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Integration of Generative Artificial Intelligence in Higher Education: Best Practices

Cordero, Jorge, Torres-Zambrano, Jonathan, Cordero-Castillo, Alison·2024·Education Sciences
DOI 10.3390/educsci15010032
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Abstract

Generative artificial intelligence (GenAI) is transforming various sectors, including education. This study investigates the integration of GenAI in higher education, focusing on its potential to enhance teaching and learning. Through a series of workshops and courses delivered to university professors, it examines opportunities such as improved resource creation and challenges like ethical AI usage, proposing best practices for the sustainable implementation of GenAI in the classroom. The main objective is to analyze how the use of GenAI tools such as ChatGPT, Gemini, and Claude can improve teachers’ professional skills and the overall educational experience while ensuring ethical and responsible use. The methodology comprised a literature review and practical experimentation with university professors. Data collection involved observations, surveys, discussion forums, cooperative activities, and exercises focused on evaluating AI-generated educational resources and analyzing forum insights to identify best practices. The results highlight several opportunities around the use of GenAI in education, including improving writing, creating educational resources, supporting lesson planning, and increasing teacher productivity. In addition, significant challenges were identified, such as the ethical use of AI and strategies for detecting AI-generated text. For instance, workshops demonstrated a 30% increase in teacher confidence with GenAI tools like ChatGPT, highlighting the effectiveness of these technologies in professional development. To address these challenges, best practices for the responsible integration of GenAI in education are presented, focusing primarily on ongoing training, the establishment of institutional policies, the encouragement of responsible use, and the ongoing evaluation of impact in the educational setting. Best practices include clear ethical guidelines, prompt development techniques, and continuous professional training to ensure teachers can effectively and responsibly integrate GenAI tools into their instructional practices. These practices for the effective use of GenAI tools in education aim to maximize benefits while mitigating risks. These include the development of effective prompts for various activities and guidance on the ethical use of AI to ensure a balanced and responsible approach to the integration of GenAI in higher education.

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  • Structured workshops and guided exposure to tools such as ChatGPT have been associated with stronger teacher confidence and clearer recognition of practical benefits, including resource creation, productivity, and support for lesson planning.

    “For example, structured workshops and guided exposure to tools such as ChatGPT have been associated with stronger teacher confidence and clearer recognition of practical benefits, including resource creation, productivity, and support for lesson planning [4].”

    From Engineering student perceptions of generative AI use in learning trust judgment and information seeking (2026) · cites this paper as [4]

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Research works printed but not linked: 38

