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Cited by 183Artificial Intelligence in EducationEthics and Social Impacts of AIAcademic integrity and plagiarism

The ethical implications of using generative chatbots in higher education

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Williams, Ryan Thomas·2024·Frontiers in Education
DOI 10.3389/feduc.2023.1331607
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Abstract

Incorporating artificial intelligence (AI) into education, specifically through generative chatbots, can transform teaching and learning for education professionals in both administrative and pedagogical ways. However, the ethical implications of using generative chatbots in education must be carefully considered. Ethical concerns about advanced chatbots have yet to be explored in the education sector. This short article introduces the ethical concerns associated with introducing platforms such as ChatGPT in education. The article outlines how handling sensitive student data by chatbots presents significant privacy challenges, thus requiring adherence to data protection regulations, which may not always be possible. It highlights the risk of algorithmic bias in chatbots, which could perpetuate societal biases, which can be problematic. The article also examines the balance between fostering student autonomy in learning and the potential impact on academic self-efficacy, noting the risk of over-reliance on AI for educational purposes. Plagiarism continues to emerge as a critical ethical concern, with AI-generated content threatening academic integrity. The article advocates for comprehensive measures to address these ethical issues, including clear policies, advanced plagiarism detection techniques, and innovative assessment methods. By addressing these ethical challenges, the article argues that educators, AI developers, policymakers, and students can fully harness the potential of chatbots in education, creating a more inclusive, empowering, and ethically sound educational future.

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  • Generated text can contain inaccuracies and fake data, including fake references presented as if they were true.

    “Generated text can contain inaccuracies and fake data, including fake references (Carrigan, 2024;Williams, 2024) presented as if they were true.”

    From Toward Partnership Ethics for Generative Artificial Intelligence in Higher Education (2026) · cites this paper as Williams, 2024

  • GenAI raises critical ethical concerns, including data privacy, algorithmic bias, and educational inequality.

    “However, it raises critical ethical concerns, including data privacy, algorithmic bias, and educational inequality, requiring comprehensive regulatory frameworks and pedagogical strategies.”

    From Ethical and regulatory challenges of Generative AI in education: a systematic review (2025) · cites this paper as Williams, 2024

  • Privacy is at risk when personal data is mishandled, especially if generative models are trained on sensitive information without proper regulation.

    “The first of these, privacy, is at risk when personal data is mishandled, especially if generative models are trained on sensitive information without proper regulation (Williams, 2024).”

    From Ethical and regulatory challenges of Generative AI in education: a systematic review (2025) · cites this paper as Williams, 2024

  • GenAI-generated content can facilitate academic plagiarism, blurring the boundaries between original effort and automated production.

    “Another relevant problem is academic plagiarism, where GenAI-generated content can facilitate dishonest behavior, blurring the boundaries between original effort and automated production (Williams, 2024).”

    From Ethical and regulatory challenges of Generative AI in education: a systematic review (2025) · cites this paper as Williams, 2024

  • Improper use of GenAI could result in a decrease in the quality of autonomous learning, affecting the development of critical skills.

    “However, improper use of GenAI could result in a decrease in the quality of autonomous learning, affecting the development of critical skills (Williams, 2024).”

    From Ethical and regulatory challenges of Generative AI in education: a systematic review (2025) · cites this paper as Williams, 2024

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References 54 · Cited by 183

