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Cited by 47Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationTechnology Adoption and User Behaviour

Perceptions of Generative AI Tools in Higher Education: Insights from Students and Academics at Sultan Qaboos University

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Alshamy, Alsaeed, Al-Harthi, Aisha Salim Ali, Abdullah, Shubair·2025·Education Sciences
DOI 10.3390/educsci15040501
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Abstract

This study investigates the perceptions of generative artificial intelligence (GenAI) tools, such as ChatGPT, among students and academics at Sultan Qaboos University (SQU) within the context of higher education in Oman. Using the Technology Acceptance Model (TAM), it explores five key dimensions: actual use (AU), ease of use (EU), perceived usefulness (PU), perceived challenges (PC), and intention to use (IU). Data collected from 555 students and 168 academics provide valuable insights into the opportunities and challenges associated with the adoption of GenAI tools, based on the results of a t-test. The findings reveal notable differences between students and academics regarding their perceptions of GenAI tools across all TAM variables. Students report frequent use of GenAI for academic support, including personalized learning, brainstorming, and completing assignments, while academics highlight its role in developing learning materials, assessments, lesson plans, and customizing learning content. Both groups recognize its potential to enhance efficiency and innovation in academic practices. However, concerns arise regarding over-reliance on GenAI, diminished critical thinking and creativity, and academic integrity risks. Academics consistently express greater concerns about these challenges than students, particularly regarding plagiarism, academic misconduct, and the potential for over-reliance on GenAI. Despite these challenges, the majority of students and academics indicate a willingness to continue using GenAI tools. This contrast underscores the need for tailored interventions to address the distinct concerns of students and academics. These findings highlight the need for regulatory frameworks, comprehensive institutional guidelines, and targeted training programs to ensure the ethical and responsible use of GenAI technologies. By addressing these critical areas, higher education institutions in Oman can leverage the potential of GenAI while safeguarding academic integrity and fostering essential skills such as critical thinking and creativity.

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  • Classroom discussions about the broader impacts of AI on education, society, and future careers invite students to engage critically with the technology's role in their lives.

    “Classroom discussions about the broader impacts of AI on education, society, and future careers, invite students to engage critically with the technology's role in their lives (Alshamy et al., 2025; Sekli et al., 2024; Urban et al., 2024).”

    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 Alshamy et al., 2025

    The Discussion recommends involving students in discussions about GenAI policies and usage, and the Conclusions call for training in AI ethics and critical thinking. The paper does not describe classroom discussions about AI’s broader effects on society or future careers, or establish that such discussions prompt critical engagement. (LLM, conf=0.83)

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Research works printed but not linked: 41

