globalized era. In Academic Misconduct and Plagiarism; Editor Bernard Montoneri; Lexington Books: Maryland, USA, 2020. pp. 109.
  5. Dehouche, N. Plagiarism in the age of massive Generative Pre-trained Transformers (GPT-3). Eth. in Sci. Environ. Pol. 2021, 21, 17–23.
  6. Wahle, J.P.; Ruas, T.; Kirstein, F.; Gipp, B. How large language models are transforming machine-paraphrased plagiarism. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, December 2022.
  7. Assessing the Potential of LLM-assisted Annotation for Corpus-based Pragmatics and Discourse Analysis: The Case of Apologies. Available online: (accessed on 1 March 2024).
  8. Zaheer, M.; Guruganesh, G.; Dubey, K.A.; Ainslie, J.; Alberti, C.; Ontanon, S.; Pham, P.; Ravula, A.; Wang, Q.; Yang, L. et al. A. Big bird: Transformers for longer sequences. Adv. in Neural Inf. Processing Sys. 2020, 33, 17283–17297.
  9. Hadi, M.U.; Qureshi, R.; Shah, A.; Irfan, M.; Zafar, A.; Shaikh, M.B.; Akhtar, N.; Wu, J.; Mirjalili, S.; Shah, M. A survey on large language models: Applications, challenges, limitations, and practical usage. Authorea Prepr. 2023, 1, pp. 1–29.
  10. Jonsson, M.; Tholander, J. Cracking the code: Co-coding with AI in creative programming education. In Proceedings of the 14th Conference on Creativity and Cognition, Venice, Italy, June 2022.
  11. Arapoff, N. Writing: A thinking process. Tesol Q. 1967, 1, 33–39.
  12. Jambeck, K.K.; Winder, B.D. Vygotsky Werner, and English composition: Paradigms for thinking and writing. Writ. on Edge 1990, 1, 68–79.
  13. Spear, K.I. Thinking and writing: A sequential curriculum for composition. J. Adv. Compos. 1983, 4, 47–63.
  14. Sinaga, P.; Feranie, S. Enhancing critical thinking skills and writing skills through the variation in non-traditional writing task. Int. J. Instr. 2017, 10, 69–84.
  15. Ritchhart, R.; Church, M.; Morrison, K. Making thinking visible: How to promote engagement, understanding, and independence for all learners. John Wiley & Sons: New York, USA, 2011.
  16. Yeo, M.A. Academic integrity in the age of Artificial Intelligence (AI) authoring apps. TESOL J. 2023, 14, e716.
  17. An Empirical Study of AI Generated Text Detection Tools. Available online: (accessed on 17 March 2024)
  18. Walters, W.H. The effectiveness of software designed to detect AI-generated writing: A comparison of 16 AI text detectors. Open Inf. Sci. 2023, 7, 20220158.
  19. AI Writing Detection Update from Turnitin’s Chief Product Officer. Available online: (accessed on 18 March 2024)
  20. Chaka, C. Detecting AI content in responses generated by ChatGPT, YouChat, and Chatsonic: The case of five AI content detection tools. J. Appl. Learning Teaching 2023, 6, 1–11.
  21. Rashidi, H.H.; Fennell, B.D.; Albahra, S.; Hu, B.; Gorbett, T. The ChatGPT conundrum: Human-generated scientific manuscripts misidentified as AI creations by AI text detection tool. J. Pathol. Inf. 2023, 14, 100342.
  22. Oleksy, E. That thing ain’t human: The artificiality of “human authorship” and the intelligence in expanding copyright authorship to fully-autonomous AI. Clev. St. L. Rev. 2023, 72, 263.
  23. Human-AI Collaboration for Smart Education: Reframing Applied Learning to Support Metacognition. Available online: on 6 March 2024)
  24. ChatGPT Cheating Statistics & Impact on Education (2024). Available online: on 2 March 2024)
  25. Kreps, S.; George, J.; Lushenko, P.; Rao, A. Exploring the artificial intelligence “Trust paradox”: Evidence from a survey experiment in the United States. Plos one 2023, 18, e0288109.
  26. Southworth, J.; Migliaccio, K.; Glover, J.; Reed, D.; McCarty, C.; Brendemuhl, J.;Thomas, A. Developing a model for AI Across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Comput. Edu.: Artif. Intell. 2023, 4, 100127.
