Knowledge and attitudes towards the use of artificial Intelligence for learning among health sciences students at Mildmay Institute of Health Sciences, Uganda
DOI:
https://doi.org/10.71020/ywtsnw72Abstract
Background:
The study aims to assess knowledge and attitudes towards the use of artificial Intelligence for learning among health sciences students at Mildmay Institute of Health Sciences, Uganda
Methodology:
A descriptive cross-sectional study was conducted. The study was carried out at Mildmay Institute of Health Sciences. All students enrolled in Diploma or certificate courses at MIHS doing health-related programs during the data collection period were eligible to be part of our study population. Targeting. Student lists were stratified into cells based on program and year.
Results:
The majority of participants were female (53.6%, n=119). Over two-fifths (44.1%, n=98) were in Year 3 or above, while Year 1 and Year 2 students accounted for 24.3% (n=54) and 30.2% (n=67), respectively. Diploma in Clinical Medicine was the most represented program (54.9%, n=122), followed by Diploma in Nursing (22.5%, n=50) and Diploma in Midwifery (12.2%, n=27). (86.0%, n=191) demonstrated adequate knowledge of AI tools for learning, scoring 3 or 4 out of 4. The mean knowledge score was 3.44 (SD=0.85). AI tools for understanding difficult topics, 86.5% (n=192) of students agreed or strongly agreed, while only 13.5% (n=30) disagreed or strongly disagreed. On confidence in using AI tools for learning, 72.1% (n=160) of students expressed agreement, though 27.9% (n=62) were neutral or unsure, suggesting some students remain hesitant in their practical engagement with AI tools. The most strongly endorsed item stated that AI tools can improve the quality of studies when used correctly; 86.5% (n=192) agreed or strongly agreed, including 70.3% (n=156) who strongly agreed.
Recommendations:
Address digital access inequities by supporting subsidized institutional AI tool subscriptions, improving internet access, and integrating AI infrastructure into national planning.
Conclusions:
These results highlight the absence of formal AI literacy education, clear institutional policies, and structured pedagogical support at MIHS.
References
Abid, A., Murugan, A., Banerjee, I., Purkayastha, S., Trivedi, H., & Gichoya, J. (2024). AI
education for fourth-year medical students: Two-Year experience of a web-based, self
guided curriculum and mixed methods study. JMIR Medical Education, 10, e46500.
Ahsan, Z. (2025). Integrating artificial intelligence into medical education: A narrative
systematic review of current applications, challenges, and future directions. BMC
Medical Education, 25(1), 1187-. https://doi.org/10.1186/s12909-025-07744-0
Ajalo, E., Mukunya, D., Nantale, R., Kayemba, F., Pangholi, K., Babuya, J., Akuu, S. L.,
Namiiro, A. M., Nsubuga, Y. B., Mpagi, J. L., Musaba, M. W., Oguttu, F., Kuteesa, J.,
Mubuuke, A. G., Munabi, I. G., & Kiguli, S. (2025). Widespread use of ChatGPT and
other Artificial Intelligence tools among medical students in Uganda: A cross-sectional
study. PLOS ONE, 20(1). https://doi.org/10.1371/journal.pone.0313776
Alqaissi, N., & Qtait, M. (2025). Knowledge, attitudes, practices, and barriers regarding the
integration of artificial intelligence in nursing and health sciences education: A
systematic
review.
SAGE
Open
Nursing,
11,
23779608251374184.
https://doi.org/10.1177/23779608251374185
Al-Qerem, W., Eberhardt, J., Jarab, A., Bawab, A. Q. A., Hammad, A., Alasmari, F., Alazab,
B., Husein, D. A., Alazab, J., & Al-Beool, S. (2023). Exploring knowledge, attitudes,
and practices towards artificial intelligence among health professions’ students in
Jordan.
BMC Medical Informatics and Decision Making, 23(1), 288-.
https://doi.org/10.1186/s12911-023-02403-0
Amiri, H., Peiravi, S., Rezazadeh Shojaee, S. S., Rouhparvarzamin, M., Nateghi, M. N.,
Etemadi, M. H., ShojaeiBaghini, M., Musaie, F., Anvari, M. H., & Asadi Anar, M.
