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Cited by 62Artificial Intelligence in Healthcare and EducationInnovations in Medical EducationArtificial Intelligence in Education

Navigating the integration of artificial intelligence in the medical education curriculum: a mixed-methods study exploring the perspectives of medical students and faculty in Pakistan

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Naseer, Muhammad Ahsan, Saeed, Sana, Afzal, Azam, Ali, Sobia, Malik, Marib Ghulam Rasool·2025·BMC Medical Education
DOI 10.1186/s12909-024-06552-2PMC PMC11844081
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Abstract

BACKGROUND: The integration of artificial intelligence (AI) into medical education is poised to revolutionize teaching, learning, and clinical practice. However, successful implementation of AI-based tools in medical curricula faces several challenges, particularly in resource-limited settings like Pakistan, where technological and institutional barriers remain significant. This study aimed to evaluate knowledge, attitudes, and practices of medical students and faculty regarding AI in medical education, and explore the perceptions and key barriers regarding strategies for effective AI integration. METHODS: A concurrent mixed-methods study was conducted over six months (July 2023 to January 2024) at a tertiary care medical college in Pakistan. The quantitative component utilized a cross-sectional design, with 236 participants (153 medical students and 83 faculty members) completing an online survey. Mean composite scores for knowledge, attitudes, and practices were analyzed using non-parametric tests. The qualitative component consisted of three focus group discussions with students and six in-depth interviews with faculty. Thematic analysis was performed to explore participants' perspectives on AI integration. RESULTS: Majority of participants demonstrated a positive attitude towards AI integration. Faculty had significantly higher mean attitude scores compared to students (3.95 ± 0.63 vs. 3.81 ± 0.75, p = 0.040). However, no statistically significant differences in knowledge (faculty: 3.53 ± 0.66, students: 3.55 ± 0.73, p = 0.870) or practices (faculty: 3.19 ± 0.87, students: 3.23 ± 0.89, p = 0.891) were found. Older students reported greater self-perceived knowledge (p = 0.010) and more positive attitudes (p = 0.016) towards AI, while male students exhibited higher knowledge scores than females (p = 0.025). Qualitative findings revealed key themes, including AI's potential to enhance learning and research, concerns about over-reliance on AI, ethical issues surrounding privacy and confidentiality, and the need for institutional support. Faculty emphasized the importance of training to equip educators with the necessary skills to effectively integrate AI into their teaching. CONCLUSIONS: This study highlights both the enthusiasm for AI integration and the significant barriers that must be addressed to successfully implement AI in medical education. Addressing technological constraints, providing faculty training, and developing ethical guidelines are critical steps toward fostering the responsible use of AI in medical curricula. These findings underscore the need for context-specific strategies, particularly in resource-limited settings, to ensure that medical students and educators are well-prepared for the future of healthcare.

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  • Externalization of cognitive work to AI systems may weaken self-regulated learning and independent problem-solving when used as a substitute rather than a scaffold.

    “This pattern suggests the externalization of cognitive work to AI systems, echoing concerns that generative AI may weaken self-regulated learning and independent problem-solving when used as a substitute rather than a scaffold [5, 19, 38].”

    From From adoption to interaction: examining human-GenAI engagement patterns, ethical tensions, and educational trade-offs in nursing education (2026) · cites this paper as [38]

    In Results, under “Theme-specific insights: faculty perspectives” (page unavailable), faculty caution that over-reliance on AI could undermine critical thinking and problem-solving, and recommend using it as a supplement rather than a replacement. The paper does not say that such use weakens self-regulated learning; its Results instead describe AI as potentially useful for self-directed learning. (LLM, conf=0.84)

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  1. [2]Johnson L, Johnson L, Becker SA, Cummins M, Estrada V, Freeman A, et al. NMC Horizon Report: 2016 Higher Education Edition. New Media Consortium; 2016. 1–50 p. Available from: https://www.learntechlib.org/p/171478/. [cited 2024 Sep 6].
  2. [5]Masoumian M, Hosseini T, Qayumi K. Integration of Artificial Intelligence in Medical Education: Opportunities, Challenges, and Ethical Considerations. J Med Educ. 2024Jan;1:22.
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Authors
Naseer, M. A., Saeed, S., Afzal, A., Ali, S., & Malik, M. G. R.
Affiliations
  • Liaquat National Hospital (PK)
  • Aga Khan University (PK)
Corresponding author
Saeed, Sana
ORCID
Venue
BMC Medical Education
Publisher
Springer Science and Business Media LLC
Published
2025-02-20
Volume
25
Issue
1
Pages
273-273
ISSN
1472-6920 (ISSN-L)
DOI
10.1186/s12909-024-06552-2
PMC
PMC11844081
OpenAlex
W4407771049
Cited by
60
References
23
Citations by year
2026: 36 · 2025: 24
FWCI
14.44
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medical Education (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
Keywords
artificial intelligence, medical education, curriculum integration, medical students' attitudes, teacher perceptions, AI adoption barriers, faculty training, AI ethics, privacy and confidentiality, resource-limited settings, Pakistan, educational technology implementation, overreliance on AI, responsible AI
MeSH terms
Adult, Artificial Intelligence, Attitude of Health Personnel, Cross-Sectional Studies, Curriculum, Education, Medical, Education, Medical, Undergraduate, Faculty, Medical, Female, Humans, Health Knowledge, Attitudes, Practice, Male, Pakistan, Surveys and Questionnaires, Students, Medical, Focus Groups, Young Adult

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