Students’ Perceptions of Artificial Intelligence Integration in Higher Education
This study explores the impact of artificial intelligence (AI) integration on students' educational experiences. It investigates student perceptio.
- Pub. date: April 15, 2025
- Online Pub. date: March 06, 2025
- Pages: 471-484
- 38 Downloads
- 199 Views
- 0 Citations
This study explores the impact of artificial intelligence (AI) integration on students' educational experiences. It investigates student perceptions of AI across various academic aspects, such as module outlines, learning outcomes, curriculum design, instructional activities, assessments, and feedback mechanisms. It evaluates the impact of AI on students' learning experiences, critical thinking, self-assessment, cognitive development, and academic integrity. This research used a structured survey distributed to 300 students through Microsoft Forms 365, yet the response rate was 29.67%. A structured survey and thematic analysis were employed to gather insights from 89 students. Thematic analysis is a qualitative method for identifying and analysing patterns or themes within data, providing insights into key ideas and trends. The limited response rate may be attributed to learners' cultural backgrounds, as not all students are interested in research or familiar with AI tools. The survey questions are about AI integration in different academic areas. Thematic analysis was used to identify patterns and themes within the data. Benefits such as enhanced critical thinking, timely feedback, and personalised learning experiences are prevalent. AI tools like Turnitin supported academic integrity, and platforms like ChatGPT and Grammarly were particularly valued for their utility in academic tasks. The study acknowledges limitations linked to the small sample size and a focus on undergraduate learners only. The findings suggest that AI can significantly improve educational experiences. AI provides tailored support and promotes ethical practices. This study recommends continued and expanded use of AI technologies in education while addressing potential implementation challenges.
Keywords: AI benefits, AI in education, digital literacy, Omani higher education, student perceptions.
References
Adel, A., Ahsan, A., & Davison, C. (2024). ChatGPT promises and challenges in education: Computational and ethical perspectives. Education Sciences, 14(8), Article 814. https://doi.org/10.3390/educsci14080814
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), Article 643. https://doi.org/10.3390/educsci14060643
Asad, M. M., Shahzad, S., Shah, S. H. A., Sherwani, F., & Almusharraf, N. M. (2024). ChatGPT as artificial intelligence-based generative multimedia for English writing pedagogy: Challenges and opportunities from an educator's perspective. International Journal of Information and Learning Technology, 41(5), 490-506. https://doi.org/10.1108/IJILT-02-2024-0021
Bin-Hady, W. R. A., Al-Kadi, A., Hazaea, A., & Ali, J. K. M. (2023). Exploring the dimensions of ChatGPT in English language learning: A global perspective. Library Hi Tech. Advance online publication. https://doi.org/10.1108/LHT-05-2023-0200
Bin-Nashwan, S. A., Sadallah, M., & Bouteraa, M. (2023). Use of ChatGPT in academia: Academic integrity hangs in the balance. Technology in Society, 75, Article 102370. https://doi.org/10.1016/j.techsoc.2023.102370
Birks, D., & Clare, J. (2023). Linking artificial intelligence facilitated academic misconduct to existing prevention frameworks. International Journal for Educational Integrity, 19, Article 20. https://doi.org/10.1007/s40979-023-00142-3
Bozkurt, A. (2024). Why generative AI literacy, why now and why it matters in the educational landscape? Kings, queens and GenAI dragons. Open Praxis, 16(3), 283-290. https://doi.org/10.55982/openpraxis.16.3.739
Brusilovsky, P., & Millán, E. (2007). User models for adaptive hypermedia and adaptive educational systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The adaptive web: Methods and strategies of web personalisation (pp. 3-53). Springer. https://doi.org/10.1007/978-3-540-72079-9_1
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Chu, S. K. W., Reynolds, R. B., Tavares, N. J., Notari, M., & Lee, C. W. Y. (2017). 21st century skills development through inquiry-based learning: From theory to practice. Springer. https://doi.org/10.1007/978-981-10-2481-8
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20, Article 22. https://doi.org/10.1186/s41239-023-00392-8
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://www.jstor.org/stable/249008
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behaviour. Springer. https://doi.org/10.1007/978-1-4899-2271-7
Du, J., & Alm, A. (2024). The impact of ChatGPT on English for academic purposes (EAP) students' language learning experience: A self-determination theory perspective. Education Sciences, 14(7), Article 726. https://doi.org/10.3390/educsci14070726
Elkhatat, A. M., Elsaid, K., & Almeer, S. (2023). Evaluating the efficacy of AI content detection tools in differentiating between human and AI-generated text. International Journal for Educational Integrity, 19, Article 17. https://doi.org/10.1007/s40979-023-00140-5
Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2020). How to design AI for social good: Seven essential factors. Science and Engineering Ethics, 26, 1771-1796. https://doi.org/10.1007/s11948-020-00213-5
Goksel, N., & Bozkurt, A. (2019). Artificial intelligence in education. In S. Sisman-Ugur, & G. Kurubacak (Eds.), Handbook of research on learning in the age of transhumanism (pp. 224-236). IGI Global. https://doi.org/10.4018/978-1-5225-8431-5.ch014
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. https://bit.ly/4ibkEJC
Hwang, H. S., Zhu, L. C., & Cui, Q. (2023). Development and validation of a digital literacy scale in the artificial intelligence era for college students. KSII Transactions on Internet and Information Systems, 17(8), 2241-2258.