(2024). Medical, dental, and nursing students’ attitudes and knowledge towards
artificial intelligence: A systematic review and meta-analysis. BMC Medical
Education, 24(1), 412. https://doi.org/10.1186/s12909-024-05406-1
Boateng, G. O., Neilands, T. B., Frongillo, E. A., Melgar-Quiñonez, H. R., & Young, S. L.
(2018). Best practices for developing and validating scales for health, social, and
behavioral
research:
a
primer.
https://doi.org/10.3389/fpubh.2018.00149
Frontiers
in
Public
Health,
6.
Boyacı, H., & Söyük, S. (2025). Healthcare workers’ readiness for artificial intelligence and
organizational change: A quantitative study in a university hospital. BMC Health
Services Research, 25(1), 813-. https://doi.org/10.1186/s12913-025-12846-y
36
Buabbas, A. J., Miskin, B., Alnaqi, A. A., Ayed, A. K., Shehab, A. A., Syed-Abdul, S., &
Uddin, M. (2023). Investigating students’ perceptions towards artificial intelligence in
medical education. Healthcare, 11(9). https://doi.org/10.3390/healthcare11091298
Busch, F., Hoffmann, L., Truhn, D., Ortiz-Prado, E., Makowski, M. R., Bressem, K. K.,
Adams, L. C., & COMFORT Consortium. (2024). Global cross-sectional student
survey on AI in medical, dental, and veterinary education and practice at 192 faculties.
BMC Medical Education, 24(1), 1066. https://doi.org/10.1186/s12909-024-06035-4
Creswell, J. W. (2014). Research Design Qualitative, Quantitative and Mixed Methods
Approaches (4th ed.). Thousand Oaks, CA Sage. - References. (n.d.). Scientific
Research
Publishing.
https://www.scirp.org/reference/ReferencesPapers?ReferenceID=1964849
Creswell, J. W., & Creswell, J. D. (2020). Research Design Qualitative, Quantitative, and
Mixed Methods Approaches (5th ed.). SAGE. - References. (n.d.). Scientific Research
Publishing. https://www.scirp.org/reference/referencespapers?referenceid=3491413
Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods
Research (3rd ed.). Thousand Oaks, CA SAGE. - References. (n.d.). Scientific Research
Publishing. https://www.scirp.org/reference/referencespapers?referenceid=2697821
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. Management Information Systems Quarterly, 13(3), 319–340.
https://doi.org/10.2307/249008
Emmanuel, O. (2023). ASSESING FACTORS ASSOCIATED WITH PREVALENCE OF
ANEMIA AMONG CHILDREN UNDER FIVE YEARS OF AGE IN PAEDIATRIC
WARD AT LIRA REGIONAL REFERRAL HOSPITAL. A DESCRIPTIVE CROSS
SECTIONAL
STUDY.
[Students
Journal
of
Health
Research
Africa].https://sjhresearchafrica.org/index.php/public
html/article/download/382/337/2344
El-Banna, M. M., Sajid, M. R., Rizvi, M. R., Sami, W., & McNelis, A. M. (2025). AI literacy
and competency in nursing education: Preparing students and faculty members for an
AI-enabled future-a systematic review and meta-analysis. Frontiers in Medicine, 12,
1681784. https://doi.org/10.3389/fmed.2025.1681784
37
European commission. (2024). Ethics guidelines for trustworthy AI. Shaping Europe’s Digital
Future.
https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy
ai
Eysenbach, G., & Wyatt, J. (2002). Using the Internet for surveys and health research. Journal
of Medical Internet Research, 4(2), E13. https://doi.org/10.2196/jmir.4.2.e13
Feigerlova, E., Hani, H., & Hothersall-Davies, E. (2025). A systematic review of the impact of
artificial intelligence on educational outcomes in health professions education. BMC
Medical Education, 25(1), 129. https://doi.org/10.1186/s12909-025-06719-5
Issa, W. B., Shorbagi, A., Al-Sharman, A., Rababa, M., Al-Majeed, K., Radwan, H., Ahmed,
F. R., Al-Yateem, N., Mottershead, R., Abdelrahim, D. N., Hijazi, H., Khasawneh, W.,
Ali, I., Abbas, N., & Fakhry, R. (2024). Shaping the future: Perspectives on the
Integration of Artificial Intelligence in health profession education: A multi-country
survey. BMC Medical Education, 24(1), 1166-. https://doi.org/10.1186/s12909-024
06076-9
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang,
Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and
Vascular Neurology, 2(4). https://doi.org/10.1136/svn-2017-000101
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang,
Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and
Vascular Neurology, 2(4). https://doi.org/10.1136/svn-2017-000101
Kotecha, D., Asselbergs, F. W., Achenbach, S., Anker, S. D., Atar, D., Baigent, C., Banerjee,
A., Beger, B., Brobert, G., Casadei, B., Ceccarelli, C., Cowie, M. R., Crea, F., Cronin,
M., Denaxas, S., Derix, A., Fitzsimons, D., Fredriksson, M., Gale, C. P., … Grobbee,
D. E. (2022). CODE-EHR best-practice framework for the use of structured electronic
health-care records in clinical research. The Lancet Digital Health, 4(10), e757–e764.