Research works printed but not linked: 40

  1. Alenizi, F. A., Abbasi, S., Hussein Mohammed, A., and Masoud Rahmani, A. (2023). The artificial intelligence Technologies in Industry 4.0: a taxonomy, approaches, and future directions. Comput. Ind. Eng. 185:109662. doi: 10.1016/j.cie.2023.109662 · DOI
  2. Barocas, S., Hardt, M., and Narayanan, A. (2023). Fairness and machine learning: Limitations and opportunities. Cambridge, Massachusetts: MIT Press.
  3. Biswas, S. (2023). Role of chat GPT in public health. Ann. Biomed. Eng. 51, 868-869. doi: 10.1007/s10439-023-03172-7 · DOI · Paper page
  4. Castillo, A. G. R., Silva, G. J. S., Arocutipa, J. P. F., Berrios, H. Q., Rodriguez, M. A. M., Reyes, G. Y., et al. (2023). Effect of chat GPT on the digitized learning process of university students. J. Namib. Stud. 33, 1-15. doi: 10.59670/jns.v33i.411 · DOI
  5. Chen, K., Chen, V., and Yen, D. C. (2011). Dimensions of self-efficacy in the study of smart phone acceptance. Comput. Stand. Interfaces 33, 422-431. doi: 10.1016/j. csi.2011.01.003 · DOI
  6. Compeau, D., and Higgins, C. (1995). Computer self-efficacy: development of a measure and initial test. Manag. Inf. Syst. Q. 19:189. doi: 10.2307/249688 · DOI · Paper page
  7. Crittenden, W. F., Biel, I. K., and Lovely, W. A. (2018). Embracing digitalization: student learning and new technologies. J. Mark. Educ. 41, 5-14. doi: 10.1177/0273475318820895 · DOI
  8. De Vries, K. (2020). You never fake alone. Creative AI in action. Inf. Commun. Soc. 23, 2110-2127. doi: 10.1080/1369118x.2020.1754877 · DOI
  9. Dong, Y., Wang, S., Gan, Z., Cheng, Y., Cheung, J. C. K., and Liu, J. (2020) 'Multi-fact correction in abstractive text summarization,' arXiv. [Epub ahead of preprint] doi: 10.48550/arxiv.2010.02443 · DOI
  10. Els, G. (2022). "'A hybrid model in tourism postgraduate education -a learning journey" in Team Academy in Diverse Settings. eds. B. Urzelai and E. Vettraino (Routledge), 19-29.
  11. Emsley, R. (2023). ChatGPT: these are not hallucinations -they're fabrications and falsifications. Schizophrenia 9:52. doi: 10.1038/s41537-023-00379-4 · DOI · Paper page
  12. Fitria, T. N. (2023). "Artificial intelligence (AI) technology in OpenAI ChatGPT application: A review of ChatGPT in writing English essay, " in ELT Forum: Journal of English Language Teaching. 12, 44-58.
  13. Fryer, L. K., Thompson, A., Nakao, K., Howarth, M., and Gallacher, A. (2020). Supporting self-efficacy beliefs and interest as educational inputs and outcomes: framing AI and human partnered task experiences. Learn. Individ. Differ. 80:101850. doi: 10.1016/j.lindif.2020.101850 · DOI · Paper page
  14. Hellström, T., Dignum, V., and Bensch, S. (2020) 'Bias in machine learning -what is it good for?,' European Conference on Artificial Intelligence, pp. 3-10.
  15. Hilton, J. (2016). Open educational resources and college textbook choices: A review of research on efficacy and perceptions. Educ. Technol. Res. Dev. 64, 573-590. doi: 10.1007/s11423-016-9434-9 · DOI
  16. Hsu, M., and Chiu, C. (2004). Internet self-efficacy and electronic service acceptance. Decis. Support. Syst. 38, 369-381. doi: 10.1016/j.dss.2003.08.001 · DOI
  17. Igbaria, M. (1995). The effects of self-efficacy on computer usage. Omega 23, 587-605. doi: 10.1016/0305-0483(95)00035-6 · DOI
  18. Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., et al. (2022) 'Survey of hallucination in natural language generation,' arXiv. [Epub ahead of preprint] doi: 10.48550/ arxiv.2202.03629 · DOI · Paper page
  19. John, S.P. (ed.) (2013) Antecedents and effects of computer self-efficacy on social networking adoption among Asian online users. Americas Conference on Information Systems.
  20. King, M. R. (2023). A conversation on artificial intelligence, chatbots, and plagiarism in higher education. Cell. Mol. Bioeng. 16, 1-2. doi: 10.1007/s12195-022-00754-8 · DOI · Paper page
  21. Lee, D. Y., and Ryu, H. (2013). Learner acceptance of a multimedia-based learning system. Int. J. Hum. Comput. Int. 29, 419-437. doi: 10.1080/10447318.2012.715278 · DOI
  22. Mansell, R. (Ed.). (2007). The Oxford handbook of information and communication technologies. Oxford Handbooks Online. doi: 10.1093/oxfordhb/ 9780199548798.001.0001 · DOI
  23. Maynez, J., Narayan, S., Bohnet, B., and McDonald, R. (2020) 'On faithfulness and factuality in abstractive summarization,' arXiv. [Epub ahead of preprint] doi: 10.48550/ arxiv.2005.00661 · DOI · Paper page
