Medical students' AI literacy and attitudes towards AI: a cross-sectional two-center study using pre-validated assessment instruments

Matthias Carl Laupichler; Alexandra Aster; Marcel Meyerheim; Tobias Raupach; Marvin Mergen

doi:10.1186/s12909-024-05400-7

Medical students' AI literacy and attitudes towards AI: a cross-sectional two-center study using pre-validated assessment instruments

BMC Med Educ. 2024 Apr 10;24(1):401. doi: 10.1186/s12909-024-05400-7.

Authors

Matthias Carl Laupichler¹, Alexandra Aster², Marcel Meyerheim³, Tobias Raupach², Marvin Mergen³

Affiliations

¹ Institute of Medical Education, University Hospital Bonn, Venusberg Campus 1, 53127, Bonn, Germany. matthias.laupichler@ukbonn.de.
² Institute of Medical Education, University Hospital Bonn, Venusberg Campus 1, 53127, Bonn, Germany.
³ Department of Pediatric Oncology and Hematology, Faculty of Medicine, Saarland University, Homburg, Germany.

Abstract

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.

Keywords: AI literacy; Artificial intelligence; Attitudes towards AI; Confirmatory factor analysis; Medical students; Questionnaire.

MeSH terms

Artificial Intelligence
Attitude of Health Personnel
Cross-Sectional Studies
Female
Humans
Literacy
Male
Students, Medical*
Surveys and Questionnaires