AI Literacy and Training Needs Among French Midwifery Students: National Cross-Sectional Online Survey and Exploratory Qualitative Follow-Up
Abstract
BackgroundAI is increasingly relevant to health care and health professional education, but AI-related knowledge, perceptions, and training needs among midwifery students remain underdocumented. Evidence is needed to inform midwifery curricula and to clarify how students understand and operationalize AI in training and placements. ObjectiveThis cross-sectional online study aimed to assess AI-related knowledge, perceptions, perceived risks, and training needs among French midwifery students. MethodsWe conducted a national cross-sectional online survey of students enrolled in years 2 to 5 of the French midwifery curriculum between January and May 2025, complemented by exploratory qualitative follow-up interviews. Of 34 midwifery schools and universities contacted, 30 confirmed dissemination of the survey link through institutional and student-association channels. The voluntary, anonymous survey combined 5 previously published instruments or questionnaires administered in linguistically validated French versions. Likert-type items were analyzed descriptively on a 1 to 5 scale at the item level and through post hoc descriptive thematic summaries across 5 domains and 23 themes. Analyses were restricted to fully completed survey entries, with no imputation, inferential testing, or subgroup comparisons. Eight volunteer students from 1 school in eastern France participated in semistructured interviews, which were analyzed thematically to provide exploratory contextual observations. ResultsOf 414 initiated survey entries, 190 (45.9%) were fully completed and analyzed. Self-reported AI knowledge and familiarity were below the scale midpoint, with the lowest score observed for familiarity with advanced AI techniques (1.20, 95% CI 1.13‐1.27). General AI familiarity was higher (n=91, 47.9% agreement) than familiarity with specific concepts such as machine learning (n=20, 10.5%) or deep learning (n=13, 6.8%). Perceived ability to use AI for clinical purposes was limited (2.05, 95% CI 1.93‐2.16). In contrast, students strongly endorsed AI education, particularly the belief that students and professionals should be trained in AI (4.11, 95% CI 4.02‐4.20). Prior exposure to AI (1.36, 95% CI 1.27‐1.45) and perceived institutional and social support (1.43, 95% CI 1.34‐1.52) were low. Ethics- and safety-related concerns were salient, with the highest score for evidence and safety requirements (4.04, 95% CI 3.96‐4.12). Exploratory interviews suggested that students often referred to AI through everyday generative tools, reported little supervised exposure to AI in clinical placements, and emphasized safety, privacy, accountability, and preservation of human judgment. ConclusionsInterpreted as a time-bound baseline, these findings suggest an AI readiness gap among French midwifery students, in which low perceived knowledge and limited clinical readiness coexist with strong demand for training and high salience of safety and governance. They support the progressive and proportionate introduction of AI literacy in midwifery education, explicitly distinguishing general AI, generative AI tools, and regulated clinical AI systems. Future work should develop and validate midwifery-specific AI literacy measures and evaluate curriculum interventions.
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BibTeX
@article{kotzki2026literacy,
title = {{AI Literacy and Training Needs Among French Midwifery Students: National Cross-Sectional Online Survey and Exploratory Qualitative Follow-Up}},
author = {Sylvain Kotzki and Sonia Knepfler and Lionel Di Marco and Nicolas Vuillerme},
journal = {JMIR Medical Education},
year = {2026},
month = oct,
doi = {10.2196/94310},
url = {https://doi.org/10.2196/94310},
note = {CC BY},
}
RIS
TY - JOUR
TI - AI Literacy and Training Needs Among French Midwifery Students: National Cross-Sectional Online Survey and Exploratory Qualitative Follow-Up
AU - Sylvain Kotzki
AU - Sonia Knepfler
AU - Lionel Di Marco
AU - Nicolas Vuillerme
PY - 2026
DA - 2026/10/01
JO - JMIR Medical Education
DO - 10.2196/94310
UR - https://doi.org/10.2196/94310
AB - BackgroundAI is increasingly relevant to health care and health professional education, but AI-related knowledge, perceptions, and training needs among midwifery students remain underdocumented. Evidence is needed to inform midwifery curricula and to clarify how students understand and operationalize AI in training and placements. ObjectiveThis cross-sectional online study aimed to assess AI-related knowledge, perceptions, perceived risks, and training needs among French midwifery students. MethodsWe conducted a national cross-sectional online survey of students enrolled in years 2 to 5 of the French midwifery curriculum between January and May 2025, complemented by exploratory qualitative follow-up interviews. Of 34 midwifery schools and universities contacted, 30 confirmed dissemination of the survey link through institutional and student-association channels. The voluntary, anonymous survey combined 5 previously published instruments or questionnaires administered in linguistically validated French versions. Likert-type items were analyzed descriptively on a 1 to 5 scale at the item level and through post hoc descriptive thematic summaries across 5 domains and 23 themes. Analyses were restricted to fully completed survey entries, with no imputation, inferential testing, or subgroup comparisons. Eight volunteer students from 1 school in eastern France participated in semistructured interviews, which were analyzed thematically to provide exploratory contextual observations. ResultsOf 414 initiated survey entries, 190 (45.9%) were fully completed and analyzed. Self-reported AI knowledge and familiarity were below the scale midpoint, with the lowest score observed for familiarity with advanced AI techniques (1.20, 95% CI 1.13‐1.27). General AI familiarity was higher (n=91, 47.9% agreement) than familiarity with specific concepts such as machine learning (n=20, 10.5%) or deep learning (n=13, 6.8%). Perceived ability to use AI for clinical purposes was limited (2.05, 95% CI 1.93‐2.16). In contrast, students strongly endorsed AI education, particularly the belief that students and professionals should be trained in AI (4.11, 95% CI 4.02‐4.20). Prior exposure to AI (1.36, 95% CI 1.27‐1.45) and perceived institutional and social support (1.43, 95% CI 1.34‐1.52) were low. Ethics- and safety-related concerns were salient, with the highest score for evidence and safety requirements (4.04, 95% CI 3.96‐4.12). Exploratory interviews suggested that students often referred to AI through everyday generative tools, reported little supervised exposure to AI in clinical placements, and emphasized safety, privacy, accountability, and preservation of human judgment. ConclusionsInterpreted as a time-bound baseline, these findings suggest an AI readiness gap among French midwifery students, in which low perceived knowledge and limited clinical readiness coexist with strong demand for training and high salience of safety and governance. They support the progressive and proportionate introduction of AI literacy in midwifery education, explicitly distinguishing general AI, generative AI tools, and regulated clinical AI systems. Future work should develop and validate midwifery-specific AI literacy measures and evaluate curriculum interventions.
SN - 2369-3762
ER -