https://doi.org/10.1016/S2589-7500(22)00151-0
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., Leon, L. D., Elepaño, C., Madriaga, M.,
Aggabao, R., Diaz-Candido, G., Maningo, J., & Tseng, V. (2023). Performance of
ChatGPT on USMLE: Potential for AI-assisted medical education using large language
models. PLOS Digital Health, 2(2). https://doi.org/10.1371/journal.pdig.0000198
Launiala, A. (2009). How much can a KAP survey tell us about people’s knowledge, attitudes
and practices? Some observations from medical anthropology research on malaria in
pregnancy
in
Malawi.
https://doi.org/10.22582/am.v11i1.31
Anthropology
Matters,
11(1).
38
Li, Q., & Qin, Y. (2023). AI in medical education: Medical student perception, curriculum
recommendations and design suggestions. BMC Medical Education, 23(1), 852-.
https://doi.org/10.1186/s12909-023-04700-8
Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to medical artificial
intelligence.
Journal
of
Consumer
Research,
46(4),
629–650.
https://doi.org/10.1093/jcr/ucz013
Ma, Q. (2005, January 1). The technology acceptance model: a meta-analysis of empirical
findings.
IGI
Global
Scientific
Publishing.
global.com/gateway/chapter/4475
Masters, K. (2019). Artificial intelligence in medical education. Medical Teacher, 41(9), 976
980. https://doi.org/10.1080/0142159x.2019.1595557
Matheny, M., Thadaney, S., & Beam, A. (2020). Artificial intelligence in health care: The
hope, the hype, the promise, the peril. NAM. https://nam.edu/publications/artificial
intelligence-in-health-care-the-hope-the-hype-the-promise-the-peril/
Mousavi Baigi, S. F., Sarbaz, M., Ghaddaripouri, K., Ghaddaripouri, M., Mousavi, A. S., &
Kimiafar, K. (2023). Attitudes, knowledge, and skills towards artificial intelligence
among healthcare students: A systematic review. Health Science Reports, 6(3), e1138.
https://doi.org/10.1002/hsr2.1138
Mukunya, D., Nantale, R., Paungholi, K., Epuitai, J., Kayemba, F., Jemba, B., Ajalo, E.,
Afodun, A., Musaba, M. W., Munabi, I. G., Kiguli, S., & Mubuuke, A. G. (2025). “I
can no longer give take-home exams”: Health professionals educators’ experiences and
perceptions regarding the use of artificial intelligence in health professions education
in Uganda. BMC Medical Education, 25(1), 1361-. https://doi.org/10.1186/s12909
025-07933-x
Munos, M. K., Blanc, A. K., Carter, E. D., Eisele, T. P., Gesuale, S., Katz, J., Marchant, T.,
Stanton, C. K., Campbell, H., & Improving Coverage Measurement Group. (2018).
Validation studies for population-based intervention coverage indicators: Design,
analysis,
and
interpretation.
Journal of Global Health, 8(2), 020804.
https://doi.org/10.7189/jogh.08.020804
Murphy, K., Ruggiero, E. D., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Malhotra,
N., Lui, V., & Gibson, J. (2021). Artificial intelligence for good health: A scoping
review of the ethics literature. BMC Medical Ethics, 22(1), 14-.
https://doi.org/10.1186/s12910-021-00577-8
39
Okolo, C. T., Aruleba, K., & Obaido, G. (2023, January 1). Responsible AI in Africa—
challenges
and
opportunities.
