Perceptions of Online University Students Toward Artificial Intelligence Integration in Medical Education and Decision-Making: A Multi-Faculty Survey Study in Afghanistan
DOI:
10.56566/cer.v2i1.720Published:
2026-03-31Downloads
Abstract
The integration of artificial intelligence (AI) into education has emerged as a transformative force reshaping pedagogical approaches, decision-making processes, and learning outcomes across disciplines. In Afghanistan, where the higher education system is navigating unprecedented digital transformation amid resource constraints and post-conflict reconstruction, understanding student perceptions of AI adoption is critically important. This cross-sectional survey study investigated the attitudes, awareness, perceived usefulness, and behavioral intentions of online university students across six faculties at the Vision Online University of Afghanistan toward AI integration in medical and general higher education. A structured, validated questionnaire was administered to 384 purposively sampled students from the Faculties of Medicine, Engineering, Law, Education, Natural Sciences, and Business Administration. Data were collected between March and June 2024 and analyzed using descriptive statistics, one-way ANOVA, and structural equation modeling. Results indicated that overall awareness of AI was moderately high (M = 3.72, SD = 0.84 on a 5-point Likert scale), with Medical Faculty students reporting significantly higher perceived usefulness (M = 4.01) compared to Law (M = 3.45) and Business (M = 3.90) faculties (F(5,378) = 4.83, p < .001). Students broadly favored AI-assisted clinical decision support and adaptive learning tools. Key barriers included limited digital infrastructure, inadequate AI literacy training, and language-related concerns specific to Dari and Pashto speakers. The findings provide empirical grounding for a faculty-differentiated AI integration strategy in Afghan higher education
Keywords:
Afghanistan Artificial intelligence Decision-making Medical education Online learning Student perceptions Survey studyReferences
Afghan Ministry of Higher Education. (2023). Higher education statistical yearbook 2022–2023. MoHE Afghanistan. https://mohe.gov.af
Bird, S., Daelemans, W., Farghaly, A., Frank, A., Haji, J., Nirenburg, S., & Rudnicky, A. (2023). Low-resource NLP for under-resourced languages: Challenges and opportunities. Computational Linguistics, 49(2), 451–497. https://doi.org/10.1162/coli_a_00469
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
Coiera, E., Ash, J., & Berg, M. (2016). The unintended consequences of health information technology revisited. Yearbook of Medical Informatics, 25(1), 163–169. https://doi.org/10.15265/IY-2016-014
Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Doraiswamy, S., Abraham, A., Mamtani, R., & Cheema, S. (2021). Use of telehealth during the COVID-19 pandemic: Scoping review. Journal of Medical Internet Research, 23(1), e24087. https://doi.org/10.2196/24087
Hossain, S. F. A., Nurunnabi, M., & Iqbal, K. (2023). Perceptions of AI-assisted learning among medical students in Bangladesh. Medical Education Online, 28(1), 2191847. https://doi.org/10.1080/10872981.2023.2191847
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
International Telecommunication Union. (2023). Measuring digital development: Facts and figures 2023. ITU. https://www.itu.int
Iqbal, M. Z., Vandelanotte, C., Mummery, W. K., & Ginis, K. A. M. (2021). Medical students' attitudes toward artificial intelligence in clinical education in Pakistan. BMC Medical Education, 21, 388. https://doi.org/10.1186/s12909-021-02826-3
Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607–610. https://doi.org/10.1177/001316447003000308
Latif, G., Mohammad, N., Alghazo, J., & AlKhalaf, R. (2023). Artificial intelligence in medical education: A systematic review of ChatGPT and related tools. International Journal of Environmental Research and Public Health, 20(14), 6371. https://doi.org/10.3390/ijerph20146371
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3313831.3376727
Maina, I. W., Welch, T. D., Lawson, L., Decamp, M., Newhouse, A., Nagurney, J. T., & Obermeyer, Z. (2019). A decade of investigating the fair use of AI in health care. The Lancet, 394(10204), 1119–1120. https://doi.org/10.1016/S0140-6736(19)31922-7
Newbrander, W., Ickx, P., Feroz, F., & Stanekzai, H. (2014). Afghanistan's basic package of health services: Its development and effects on rebuilding the health system. Global Public Health, 9(Suppl 1), S6–S28. https://doi.org/10.1080/17441692.2014.916735
Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future Big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216–1219. https://doi.org/10.1056/NEJMp1606181
Peng, H., Ma, S., & Spector, J. M. (2021). Personalized adaptive learning: An emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environments, 6(1), 9. https://doi.org/10.1186/s40561-019-0089-y
Rahimi, F., & Haidari, Z. (2022). Digital readiness of medical educators in Afghanistan: A cross-sectional needs assessment. Afghan Journal of Health Sciences, 5(1), 14–23.
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature Medicine, 28(1), 31–38. https://doi.org/10.1038/s41591-021-01614-0
Saleem, M., Kamarudin, S., Shoaib, H. M., & Nasar, A. (2022). Influence of technology on student learning in higher education: Role of TAM in the context of developing countries. Frontiers in Psychology, 13, 907693. https://doi.org/10.3389/fpsyg.2022.907693
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers' adoption of digital technology in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
Schiff, D., Rakova, B., Ayesh, A., Fanti, A., & Lennon, M. (2020). Principles to practices for responsible AI: Closing the gap. arXiv preprint arXiv:2006.04707. https://doi.org/10.48550/arXiv.2006.04707
Sindermann, C., Sha, P., Zhou, M., Wernicke, J., Schmitt, H. S., Li, M., Sariyska, R., Stavrou, M., Becker, B., & Montag, C. (2021). Assessing the attitude towards artificial intelligence: Introduction of a short measure in German, Chinese, and English language. KI – Künstliche Intelligenz, 35, 109–118. https://doi.org/10.1007/s13218-020-00689-0
Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: Benefits, risks, and strategies for success. NPJ Digital Medicine, 3(1), 17. https://doi.org/10.1038/s41746-020-0221-y
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
Wahl, B., Cossy-Gantner, A., Germann, S., & Schwalbe, N. R. (2018). Artificial intelligence (AI) and global health: How can AI contribute to health in resource-poor settings? BMJ Global Health, 3(4), e000798. https://doi.org/10.1136/bmjgh-2018-000798
World Health Organization. (2022). Afghanistan health profile 2022. WHO. https://www.who.int/countries/afg
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101
License
Copyright (c) 2026 Pashtana Amiry, Mustafa Qaderi, Khudai Qul Khaliqyar

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with Current Educational Review, agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License (CC-BY License). This license allows authors to use all articles, data sets, graphics, and appendices in data mining applications, search engines, web sites, blogs, and other platforms by providing an appropriate reference. The journal allows the author(s) to hold the copyright without restrictions and will retain publishing rights without restrictions.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in Current Educational Review.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).


