Perceptions of Generative AI Tools in Higher Education: Insights from Students and Academics at Sultan Qaboos University
Abstract
:1. Introduction
- How familiar are students and academics at SQU with GenAI tools like ChatGPT?
- Are there any differences between students’ and academics’ perceptions of GenAI (potential usefulness, PU, ease of use, EU, and perceived challenges, PC) at SQU?
- What are the factors influencing students’ and academics’ intentions to use GenAI in the future at Sultan Qaboos University?
1.1. Students’ Perceptions on the Use of GenAI in Higher Education
1.2. Academics’ Perceptions on the Use of GenAI in Higher Education
2. Materials and Methods
2.1. Survey Development and Validation
2.2. Data Collection and Sample
3. Results
3.1. Differences Between Students’ and Academics’ Perceptions of GenAI
3.1.1. Perceptions of Perceived Usefulness (PU)
3.1.2. Perceptions of Perceived Ease of Use (EU)
Statements | Students (n1 = 555) | Academics (n2 = 168) | ||
---|---|---|---|---|
Mean | Std. Deviation | Mean | Std. Deviation | |
| 4.01 | 0.99 | 4.20 | 0.86 |
| 4.03 | 0.99 | 4.24 | 0.75 |
| 3.82 | 1.01 | 3.86 | 0.9 |
| 4.18 | 1.06 | 4.16 | 0.84 |
| 4.10 | 0.91 | 4.11 | 0.79 |
| 3.43 | 1.02 | 3.68 | 1.06 |
| 3.90 | 0.96 | 4.01 | 0.94 |
| 3.71 | 1.03 | 4.05 | 0.86 |
| 3.66 | 0.98 | 3.77 | 0.97 |
| 3.50 | 1.07 | 3.77 | 0.94 |
| 3.56 | 1.03 | 3.76 | 0.92 |
Mean | 3.85 | 0.68 | 3.96 | 0.62 |
3.1.3. Perceptions of Perceived Challenges (PC)
Statements | Students (n1 = 555) | Academics (n2 = 168) | ||
---|---|---|---|---|
Mean | Std. Deviation | Mean | Std. Deviation | |
| 3.76 | 0.93 | 3.79 | 0.84 |
| 3.66 | 0.97 | 3.95 | 0.85 |
| 3.74 | 1.02 | 3.90 | 0.9 |
| 2.97 | 1.08 | 3.07 | 1.13 |
| 3.67 | 1.11 | 3.96 | 1 |
| 3.46 | 1.1 | 3.78 | 0.9 |
| 3.83 | 1.11 | 3.95 | 0.92 |
| 3.39 | 1.02 | 3.50 | 1.01 |
Mean | 3.56 | 0.64 | 3.74 | 0.61 |
3.2. Intended Future Use of GenAI at SQU
4. Discussion
5. Conclusions
6. Limitations and Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Albayati, H. (2024). Investigating undergraduate students’ perceptions and awareness of using ChatGPT as a regular assistance tool: A user acceptance perspective study. Computers and Education: Artificial Intelligence, 6. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- Al Hadithy, Z. A., Al Lawati, A., Al-Zadjali, R., & Al Sinawi, H. (2023). Knowledge, attitudes, and perceptions of artificial intelligence in healthcare among medical students at Sultan Qaboos University. Cureus, 15(9), e44887. [Google Scholar] [CrossRef] [PubMed]
- 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. [Google Scholar] [CrossRef]
- Amoozadeh, M., Daniels, D., Nam, D., Kumar, A., Chen, S., Hilton, M., Srinivasa Ragavan, S., & Alipour, M. A. (2024, March 20–23). Trust in generative AI among students: An exploratory study. SIGCSE 2024—Proceedings of the 55th ACM Technical Symposium on Computer Science Education (Vol. 1, ), Portland, OR, USA. [Google Scholar] [CrossRef]
- Atlas, S. (2023). ChatGPT for higher education and professional development: A guide to conversational AI. In DigitalCommons@URI (Vol. 1). University of Rhode Island. [Google Scholar]
- Bernabei, M., Colabianchi, S., Falegnami, A., & Costantino, F. (2023). Students’ use of large language models in engineering education: A case study on technology acceptance, perceptions, efficacy, and detection chances. Computers and Education: Artificial Intelligence, 5, 100172. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- Centre for Excellence in Teaching and Learning (CETL). (2025). Useful resources. Available online: https://www.squ.edu.om/About/Support-Centers-/Centre-For-Excellence-In-Teaching-and-Learning (accessed on 5 January 2025).
- Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 43. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- Chiu, T. K. F. (2024). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 6, 100197. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- David, P., Choung, H., & Seberger, J. S. (2024). Who is responsible? US public perceptions of AI governance through the lenses of trust and ethics. Public Understanding of Science, 33(5), 654–672. [Google Scholar] [CrossRef] [PubMed]
- 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).
- 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. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. [Google Scholar] [CrossRef]
- Eke, D. O. (2023). ChatGPT and the rise of generative AI: Threat to academic integrity? Journal of Responsible Technology, 13, 100060. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- Hashem, R., Ali, N., El Zein, F., Fidalgo, P., & Khurma, O. A. (2024). AI to the rescue: Exploring the potential of ChatGPT as a teacher ally for workload relief and burnout prevention. Research and Practice in Technology Enhanced Learning, 19, 23. [Google Scholar] [CrossRef]
- Huang, L. (2023). Ethics of artificial intelligence in education: Student privacy and data protection. Science Insights Education Frontiers, 16(2), 2577–2587. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- Kaplan-Rakowski, R., Grotewold, K., Hartwick, P., & Papin, K. (2023). Generative AI and teachers’ perspectives on its implementation in education. Journal of Interactive Learning Research, 34(2), 313–338. [Google Scholar]
- 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. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- Mhlanga, D. (2023). Open AI in education, the responsible and ethical use of ChatGPT towards lifelong learning. SSRN Electronic Journal. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- Okulu, H. Z., & Muslu, N. (2024). Designing a course for pre-service science teachers using ChatGPT: What ChatGPT brings to the table. Interactive Learning Environments, 32, 7450–7467. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- Qashou, A. (2021). Influencing factors in M-learning adoption in higher education. Education and Information Technologies, 26(2), 1755–1785. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- Qizi, S. (2023). The role of AI in teaching: Advantages, disadvantages, and future implications. Eurasian Scientific Herald, 24, 8–12. Available online: https://geniusjournals.org/index.php/esh/article/view/4885 (accessed on 3 January 2025).
- 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. [Google Scholar] [CrossRef]
- Rahman, M. M., & Watanobe, Y. (2023). ChatGPT for education and research: Opportunities, threats, and strategies. Applied sciences, 13(9), 5783. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- Sánchez-Prieto, J. C., Cruz-Benito, J., Therón, R., & García-Pẽalvo, F. J. (2019, October 16–18). How to measure teachers’ acceptance of AI-driven assessment in eLearning: A TAM-based proposal [ACM International Conference Proceeding Series]. TEEM’19: Technological Ecosystems for Enhancing Multiculturality, León, Spain. [Google Scholar] [CrossRef]
- Sánchez-Vera, F. (2025). Subject-specialized chatbot in higher education as a tutor for autonomous exam preparation: Analysis of the impact on academic performance and students’ perception of its usefulness. Education Sciences, 15(1), 26. [Google Scholar] [CrossRef]
- Sharma, S., Singh, G., Sharma, C. S., & Kapoor, S. (2024). Artificial intelligence in Indian higher education institutions: A quantitative study on adoption and perceptions. International Journal of System Assurance Engineering and Management. [Google Scholar] [CrossRef]
- Shoufan, A. (2023). Exploring students’ perceptions of ChatGPT: Thematic analysis and follow-up survey. IEEE Access, 11, 38805–38818. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- Taylor, K. (2023). Supporting students and educators in using generative artificial intelligence. ASCILITE Publications. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- 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. [Google Scholar]