  27. Su, J.; Ng, D.T.K.; Chu, S.K.W. Artificial intelligence (AI) literacy in early childhood education: The challenges and opportunities. Comput. Edu.: Artif. Intell. 2023, 4, 100124.
  28. Kumar, V.; Verma, A.; Aggarwal, S.P. Reviewing academic integrity: Assessing the influence of corrective measures on adverse attitudes and plagiaristic behavior. J. Academic Eth. 2023, 21, 497–518.
  29. Putra, I.E.; Jazilah, N.I.; Adishesa, M.S.; Al Uyun, D.; Wiratraman, H.P. Denying the accusation of plagiarism: Power relations at play in dictating plagiarism as academic misconduct. High. Edu. 2023, 85, 979–997.
  30. Kleebayoon, A.; Wiwanitkit, V. Artificial intelligence, chatbots, plagiarism and basic honesty: comment. Cell. Mol. Bioeng. 2023, 16, 173–174.
  31. Cultural Conceptions of Intellectual Property: The Pirated Disc Market in Xi'An, China. Available online: (accessed on 1 August 2006)
  32. Grudecki, M.R. Plagiarism as a culturally-motivated crime. Asian J. Law Econ. 2021, 12, 237–252.
  33. Middle Eastern Cities: A Symposium on Ancient, Islamic, and Contemporary Middle Eastern Urbanism, 1st ed.; Lapidus, I.M., Ed.; University of California Press: CA, USA, 2022; pp. 220.
  34. Makarova, M. Factors of academic misconduct in a cross-cultural perspective and the role of integrity systems. J. Acad. Eth. 2019, 17, 51–71.
  35. Ison, D.C. An empirical analysis of differences in plagiarism among world cultures. J. High. Edu. Policy Manag. 2018, 40, 291–304.
  36. Leask, B. Plagiarism, cultural diversity and metaphor—implications for academic staff development. Assess. Eval. in High. Edu. 2006, 31, 183–199.
  37. Stoesz, B.M.; Eaton, S.E. Academic integrity policies of publicly funded universities in western Canada. Edu. Policy 2022, 36, 1529–1548.
  38. Amsberry, D. Deconstructing plagiarism: International students and textual borrowing practices. Ref. Librarian 2009, 51, 31–44.
  39. Gunnarsson, J.; Kulesza, W.J.; Pettersson, A. Teaching international students how to avoid plagiarism: Librarians and faculty in collaboration. J. Acad. Librariansh. 2014, 40, 413–417.
  40. Esplugas, M. The use of artificial intelligence (AI) to enhance academic communication, education and research: a balanced approach. J. Hand Surg. (EU Vol.) 2023, 48, 819–822.
  41. Zeichner, K.M. Beyond the divide of teacher research and academic research. Teachers Teaching 1995, 1, 153–172.
  42. Abdelrazek, A.; Eid, Y.; Gawish, E.; Medhat, W.; Hassan, A. Topic modeling algorithms and applications: A survey. Inf. Syst. 2023, 112, 102131.
  43. Heston, T.F.; Khun, C. Prompt engineering in medical education. Int. Med. Edu. 2023, 2, 198–205.
  44. Wagner, G.; Lukyanenko, R; Paré, G. Artificial intelligence and the conduct of literature reviews. J. Inf. Tech. 2022, 37, 209–226.
  45. Robledo, S.; Grisales, A.A.M.; Hughes, M.; Eggers, F. “Hasta la vista, baby”—will machine learning terminate human literature reviews in entrepreneurship? J. Small Bus. Manag. 2023, 61, 1314–1343.
  46. Chapinal-Heras, D.; Díaz-Sánchez, C. A review of AI applications in human sciences research. Digit. Appl. in Archaeol. Cult. Herit. 2023, E00288.
  47. Pinzolits, R. AI in academia: An overview of selected tools and their areas of application. MAP Edu. Humanit. 2024, 4, 37–50.
  48. Fui-Hoon Nah, F.; Zheng, R.; Cai, J.; Siau, K.; Chen, L. Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. J. Inf. Tech. Case Appl. Res. 2023, 25, 277–304.
  49. El-Guebaly, N.; Foster, J.; Bahji, A.; Hellman, M. The critical role of peer reviewers: Challenges and future steps. Nordic Stud. on Alcohol Drugs 2023, 40, 14–21.
  50. King, E.B.; Avery, D.R., Hebl, M.R.; Cortina, J.M.; Systematic subjectivity: How subtle biases infect the scholarship review process. J. Manag. 2018, 44, 843–853.