  1. Ali, D., Fatemi, Y., Boskabadi, E., Nikfar, M., Ugwuoke, J., & Ali, H. (2024). ChatGPT in teaching and learning: A systematic review. Education Sciences, 14(6), 643.
  2. Alqadi, R., Alrbaiyan, A., Alrumayyan, N., Alqahtani, N., & Najjar, A. (2023, July 24-27). Exploring the user experience and the role of ChatGPT in the academic writing process. 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE) (pp. 1082-1089), Las Vegas, NV, USA.
  3. CrossRef · DOI
  4. Bahroun, Z., Anane, C., Ahmed, V., & Zacca, A. (2023). Transforming education: A comprehensive review of generative artificial intelligence in educational settings through bibliometric and content analysis. Sustainability, 15(17), 12983.
  5. CrossRef · DOI · Paper page
  6. CrossRef · DOI · Paper page
  7. Cooper, G. (2023). Examining science education in ChatGPT: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32(3), 444-452.
  8. Elbanna, S., & Armstrong, L. (2024). Exploring the integration of ChatGPT in education: Adapting for the future. Management & Sustainability: An Arab Review, 3(1), 16-29.
  9. Farrelly, T., & Baker, N. (2023). Generative artificial intelligence: Implications and considerations for higher education practice. Education Sciences, 13(11), 1109.
  10. Haleem, A., Javaid, M., Qadri, M. A., & Suman, R. (2022). Understanding the role of digital technologies in education: A review. Sustainable Operations and Computers, 3, 275-285.
  11. CrossRef · DOI · Paper page
  12. Javaid, M., Haleem, A., Singh, R. P., Khan, S., & Khan, I. H. (2023). Unlocking the opportunities through ChatGPT Tool towards ameliorating the education system. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 3(2), 100115.
  13. Jeon, J., & Lee, S. (2023). Large language models in education: A focus on the complementary relationship between human teachers and ChatGPT. Education and Information Technologies, 28(12), 15873-15892.
  14. CrossRef · DOI · Paper page
  15. Jo, J., & Choi, S. (2023, July 24-27). Analysis of plagiarism via ChatGPT on domain-specific exams. 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE) (pp. 1026-1033), Las Vegas, NV, USA.
  16. CrossRef · DOI
  17. Kalyan, K. S. (2024). A survey of GPT-3 family large language models including ChatGPT and GPT-4. Natural Language Processing Journal, 6, 100048.
  18. CrossRef · DOI · Paper page
  19. Kiryakova, G., & Angelova, N. (2023). ChatGPT-A challenging tool for the university professors in their teaching practice. Education Sciences, 13(10), 1056.
  20. CrossRef · DOI · Paper page
  21. Lye, C. Y., & Lim, L. (2024). Generative artificial intelligence in tertiary education: Assessment redesign principles and considerations. Education Sciences, 14(6), 569.
  22. CrossRef · DOI
  23. Malik, A. R., Pratiwi, Y., Andajani, K., Numertayasa, I. W., Suharti, S., Darwis, A., & Marzuki. (2023). Exploring artificial intelligence in academic essay: Higher education student's perspective. International Journal of Educational Research Open, 5, 100296.
  24. CrossRef · DOI · Paper page
  25. Mao, J., Chen, B., & Liu, J. C. (2024). Generative artificial intelligence in education and its implications for assessment. TechTrends, 68(1), 58-66.
  26. CrossRef · DOI · Paper page
  27. Mhlanga, D. (2023). Open AI in education, the responsible and ethical use of ChatGPT towards lifelong learning. In D. Mhlanga (Ed.), FinTech and artificial intelligence for sustainable development: The role of smart technologies in achieving development goals (pp. 387-409). Springer Nature Switzerland.
  28. CrossRef · DOI · Paper page
  29. 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), 856.
  30. CrossRef · DOI
  31. CrossRef · DOI · Paper page
  32. Pesovski, I., Santos, R., Henriques, R., & Trajkovik, V. (2024). Generative AI for customizable learning experiences. Sustainability, 16(7), 3034.
  33. Ratten, V., & Jones, P. (2023). Generative artificial intelligence (ChatGPT): Implications for management educators. The International Journal of Management Education, 21(3), 100857.
  34. CrossRef · DOI
  35. Ray, P. P. (2023). ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber-Physical Systems, 3, 121-154.
  36. Wang, Y., Pan, Y., Yan, M., Su, Z., & Luan, T. H. (2023). A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutions. IEEE Open Journal of the Computer Society, 4, 280-302.
  37. CrossRef · DOI
  38. Wu, Y., Henriksson, A., Duneld, M., & Nouri, J. (2023). Towards improving the reliability and transparency of ChatGPT for educational question answering. In O. Viberg, I. Jivet, P. J. Muñoz-Merino, M. Perifanou, & T. Papathoma (Eds.), Responsive and Sustainable Educational Futures (pp. 475-488). Springer Nature Switzerland. Disclaimer/Publisher's Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

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Authors
Cordero, J., Torres-Zambrano, J., & Cordero-Castillo, A.
Affiliations
Departamento de Ciencias de la Computación y Electrónica, Universidad Técnica Particular de Loja, Loja 110107, Ecuador
ORCID
Venue
Education Sciences
Publisher
MDPI AG
Published
2024-12-31
Volume
15
Issue
1
Pages
32
ISSN
2227-7102
DOI
10.3390/educsci15010032
Cited by
112
References
14
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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