- Ursavaş, Ö. F. (2022). Conducting technology acceptance research in education: Theory, models, implementation, and analysis. Springer Nature. [Google Scholar]
- Vartiainen, H., & Tedre, M. (2023). Using artificial intelligence in craft education: Crafting with text-to-image generative models. Digital Creativity, 34(1), 1–21. [Google Scholar] [CrossRef]
- Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- Xiong, L., Chen, Y., Peng, Y., & Ghadi, Y. Y. (2024). Improving robot-assisted virtual teaching using transformers, GANs, and computer vision. Journal of Organizational and End User Computing, 36(1), 1–32. [Google Scholar] [CrossRef]
Variables | Students’ Survey | Academics’ Survey | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Number of Items | Cronbach Alpha | Pearson Correlation | Number of Items | Cronbach Alpha | Pearson Correlation | |||||
1 | 2 | 3 | 1 | 2 | 3 | |||||
| 11 | 0.879 | 1 | 0.750 ** | 0.221 ** | 11 | 0.891 | 1 | 0.674 ** | 0.363 ** |
| 12 | 0.915 | 1 | 0.259 ** | 9 | 0.907 | 1 | 0.360 ** | ||
| 8 | 0.763 | 1 | 8 | 0.769 | 1 |
Groups | Students n1 = 555 | Academics n2 = 168 | Total N = 723 | ||||
---|---|---|---|---|---|---|---|
Variable | Sub-Variable | Number | % | Number | % | Number | % |
Gender | Male | 229 | 41.3 | 98 | 58.3 | 327 | 45.2 |
Female | 326 | 58.7 | 70 | 41.7 | 396 | 54.8 | |
Specialization | Social Sciences and Humanities | 288 | 51.9 | 90 | 53.6 | 378 | 52.3 |
Natural Sciences | 85 | 15.3 | 32 | 19.0 | 117 | 16.2 | |
Engineering and Technology | 91 | 16.4 | 25 | 14.9 | 116 | 16.0 | |
Health Sciences | 91 | 16.4 | 21 | 12.5 | 112 | 15.5 |
Scales | Students n1 = 555 | Academics n2 = 168 | t-Test |
---|---|---|---|
Ease of Use | 3.847 (0.66) | 3.963 (0.62) | t = −1.97, df = 721, SE = 0.06, p = 0.049 |
Perceived Usefulness | 3.853 (0.69) | 3.634 (0.70) | t = 3.56, df = 721, SE = 0.06, p = 0.000 |
Perceived Challenges | 3.561 (0.64) | 3.736 (0.61) | t = −1.76.732, df = 721, SE = 0.06, p = 0.002 |
Statements for Students | Students (n1 = 555) | Statements for Academics | Academics (n2 = 168) | ||
---|---|---|---|---|---|
Mean | SD | Mean | SD | ||
| 4.15 | 0.92 |
| 3.89 | 0.82 |
| 4.18 | 0.87 |
| 3.89 | 0.85 |
| 3.98 | 0.92 |
| 3.85 | 0.84 |
| 3.96 | 0.95 |
| 3.80 | 0.91 |
| 3.92 | 1.01 |
| 3.74 | 0.94 |
| 3.87 | 1.02 |
| 3.62 | 0.92 |
| 3.83 | 0.94 |
| 3.49 | 1.00 |
| 3.8? | 0.99 |
| 3.27 | 0.97 |
| 3.74 | 0.96 |
| 3.17 | 1.13 |
| 3.68 | 0.97 | |||
| 3.6? | 0.99 | |||
| 3.51 | 1.03 | |||
Mean of Usefulness | 3.85 | 0.69 | 3.64 | 0.71 |
Category | Response | Students (n = 555) | Academics (n = 168) | Total (N = 723) | |||
---|---|---|---|---|---|---|---|
Number | % | Number | % | Number | % | ||
Intention to Use GenAI in The Future | Yes | 448 | 80.7 | 145 | 86.3 | 593 | 82.0 |
Not sure | 96 | 17.3 | 22 | 13.1 | 118 | 16.3 | |
No | 11 | 2.0 | 1 | 0.6 | 12 | 1.7 | |
Adoption of GenAI | It should be embraced | 375 | 67.6 | 124 | 73.8 | 499 | 69.0 |
I am not sure | 157 | 28.3 | 39 | 23.2 | 196 | 27.1 | |
It should be prohibited | 23 | 4.1 | 5 | 3.0 | 28 | 3.9 | |
Awareness of GenAI Policies | Yes, available | 206 | 37.1 | 43 | 25.6 | 249 | 34.4 |
I am not sure | 300 | 54.1 | 85 | 50.6 | 385 | 53.3 | |
No, not available | 49 | 8.8 | 40 | 23.8 | 89 | 12.3 |
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Alshamy, A.; Al-Harthi, A.S.A.; Abdullah, S. Perceptions of Generative AI Tools in Higher Education: Insights from Students and Academics at Sultan Qaboos University. Educ. Sci. 2025, 15, 501. https://doi.org/10.3390/educsci15040501
Alshamy A, Al-Harthi ASA, Abdullah S. Perceptions of Generative AI Tools in Higher Education: Insights from Students and Academics at Sultan Qaboos University. Education Sciences. 2025; 15(4):501. https://doi.org/10.3390/educsci15040501
Chicago/Turabian StyleAlshamy, Alsaeed, Aisha Salim Ali Al-Harthi, and Shubair Abdullah. 2025. "Perceptions of Generative AI Tools in Higher Education: Insights from Students and Academics at Sultan Qaboos University" Education Sciences 15, no. 4: 501. https://doi.org/10.3390/educsci15040501
APA StyleAlshamy, A., Al-Harthi, A. S. A., & Abdullah, S. (2025). Perceptions of Generative AI Tools in Higher Education: Insights from Students and Academics at Sultan Qaboos University. Education Sciences, 15(4), 501. https://doi.org/10.3390/educsci15040501