Springer
International
Publishing.
https://link.springer.com/chapter/10.1007/978-3-031-08215-3_3
Oladipo, E. K. (2024). Impact and challenges of artificial intelligence integration in the African
health sector: a review. https://tmr.scione.com/cms/fulltext.php?id=144
Parasuraman, R., & Manzey, D. H. (2010). Complacency and bias in human use of automation:
an attentional integration. Human Factors: The Journal of the Human Factors and
Ergonomics
Society,
52(3),
381–410.
https://doi.org/10.1177/0018720810376055 Polit, D.F. and Beck, C.T. (2017) Nursing
Research Generating and Assessing Evidence for Nursing Practice. 10th Edition,
Wolters Kluwer Health, Philadelphia, 784 p. - References. (n.d.). Scientific Research
Publishing. https://www.scirp.org/reference/referencespapers?referenceid=2325091
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England
Journal of Medicine, 380(14), 1347–1358. https://doi.org/10.1056/nejmra1814259
Sami, A., Tanveer, F., Sajwani, K., Kiran, N., Javed, M. A., Ozsahin, D. U., Muhammad, K.,
& Waheed, Y. (2025). Medical students’ attitudes toward AI in education: Perception,
effectiveness,
and
its
credibility.
BMC Medical Education, 25(1), 82.
https://doi.org/10.1186/s12909-025-06704-y
Sapci, A. H. (2020). Artificial intelligence education and tools for medical and health
informatics students: Systematic review. JMIR Medical Education, 6(1).
Seneviratne, T., Kodikara, K., Abeykoon, I., & Palpola, W. (2025). Perception of medical
undergraduates on artificial intelligence in medical education: Qualitative exploration.
JMIR Medical Education, 11, e73798. https://doi.org/10.2196/73798
Santos, D. P. dos, Giese, D., Brodehl, S., Chon, S. H., Staab, W., Kleinert, R., Maintz, D., &
Baeßler, B. (2018). Medical students’ attitude towards artificial intelligence: A
multicentre
survey.
European
Radiology,
29(4),
1640–1646.
https://doi.org/10.1007/s00330-018-5601-1
Shaw, K., Henning, M. A., & Webster, C. S. (2025). Artificial intelligence in medical
education: A scoping review of the evidence for efficacy and future directions. Medical
Science Educator, 35(3), 1803–1816. https://doi.org/10.1007/s40670-025-02373-0
Shishehgar, S., Murray-Parahi, P., Alsharaydeh, E., Mills, S., & Liu, X. (2025). Artificial
intelligence in health education and practice: A systematic review of health students’
40
and academics’ knowledge, perceptions and experiences. International Nursing
Review, 72(2), e70045. https://doi.org/10.1111/inr.70045
Srinivasu, P. N., Sandhya, N., Jhaveri, R. H., & Raut, R. (n.d.). From blackbox to explainable
AI in healthcare: existing tools and case studies. Mobile Information Systems, 2022(1).
https://doi.org/10.1155/2022/8167821
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
Tung, A. Y. Z., & Dong, L. W. (2023). Malaysian medical students’ attitudes and readiness
toward AI (artificial intelligence): A cross-sectional study. Journal of Medical
Education
and
Curricular
Development,
10,
23821205231201164.
https://doi.org/10.1177/23821205231201164
Vasudevan, R., Alqahtani, T., Alqahtani, S., Devanandan, P., Kandasamy, G., Saad, R., Amer,
A., Abduallah, R., Waleed, G., Hasan, R., & Ahmed, L. (2025). Integrating AI in
healthcare education: Attitudes of pharmacy students at King Khalid University
towards using ChatGPT in clinical decision-making. Healthcare, 13(11).
https://doi.org/10.3390/healthcare13111265
Vojdani, S., Najibi, S. M., Niknam, B., Daneshfard, B., Salehi-Marzijarani, M., Azgomi, R. N.
D., & Hashempur, M. H. (n.d.). Evaluation of knowledge, attitude, and practice of
Iranian medical students toward complementary and alternative medicine: a cross
sectional survey. Health Science Reports, 8(3). https://doi.org/10.1002/hsr2.70539
WHO. (2021, June 28). Ethics and governance of artificial intelligence for health. World Health
Organization. https://www.who.int/publications/i/item/9789240029200
Wobo, K. N., Nnamani, I. O., Alinnor, E. A., Gabriel-Job, N., & Paul, N. (2025). Medical
students’ perception of the use of artificial intelligence in medical education.
International
Journal
of
Research in Medical Sciences, 13(1), 82–89.
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