Medical students’ AI literacy and attitudes towards AI: a cross-sectional two-center study using pre-validated assessment instruments
Open accessAbstract
Background: Artificial intelligence (AI) is becoming increasingly important in healthcare. It is therefore crucial that today’s medical students have certain basic AI skills that enable them to use AI applications successfully. These basic skills are often referred to as “AI literacy”. Previous research projects that aimed to investigate medical students’ AI literacy and attitudes towards AI have not used reliable and validated assessment instruments. Methods: We used two validated self-assessment scales to measure AI literacy (31 Likert-type items) and attitudes towards AI (5 Likert-type items) at two German medical schools. The scales were distributed to the medical students through an online questionnaire. The final sample consisted of a total of 377 medical students. We conducted a confirmatory factor analysis and calculated the internal consistency of the scales to check whether the scales were sufficiently reliable to be used in our sample. In addition, we calculated t-tests to determine group differences and Pearson’s and Kendall’s correlation coefficients to examine associations between individual variables. Results: The model fit and internal consistency of the scales were satisfactory. Within the concept of AI literacy, we found that medical students at both medical schools rated their technical understanding of AI significantly lower ( M MS1 = 2.85 and M MS2 = 2.50) than their ability to critically appraise ( M MS1 = 4.99 and M MS2 = 4.83) or practically use AI ( M MS1 = 4.52 and M MS2 = 4.32), which reveals a discrepancy of skills. In addition, female medical students rated their overall AI literacy significantly lower than male medical students, t (217.96) = -3.65, p <.001. Students in both samples seemed to be more accepting of AI than fearful of the technology, t (745.42) = 11.72, p <.001. Furthermore, we discovered a strong positive correlation between AI literacy and positive attitudes towards AI and a weak negative correlation between AI literacy and negative attitudes. Finally, we found that prior AI education and interest in AI is positively correlated with medical students’ AI literacy. Conclusions: Courses to increase the AI literacy of medical students should focus more on technical aspects. There also appears to be a correlation between AI literacy and attitudes towards AI, which should be considered when planning AI courses.
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SNAIL has been revalidated in another three studies, including one with comparative self-assessment gains.
“SNAIL45 was constructed on an elaborate Delphi study63, it has been revalidated in another three studies49–51 including one with comparative self-assessment gains49, it is one of the few scales with evidence of reliability and responsiveness, and it demonstrated good structural validity and internal consistency, which makes it a promising instrument.”
From A systematic review of AI literacy scales (2024) · cites this paper as [51]
The Results (SNAIL and ATAI model fit; page unavailable) report good three-factor model fit and internal consistency, supporting this paper as a further validation of SNAIL. The Results (Effect of AI education and interest; page unavailable) compare students who had and had not attended an AI course and find higher self-rated literacy among attendees. Because the study is cross-sectional, that comparison does not establish self-assessment gains over time. (LLM, conf=0.86)
Experimental — accuracy not yet measuredIn medical schools the technical aspects of GAI literacy need to be enhanced, and there is a positive correlation between AI literacy and positive attitudes toward GAI.
“In medical schools the technical aspects of GAI literacy need to be enhanced, there is a positive correlation between AI literacy and positive attitudes toward GAI [36].”
From Generative artificial intelligence in physiotherapy education: great potential amidst challenges- a qualitative interview study (2025) · cites this paper as [36]
The Abstract (Results and Conclusions; page not provided) recommends more attention to technical aspects of medical students’ AI literacy and reports a positive correlation between AI literacy and positive attitudes toward AI. The Discussion (Interpretation and implications of the results; page not provided) likewise describes that correlation. These findings concern AI generally; the cited passages do not establish the claim specifically for generative AI (GAI). (LLM, conf=0.88)
Experimental — accuracy not yet measuredBoth learners and teachers are required to have the necessary knowledge and skills to benefit effectively from GenAI and integrate it into the learning process.
“In other words, both learners and teachers are required to have the necessary knowledge and skills to benefit effectively from these novel technologies and integrate them into the learning process (Kong et al., 2025;Laupichler et al., 2024).”
From Development and validation of the Generative AI Literacy for Learning Scale (GenAI-LLs) (2025) · cites this paper as Laupichler et al., 2024
The Background says AI applications are used by students and educators to improve teaching and learning, and the Discussion argues that medical students need AI literacy to use AI effectively. It does not make the same skills requirement for teachers or specifically for generative AI; its mention of ChatGPT in the Limitations does not establish either point. No page numbers are available in our copy. (LLM, conf=0.87)
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References 35 · Cited by 170
- Authors
- Laupichler, M. C., Aster, A., Meyerheim, M., Raupach, T., & Mergen, M.
- Affiliations
- University Hospital Bonn (DE)
- Saarland University (DE)
- Corresponding author
- Laupichler, Matthias Carl
- Venue
- BMC Medical Education
- Published
- 2024-04-10
- Volume
- 24
- Issue
- 1
- Pages
- 401-401
- ISSN
- 1472-6920 (ISSN-L)
- PMC
- PMC11007897
- OpenAlex
- W4394693297
- Cited by
- 170
- References
- 35
- Citations by year
- 2026: 102 · 2025: 60 · 2024: 8
- FWCI
- 15.93
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Medical Education (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Universitätsklinikum Bonn
- 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 › Digital literacy in education
- Keywords
- AI literacy, attitudes toward AI, medical students, artificial intelligence in education, gender differences, scale validation, artificial intelligence in healthcare, medical curriculum
- MeSH terms
- Literacy, Artificial Intelligence, Attitude of Health Personnel, Cross-Sectional Studies, Female, Humans, Male, Surveys and Questionnaires, Students, Medical
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