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The impact of artificial intelligence on personalized learning approaches in higher education institutions
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Abstract
This study explores the impact of Artificial Intelligence (AI) on personalized learning approaches within higher education institutions. The research investigates how AI-based systems, including chatbots and digital twin technologies, can enhance student support services and improve educational outcomes. Key findings indicate that AI-powered tools demonstrate significant potential in addressing academic inquiries and providing personalized assistance to students. Additionally, personalized learning approaches supported by AI have led to enhanced academic performance and increased student satisfaction. However, challenges such as technical limitations, privacy concerns, and the need for faculty training are noted. The study concludes that while AI offers transformative opportunities for higher education, its successful implementation requires careful planning, adequate resources, and ongoing evaluation. Future research should focus on long-term impacts and best practices for AI integration in educational settings.
Keywords
Artificial intelligence Personalized learning Educational chatbots Digital twin technology Smart university systemsReferences (16)
- [1] Khatun, M., Islam, R., Kumar, S., Hossain, R., & Mani, L. (2024). The impact of artificial intelligence on educational transformation: Trends and future directions. Journal of information systems and informatics, 6(4), 2347–2373. https://doi.org/10.51519/journalisi.v6i4.879
- [2] Kabashkin, I. (2025). AI-based digital twins of students: A new paradigm for competency-oriented learning transformation. Information, 16(10), 846. https://doi.org/10.3390/info16100846
- [3] Qazi, S., Kadri, M. B., Naveed, M., Khawaja, B. A., Khan, S. Z., Alam, M. M., & Su’ud, M. M. (2024). AI-driven learning management systems: Modern developments, challenges and future trends during the age of ChatGPT. Computers, materials & continua, 80(2), 3289–3314. http://dx.doi.org/10.32604/cmc.2024.048893
- [4] Ocen, S., Elasu, J., Aarakit, S. M., & Olupot, C. (2025). Artificial intelligence in higher education institutions: Review of innovations, opportunities and challenges. Frontiers in education (Vol. 10, p. 1530247). Frontiers Media SA. https://doi.org/10.3389/feduc.2025.1530247
- [5] Alenezi, M., Wardat, S., & Akour, M. (2023). The need of integrating digital education in higher education: Challenges and opportunities. Sustainability, 15(6), 4782. https://doi.org/10.3390/su15064782
- [6] Mouta, A., Pinto-Llorente, A. M., & Torrecilla-Sánchez, E. M. (2024). Uncovering blind spots in education ethics: Insights from a systematic literature review on artificial intelligence in education. International journal of artificial intelligence in education, 34(3), 1166–1205. https://doi.org/10.1007/s40593-023-00384-9
- [7] Wang, Y., & Xue, L. (2024). Using AI-driven chatbots to foster Chinese EFL students’ academic engagement: An intervention study. Computers in human behavior, 159, 108353. https://doi.org/10.1016/j.chb.2024.108353
- [8] Chang, D. H., Lin, M. P. C., Hajian, S., & Wang, Q. Q. (2023). Educational design principles of using AI chatbot that supports self-regulated learning in education: Goal setting, feedback, and personalization. Sustainability, 15(17), 12921. https://doi.org/10.3390/su151712921
- [9] Reethika, A., & Kanaga Priya, P. (2024). Personalize learning experience in education using digital twins with human-centered design and pedagogy. In Transforming industry using digital twin technology (pp. 165–185). Springer. https://doi.org/10.1007/978-3-031-58523-4_9
- [10] Karanam, S. A. K., & Hartman, N. W. (2025). A systematic review of digital twin (DT) and virtual learning environments (VLE) for smart manufacturing education. Manufacturing letters, 44, 1597–1608. https://doi.org/10.1016/j.mfglet.2025.06.179
- [11] Strielkowski, W., Grebennikova, V., Lisovskiy, A., Rakhimova, G., & Vasileva, T. (2025). AI-driven adaptive learning for sustainable educational transformation. Sustainable development, 33(2), 1921–1947. https://doi.org/10.1002/sd.3221
- [12] Omer, N. I. M. S. (2024). Maintaining meaningful human interaction in AI-enhanced language learning environments: A systematic review. Arts for linguistic & literary studies, 6(3), 533–552. https://doi.org/10.53286/arts.v6i3.2083
- [13] Yang, E., & Beil, C. (2024). Ensuring data privacy in AI/ML implementation. New directions for higher education, 2024(207), 63–78. https://doi.org/10.1002/he.20509
- [14] Duenas, T., & Ruiz, D. (2024). The risks of human overreliance on large language models for critical thinking. https://doi.org/10.13140/RG.2.2.26002.06082
- [15] Rajini, J., Kalaivani, K., Singh, L., Elavarasan, S., & Roy, S. (2025). Streamlining administrative tasks to boost teacher productivity using AI-driven solutions. In Digital leadership for sustainable higher education (pp. 191–218). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-9989-7.ch006
- [16] Vetrivel, S. C., Vidhyapriya, P., Arun, V. P., Devi, M., & Jeevitha, V. J. (2025). Smart infrastructure for educational sustainability. In Smart education and sustainable learning environments in smart cities (pp. 445–470). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-7723-9.ch026
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- Published 2025-09-16
- Issue Vol. 2 No. 3 (2025): Metaversalize
- Section Articles
- Document Type Research Article
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This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Malekpour, F. (2025). The impact of artificial intelligence on personalized learning approaches in higher education institutions. Metaversalize, 2(3), 175-181. https://doi.org/10.22105/metaverse.v2i3.86