Ibrahim, K. (2023). Using AI-based detectors to control AI-assisted plagiarism in ESL writing: "The Terminator Versus the Machines". Language Testing in Asia, 13, Article 46. https://doi.org/10.1186/s40468-023-00260-2
Kim, J. (2024). Leading teachers' perspective on teacher-AI collaboration in education. Education and Information Technologies, 29, 8693-8724. https://doi.org/10.1007/s10639-023-12109-5
Kim, N. J., & Kim, M. K. (2022). Teacher's perceptions of using an artificial intelligence-based educational tool for scientific writing. Frontiers in Education, 7, Article 755914. https://doi.org/10.3389/feduc.2022.755914
Larkin, S. (Ed.). (2023). Metacognition and education: Future trends. Routledge. https://doi.org/10.4324/9781003150602
Malik, A. R., Pratiwi, Y., Andajani, K., Numertayasa, I. W., Suharti, S., Darwis, A., & Marzuki. (2023). Exploring artificial intelligence in academic essay: Higher education students' perspective. International Journal of Educational Research Open, 5, Article 100296. https://doi.org/10.1016/j.ijedro.2023.100296
Mao, K., Ferguson, S., Magarian, J., & Olechowski, A. (2023). "Just a little bit on the outside for the whole time": Social belonging confidence and the persistence of machine learning and artificial intelligence students. In Proceedings of the 2023 Machine Learning and Artificial Intelligence Conference. arXiv. https://doi.org/10.48550/arxiv.2311.10745
McCabe, D. L., Treviño, L. K., & Butterfield, K. D. (2001). Cheating in academic institutions: A decade of research. Ethics and Behavior, 11(3), 219-232. https://doi.org/10.1207/S15327019EB1103_2
Piaget, J. (1970). Piaget's theory. In P. H. Mussen (Ed.), Carmichael's manual of child psychology (3rd ed., Vol. 1, pp. 703-732). Wiley.
Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon, 9(6), 1-6. https://doi.org/10.1108/10748120110424843
Qadhi, S. M., Alduais, A., Chaaban, Y., & Khraisheh, M. (2024). Generative AI, research ethics, and higher education research: Insights from a scientometric analysis. Information, 15(6), Article 325. https://doi.org/10.3390/info15060325
Rasul, T., Nair, S., Kalendra, D., Robin, M., Santini, F. O., Ladeira, W. J., Sun, M., Day, I., Rather, R. A., & Heathcote, L. (2023). The role of ChatGPT in higher education: Benefits, challenges, and future research directions. Journal of Applied Learning and Teaching, 6(1), 41-56. https://doi.org/10.37074/jalt.2023.6.1.29
Rusandi, M. A., Ahman, Saripah, I., Khairun, D. Y., & Mutmainnah. (2023). No worries with ChatGPT: Building bridges between artificial intelligence and education with critical thinking soft skills. Journal of Public Health, 45(3), E602-E603. https://doi.org/10.1093/pubmed/fdad049
Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400. https://doi.org/10.1177/0002764213498851
Verdú, E., Regueras, L. M., Verdú, M. J., De Castro, J. P., & Pérez, M. Á. (2008). An analysis of the research on adaptive learning: The next generation of e-learning. WSEAS Transactions on Information Science and Applications, 5(6), 859-868.
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press. https://doi.org/10.2307/j.ctvjf9vz4
Yan, D. (2023). Impact of ChatGPT on learners in an L2 writing practicum: An exploratory investigation. Education and Information Technologies, 28, 13943-13967. https://doi.org/10.1007/s10639-023-11742-4