  1. Al Ghazali, S., Zaki, N., Ali, L., & Harous, S. (2024). Exploring the potential of ChatGPT as a substitute teacher: A case study. International Journal of Information and Education Technology, 14(2), 271-278.
  2. Alrishan, A. M. H. (2023). Determinants of intention to use ChatGPT for professional development among Omani EFL pre-service teachers. International Journal of Learning, Teaching and Educational Research, 22(12), 187-209.
  3. Branum, C., & Schiavenato, M. (2023). Can ChatGPT accurately answer a PICOT question? Assessing AI response to a clinical question. Nurse Educator, 48(5), 231-233.
  4. Cao, Y., Aziz, A. A., & Arshard, W. N. R. M. (2023). University students' perspectives on artificial intelligence: A survey of attitudes and awareness among interior architecture students. International Journal of Educational Research and Innovation, 2023(20), 1-21.
  5. Chaudhry, I. S., Sarwary, S. A. M., El Refae, G. A., & Chabchoub, H. (2023). Time to revisit existing student's performance evaluation approach in higher education sector in a new era of ChatGPT-A case study. Cogent Education, 10(1), 2210461.
  6. Cong-Lem, N., Tran, T. N., & Nguyen, T. T. (2024). Academic integrity in the age of generative AI: Perceptions and responses of Vietnamese EFL teachers. Teaching English with Technology, 24(1), 28-47.
  7. Crawford, J., Vallis, C., Yang, J., Fitzgerald, R., & O'dea, C. (2023). Editorial: Artificial intelligence is awesome, but good teaching should always come first. Journal of University Teaching and Learning Practice, 20(7), 1-12.
  8. Davis, F. D., & Granić, A. (1989). Technology acceptance model. Springer Nature. Available online: https://link.springer.com/content/ pdf/10.1007/978-3-030-45274-2.pdf (accessed on 3 January 2025). · DOI
  9. Dogan, M. E., Goru Dogan, T., & Bozkurt, A. (2023). The use of artificial intelligence (AI) in online learning and distance education processes: A systematic review of empirical studies. Applied Sciences, 13(5), 3056.
  10. Donlon, E., & Tiernan, P. (2023). Chatbots and citations: An experiment in academic writing with generative AI. Irish Journal of Technology Enhanced Learning, 7(2), 75-87.
  11. Farazouli, A., Cerratto-Pargman, T., Bolander-Laksov, K., & McGrath, C. (2024). Hello GPT! Goodbye home examination? An exploratory study of AI chatbots impact on university teachers' assessment practices. Assessment and Evaluation in Higher Education, 49(3), 363-375.
  12. Firaina, R., & Sulisworo, D. (2023). Exploring the usage of ChatGPT in higher education: Frequency and impact on productivity. Buletin Edukasi Indonesia, 2(01), 39-46.
  13. Huang, L. (2023). Ethics of artificial intelligence in education: Student privacy and data protection. Science Insights Education Frontiers, 16(2), 2577-2587.
  14. Jeong, C. (2023). A Study on the implementation of generative AI services using an enterprise data-based LLM application architecture. Advances in Artificial Intelligence and Machine Learning, 3(4), 1588-1618.
  15. Kamoun, F., El Ayeb, W., Jabri, I., Sifi, S., & Iqbal, F. (2024). Exploring students' and faculty's knowledge, attitudes, and perceptions towards ChatGPT: A cross-sectional empirical study. Journal of Information Technology Education: Research, 23, 1.
  16. Lu, J., Zheng, R., Gong, Z., & Xu, H. (2024). Supporting teachers' professional development with generative AI: The effects on higher order thinking and self-efficacy. IEEE Transactions on Learning Technologies, 17, 1267-1277.
  17. Lu, Q., Yao, Y., Xiao, L., Yuan, M., Wang, J., & Zhu, X. (2024). Can ChatGPT effectively complement teacher assessment of undergraduate students' academic writing? Assessment and Evaluation in Higher Education, 49(5), 616-633.
  18. Miao, J., Thongprayoon, C., Suppadungsuk, S., Garcia Valencia, O. A., Qureshi, F., & Cheungpasitporn, W. (2024). Ethical dilemmas in using AI for academic writing and an example framework for peer review in nephrology Academia: A narrative review. Clinics and Practice, 14(1), 89-105.
  19. Ngo, T. T. A. (2023). The perception by university students of the use of ChatGPT in education. International Journal of Emerging Technologies in Learning (Online), 18(17), 4.
  20. Nikolic, S., Daniel, S., Haque, R., Belkina, M., Hassan, G. M., Grundy, S., Lyden, S., Neal, P., & Sandison, C. (2023). ChatGPT versus engineering education assessment: A multidisciplinary and multi-institutional benchmarking and analysis of this generative artificial intelligence tool to investigate assessment integrity. European Journal of Engineering Education, 48(4), 559-614.
  21. Parycek, P., Schmid, V., & Novak, A. S. (2023). Artificial intelligence (AI) and automation in administrative procedures: Potentials, limitations, and framework conditions. Journal of the Knowledge Economy, 15, 8390-8415.
  22. Pillai, R., Sivathanu, B., Metri, B., & Kaushik, N. (2024). Students' adoption of AI-based teacher-bots (T-bots) for learning in higher education. Information Technology and People, 37(1), 328-355.
