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Peer-reviewedCC BYBMC Medical Education

AI information literacy in healthcare context: profiles and associated variables among healthcare professionals

  • Lilach Alon1
  • Inbar Levkovich1
  1. 1Faculty of Education and Teaching, Tel-Hai University of Kiryat Shmona in the Galilee
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Abstract

This study examines AI information literacy among healthcare professionals and identifies differences across professional roles. An online survey was administered to 390 healthcare professionals, including 22.3% medical doctors, 42.1% nurses, and 35.6% health professionals. AI information literacy was measured using the AI Information Literacy Scale (AILIS), which captures perceived confidence across four dimensions: process/create, assess, retrieve, and ethics. Findings indicated that across AILIS dimensions, participants reported the greatest confidence in retrieving information with AI and the least confidence in critically assessing AI-generated outputs. Medical doctors scored significantly higher than nurses and health professionals on process/create and retrieve. Across the main regression models, five predictors were included: age, perceived AI knowledge, frequency of AI use, number of AI tools used, and self-perceived understanding of how AI tools work. Self-perceived understanding showed the strongest association with all four AI information literacy dimensions. The models explained between 27.3% and 48.3% of the variance. Age showed small negative associations in most models. Self-reported AI information literacy among healthcare professionals appeared uneven across dimensions, with relatively lower confidence in critical assessment. GenAI integration in healthcare should therefore be approached as a literacy and training challenge, with particular attention to evaluation skills and reflective understanding of AI outputs.

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Cite

BibTeXRIS
BibTeX
@article{alon2026information,
  title = {{AI information literacy in healthcare context: profiles and associated variables among healthcare professionals}},
  author = {Lilach Alon and Inbar Levkovich},
  journal = {BMC Medical Education},
  year = {2026},
  month = jul,
  doi = {10.1186/s12909-026-10027-x},
  url = {https://doi.org/10.1186/s12909-026-10027-x},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - AI information literacy in healthcare context: profiles and associated variables among healthcare professionals
AU  - Lilach Alon
AU  - Inbar Levkovich
PY  - 2026
DA  - 2026/07/01
JO  - BMC Medical Education
DO  - 10.1186/s12909-026-10027-x
UR  - https://doi.org/10.1186/s12909-026-10027-x
AB  - This study examines AI information literacy among healthcare professionals and identifies differences across professional roles. An online survey was administered to 390 healthcare professionals, including 22.3% medical doctors, 42.1% nurses, and 35.6% health professionals. AI information literacy was measured using the AI Information Literacy Scale (AILIS), which captures perceived confidence across four dimensions: process/create, assess, retrieve, and ethics. Findings indicated that across AILIS dimensions, participants reported the greatest confidence in retrieving information with AI and the least confidence in critically assessing AI-generated outputs. Medical doctors scored significantly higher than nurses and health professionals on process/create and retrieve. Across the main regression models, five predictors were included: age, perceived AI knowledge, frequency of AI use, number of AI tools used, and self-perceived understanding of how AI tools work. Self-perceived understanding showed the strongest association with all four AI information literacy dimensions. The models explained between 27.3% and 48.3% of the variance. Age showed small negative associations in most models. Self-reported AI information literacy among healthcare professionals appeared uneven across dimensions, with relatively lower confidence in critical assessment. GenAI integration in healthcare should therefore be approached as a literacy and training challenge, with particular attention to evaluation skills and reflective understanding of AI outputs.
SN  - 1472-6920
ER  -