Key Factors Influencing the Increased Use of Artificial Intelligence Tools Among Students at Albukhary International University
DOI:
10.65622/ijei.v2i1.238Downloads
Abstract
The rapid integration of Artificial Intelligence (AI) technologies in higher education has significantly influenced students’ learning practices, prompting increased reliance on AI-based academic tools. This study aims to analyze the key factors contributing to the growing use of AI tools among students at Albukhary International University. The research employed a qualitative, literature-based approach, using descriptive and content analysis methods to review relevant scholarly publications on AI adoption in higher education. The findings indicate that three major factors drive students’ use of AI tools: intensified academic workload and performance pressure, widespread digital accessibility and institutional technological integration, and the need for linguistic and cognitive support within an international academic environment. These factors collectively create structural conditions that encourage students to integrate AI technologies into their academic activities, particularly for writing assistance, information processing, and problem-solving tasks. The study concludes that the increasing use of AI tools among university students is not merely a technological trend but a response to academic, institutional, and learner-related demands in contemporary higher education. The findings imply that universities need to develop clear academic policies and support systems to ensure the responsible and ethical use of AI technologies while maintaining academic integrity and meaningful learning outcomes.
Keywords:
Artificial intelligence in education academic workload digital learning environment educational technology technology adoptionReferences
Alammary, A. (2022). The impact of artificial intelligence on faculty development in higher education: A systematic review. International Journal of Educational Technology in Higher Education, 19(1), 1–18. https://doi.org/10.1186/s41239-022-00345-2
Alammary, A., Sheard, J., & Carbone, A. (2016). Blended learning in higher education: Delivery methods selection. In Proceedings of the Twenty-Fourth European Conference on Information Systems. https://doi.org/10.1007/978-3-030-20135-7_5
Almaiah, M. A., Alfaisal, R., Salloum, S. A., Hajjej, F., Shishakly, R., Lutfi, A., Alrawad, M., Al Mulhem, A., Alkhdour, T., & Al-Maroof, R. S. (2022). Measuring institutions’ adoption of artificial intelligence applications in online learning environments: Integrating innovation diffusion theory with technology adoption rate. Electronics, 11(20), 3291. https://doi.org/10.3390/electronics11203291
Altbach, P. G., Reisberg, L., & Rumbley, L. E. (2019). Trends in global higher education: Tracking an academic revolution. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000375950
Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., & Kerres, M. (2020). Mapping research in student engagement and educational technology in higher education: A systematic evidence map. International Journal of Educational Technology in Higher Education, 17, 2. https://doi.org/10.1186/s41239-019-0176-8
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications. EDUCAUSE. (2023). 2023 students and technology report: Flexibility, choice, and equity in the student experience. EDUCAUSE.
Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of
Theoretical and Applied Statistics, 5(1), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11
Gayed, J. M., Carlon, M. K. J., Oriola, A. M., & Cross, J. S. (2022). Exploring an AI-based writing assistant’s impact on English language learners. Computers & Education: Artificial Intelligence, 3, 100055. https://doi.org/10.1016/j.caeai.2022.100055
Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. https://curriculumredesign.org/our-work/artificial-intelligence-in-education/
Hyland, K. (2019). Second language writing (3rd ed.). Cambridge University Press.
Kember, D. (2019). Understanding the nature of motivation and motivating students. Routledge.
Krippendorff, K. (2018). Content analysis: An introduction to its methodology (4th ed.). SAGE Publications.
Lee, J., & Briggs, N. (2021). Effects of automated writing tools on ESL academic writing. Journal of Second Language Writing, 53, 100822. https://doi.org/10.1016/j.jslw.2021.100822
Lee, S. M. (2024). Impact of ChatGPT on ESL students’ academic writing skills: A mixed methods of intervention study. Smart Learning Environments, 11(1), 1–22. https://doi.org/10.1186/s40561-024-00295-9
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2020). Intelligence unleashed: An argument for AI in education. Pearson. https://www.pearson.com/en-gb/subject-catalog/p/intelligence-unleashed/P200000006384
Organisation for Economic Co-operation and Development. (2021). Digital education outlook 2021: Pushing the frontiers with AI, blockchain and robots. OECD Publishing. https://doi.org/10.1787/589b283f-en
Russell, S. J., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson Education.
Salloum, S. A., Al-Emran, M., Shaalan, K., & Tarhini, A. (2019). Factors affecting e-learning acceptance: A case study from the UAE. Education and Information Technologies, 24, 509–530. https://doi.org/10.1007/s10639-018-9786-3
Selwyn, N. (2020). Should robots replace teachers? AI and the future of education. Polity Press. https://politybooks.com/bookdetail/?isbn=9781509522735
Sweller, J., Ayres, P., & Kalyuga, S. (2019). Cognitive load theory. Springer. https://link.springer.com/book/10.1007/978-1-4939-9031-4
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
Zawacki-Richter, O., Buntins, K., Molfenter, T., & Kenzig, M. (2019). Digitalization and academic workload: Effects on student performance. Education Sciences, 9(2), 107. https://doi.org/10.3390/educsci9020107