  24. McCarthy, J., Minsky, M. L., Rochester, N., and Shannon, C. E. (2006). A proposal for the Dartmouth summer research project on artificial intelligence, august 31, 1955. AI Mag. 27:12. doi: 10.1609/aimag.v27i4.1904 · DOI
  25. Mehrabi, N., et al. (2019) 'A survey on Bias and fairness in machine learning,' arXiv. [Epub ahead of preprint] doi: 10.48550/arxiv.1908.09635 · DOI · Paper page
  26. Meyer von Wolff, R., Nörtemann, J., Hobert, S., and Schumann, M. (2020). Chatbots for the information acquisition at universities -a student's view on the application area. Lect. Notes Comput. Sci. 11970, 231-244. doi: 10.1007/978-3-030-39540-7_16 Microsoft (2023) What is Microsoft's Approach to AI? Available at: https://news. microsoft.com/source/features/ai/microsoft-approach-to-ai/ (Accessed April 1, 2012). · DOI
  27. Ocumpaugh, J., Baker, R., Gowda, S., Heffernan, N., and Heffernan, C. (2014). Population validity for educational data mining models: a case study in affect detection. Br. J. Educ. Technol. 45, 487-501. doi: 10.1111/bjet.12156 · DOI · Paper page
  28. Popenici, S., and Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Res. Pract. Technol. Enhanc. Learn. 12:22. doi: 10.1186/s41039-017-0062-8 · DOI · Paper page
  29. Qadir, J. (2022) 'Engineering education in the era of ChatGPT: promise and pitfalls of generative AI for education,' TechRxiv. [Epub ahead of preprint] doi: 10.36227/ techrxiv.21789434.v1 · DOI · Paper page
  30. Shawar, B., and Atwell, A. (2007) Fostering language learner autonomy through adaptive conversation tutors. Available at: https://www.birmingham.ac.uk/documents/ college-artslaw/corpus/conference-archives/2007/51paper.pdf
  31. Silva, S., and Kenney, M. (2019). Algorithms, platforms, and ethnic bias. Commun. ACM 62, 37-39. doi: 10.1145/3318157 · DOI
  32. Stokel-Walker, C., and Van Noorden, R. (2023). What ChatGPT and generative AI mean for science. Nature 614, 214-216. doi: 10.1038/d41586-023-00340-6 · DOI · Paper page
  33. Teel, Z., Wang, T., and Lund, B. (2023). ChatGPT conundrums: probing plagiarism and parroting problems in higher education practices. Coll. Res. Libr. News 84:205. doi: 10.5860/crln.84.6.205 · DOI
  34. Wang, Y., and Chuang, Y.-W. (2023). Artificial intelligence self-efficacy: scale development and validation. Educ. Inf. Technol., 1-24. doi: 10.1007/ s10639-023-12015-w · DOI
  35. Watermeyer, R., Phipps, L., Lanclos, D., and Knight, C. (2023). Generative AI and the automating of academia. Postdigit. Sci. Educ., 1-21. doi: 10.1007/ s42438-023-00440-6 · DOI
  36. Wiliam, D. (2010). The role of formative assessment in effective learning environments. Educ. Res. Innov., 135-159. doi: 10.1787/9789264086487-8-en · DOI
  37. Williams, R. (2022). An exploration into the pedagogical benefits of using social media: Can educators incorporate social media into pedagogy successfully? (Doctoral dissertation), Teesside University Repository.
  38. Williams, R. T. (2023). Think piece: ethics for the virtual researcher. Practice 5, 41-47. doi: 10.1080/25783858.2023.2179893 · DOI
  39. Wolf, M. J., Miller, K., and Grodzinsky, F. S. (2017). Why we should have seen that coming. ORBIT J. 1, 1-12. doi: 10.29297/orbit.v1i2.49 · DOI
  40. Zhao, J., Wu, M., Zhou, L., Wang, X., and Jia, J. (2022). Cognitive psychology-based artificial intelligence review. Front. Neurosci. 16:1024316. doi: 10.3389/fnins.2022. 1024316 · DOI

Authors
Williams, R. T.
Affiliations
Teesside University (GB)
Corresponding author
Williams, Ryan Thomas
ORCID
Williams, Ryan Thomas
Venue
Frontiers in Education
Publisher
Frontiers Media SA
Published
2024-01-08
Volume
8
ISSN
2504-284X (ISSN-L)
DOI
10.3389/feduc.2023.1331607
OpenAlex
W4390665705
Cited by
183
References
54
Citations by year
2026: 72 · 2025: 89 · 2024: 22
FWCI
68.81
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Education (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • TeesRep (Teesside University) (submittedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Safety Research › Academic integrity and plagiarism
Keywords
generative chatbots, artificial intelligence in education, ChatGPT, ethical implications, student privacy, data protection regulation, algorithmic bias, student independence, academic self-efficacy, overreliance on AI, plagiarism, academic integrity, plagiarism detection, assessment methods, AI ethics policy

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