  23. Potter, J., Welsh, K., & Milne, L. (2023). Evaluating an institutional response to Generative Artificial Intelligence (GenAI): Applying Kotter's change model and sharing lessons learned for educational development. Journal of Perspectives in Applied Academic Practice, 11(3), 139-152.
  24. Pratama, M. P., Sampelolo, R., & Lura, H. (2023). Revolutionizing education: Harnessing the power of artificial intelligence for personalized learning. Klasikal: Journal of Education, Language Teaching and Science, 5(2), 350-357.
  25. Qashou, A. (2021). Influencing factors in M-learning adoption in higher education. Education and Information Technologies, 26(2), 1755-1785.
  26. Qiao-Franco, G., & Zhu, R. (2024). China's artificial intelligence ethics: Policy development in an emergent community of practice. Journal of Contemporary China, 33(146), 189-205.
  27. Qu, J. H., Qin, X. R., Li, C. D., Peng, R. M., Xiao, G. G., Cheng, J., Gu, S. F., Wang, H. K., & Hong, J. (2023). Fully automated grading system for the evaluation of punctate epithelial erosions using deep neural networks. British Journal of Ophthalmology, 107(4), 453-460.
  28. Rahman, M. M., & Watanobe, Y. (2023). ChatGPT for education and research: Opportunities, threats, and strategies. Applied sciences, 13(9), 5783.
  29. 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.
  30. Rohan, R., Faruk, L. I. D., Puapholthep, K., & Pal, D. (2023, December 6-9). Unlocking the black box: Exploring the use of generative AI (ChatGPT) in information systems research [ACM International Conference Proceeding Series]. 13th International Conference on Advances in Information Technology, Bangkok, Thailand.
  31. Saqr, R. R., Al-Somali, S. A., & Sarhan, M. Y. (2024). Exploring the Acceptance and User Satisfaction of AI-Driven e-Learning Platforms (Blackboard, Moodle, Edmodo, Coursera and edX): An Integrated Technology Model. Sustainability, 16(1), 204.
  32. Silva, C. A. G. d., Ramos, F. N., de Moraes, R. V., & Santos, E. L. d. (2024). ChatGPT: Challenges and benefits in software programming for higher education. Sustainability, 16(3), 1245.
  33. Su, J., & Yang, W. (2023a). Powerful or mediocre? Kindergarten teachers' perspectives on using ChatGPT in early childhood education. Interactive Learning Environments, 32(10), 6496-6508.
  34. Su, J., & Yang, W. (2023b). Unlocking the power of ChatGPT: A framework for applying generative AI in education. ECNU Review of Education, 6(3), 355-366.
  35. Su, J., & Yang, W. (2024). AI literacy curriculum and its relation to children's perceptions of robots and attitudes towards engineering and science: An intervention study in early childhood education. Journal of Computer Assisted Learning, 40(1), 241-253.
  36. Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning and Teaching, 6(1), 1-10.
  37. Sweet Moore, P., Coleman, B., Young, H., Bunch, J. C., & Jagger, C. (2023). Preservice teachers' perceptions of important elements of the student teaching experience. Journal of Agricultural Education, 64(1), 171-183.
  38. Téllez, N. R., Villela, P. R., & Bautista, R. B. (2024). Evaluating ChatGPT-generated linear algebra formative assessments. International Journal of Interactive Multimedia and Artificial Intelligence, 8(5), 75-82.
  39. Trust, T., Whalen, J., & Mouza, C. (2023). Editorial: ChatGPT: Challenges, opportunities, and implications for teacher education. Contemporary Issues in Technology and Teacher Education, 23(1), 1-23.
  40. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315.
  41. Wang, Y., & Zhang, W. (2023). Factors Influencing the adoption of generative AI for art designing among Chinese generation Z: A structural equation modeling approach. IEEE Access, 11, 143272-143284.

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Authors
Alshamy, A., Al-Harthi, A. S. A., & Abdullah, S.
Affiliations
  • Sultan Qaboos University (OM)
  • Instructional and Learning Technologies, College of Education, Sultan Qaboos University, Muscat 123, Oman
Corresponding author
Alshamy, Alsaeed
ORCID
Venue
Education Sciences
Publisher
MDPI AG
Published
2025-04-16
Volume
15
Issue
4
Pages
501
ISSN
2227-7102 (ISSN-L)
DOI
10.3390/educsci15040501
OpenAlex
W4409507413
Cited by
46
References
59
Citations by year
2026: 29 · 2025: 17
FWCI
48.23
Type
journal article
Language
en
Open access
Licence
Locations
  • Education Sciences (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Ministry of Higher Education, Research and Innovation in Oman (RC/RG-EDU/DEFA/23/01)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
Keywords
generative AI, ChatGPT, student perceptions, academic perceptions, technology acceptance, perceived usefulness, perceived ease of use, academic integrity, plagiarism, overreliance on AI, critical thinking, creativity, AI adoption in higher education, Oman

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