  51. Berlin, S. Reconsidering editorial consideration: Changing editorial assessment could reduce subjectivity in the publication process. EMBO rep. 2023, 24, e58127.
  52. Rigney, D. The Matthew effect: How advantage begets further advantage. Columbia University Press: Columbia, USA. 2010; pp. 176.
  53. Teixeira da Silva, J.A.; Dobránszki, J.; Bhar, R.H.; Mehlman, C.T. Editors should declare conflicts of interest. J. Bioeth. Inquiry 2019, 16, 279–298.
  54. Cheah, P.Y.; Piasecki, J. Should peer reviewers be paid to review academic papers? Lancet 2022, 399, 1601.
  55. Huisman, J.; Smits, J. Duration and quality of the peer review process: the author’s perspective. Scientometr. 2017, 113, 633–650.
  56. Lee, C.J.; Sugimoto, C.R.; Zhang, G.; Cronin, B. Bias in peer review. J. Am. Soc. for inf. Sci. Tech. 2013, 64, 2–17.
  57. Bancroft, S.F.; Ryoo, K.; Miles, M. Promoting equity in the peer review process of journal publication. Sci. Edu. 2022, 106, 1232–1248.
  58. Checco, A.; Bracciale, L.; Loreti, P.; Pinfield, S.; Bianchi, G. AI-assisted peer review. Humanit. Soc. Sci. Commun. 2021, 8, 1–11.
  59. Javed, S.; Adewumi, T.P.; Liwicki, F.S.; Liwicki, M. Understanding the role of objectivity in machine learning and research evaluation. Philos. 2021, 6, 22.
  60. Salah, M.; Abdelfattah, F.; Halbusi, H.A. Debate: Peer reviews at the crossroads—‘To AI or not to AI?’ Public Money Manag. 2023, 43, 781–782.
  61. Cerdá-Alberich, L.; Solana, J.; Mallol, P.; Ribas, G.; García-Junco, M.; Alberich-Bayarri, A.; Marti-Bonmati, L. MAIC-10 brief quality checklist for publications using artificial intelligence and medical images. Insights into Imag. 2023, 14, 11.
  62. del Campo J.M.; Negro, V.; Núñez, M. The history of technology in education. A comparative study and forecast. Procedia-Social Behav. Sci. 2012, 69, 1086–1092.
  63. Schiff, D. Education for AI, not AI for education: The role of education and ethics in national AI policy strategies. Int. J. Artificial Intell. in Edu. 2022, 32, 527–563.
  64. Saettler, P. The evolution of American educational technology; Information Age Publishing: New York, USA. 2004.
  65. Roberts, D.L. History of tools and technologies in mathematics education. In Handbook on the history of mathematics education. Springer New York: New York, USA, 2013; pp. 565–578.
  66. Heilmann, T.A. The beginnings of word processing: A historical account. In Digital Writing Technologies in Higher Education: Theory, Research, and Practice. Springer International Publishing: Cham, USA, 2023; pp. 3–14.
  67. Darwin; Rusdin, D.; Mukminatien, N.; Suryati, N.; Laksmi, E.D.; Marzuki. Critical thinking in the AI era: An exploration of EFL students’ perceptions, benefits, and limitations. Cogent. Edu. 2024, 11, 2290342.
  68. Challenges to Academic Integrity from New Tools-A Survey of Students’ Perceptions and Behaviors of Employing ChatGPT. Available online: (accessed on 1 March 2024)
  69. Albayati, H. Investigating undergraduate students’ perceptions and awareness of using ChatGPT as a regular assistance tool: A user acceptance perspective study. Comput. Edu.: Artificial. Intell. 2024, 100203.
  70. Grájeda, A.; Burgos, J.; Córdova, P.; Sanjinés, A. Assessing student-perceived impact of using artificial intelligence tools: Construction of a synthetic index of application in higher education. Cogent. Edu. 2024, 11, 2287917.
  71. Teaching Writing with Generative AI. Boston University 2024. Available online: (accessed on 4 March 2024).
  72. Teaching Writing in an AI World. Available online: (accessed on 7 March 2024)
  73. Pedagogic Strategies for Adapting to Generative AI Chatbots. Available online: (accessed on 9 March 2024)
  74. New Guidance on Teaching Writing in the Age of AI. Harvard Writing Project. Available online: (accessed on 11 March 2024)