JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) is increasingly shaping health care, yet the AI preparedness of midwifery students remains underdocumented. Evidence is needed to inform midwifery-specific curriculum development and to clarify how students understand and operationalize AI in training and placements.

Objective:

This mixed-methods study aimed to assess French midwifery students’ AI readiness, training needs, and ethical/regulatory concerns.

Methods:

We conducted a national sequential explanatory mixed-methods study during the 2024–2025 academic year. A web-based survey (five previously translated/adapted questionnaires) was disseminated via midwifery schools/universities in France (30/33, 91%, institutions confirmed dissemination and responses were received from these 30 institutions). Eligible participants were students enrolled in years 2–5 of the French midwifery curriculum. We computed mean theme scores (1–5) with 95% confidence intervals (CIs) and assessed internal consistency using Cronbach α. Analyses were restricted to fully completed questionnaires. Semi-structured interviews were conducted with volunteer students from one midwifery school in Eastern France (n=8), transcribed verbatim, anonymized, and analyzed using thematic analysis. Mixed-methods integration used a joint display.

Results:

Of 414 survey entries, 190 were fully completed and kept for analysis (190/414, 46.1%). Mean theme scores for AI skills and knowledge were below the neutral midpoint, ranging from 1.20 (95% CI 1.09–1.23) for familiarity with advanced AI techniques to 2.89 (95% CI 2.48–3.31) for analytical concepts in AI for health. Perceived ability to use AI for clinical purposes was low (2.05, 95% CI 1.01–3.09). In contrast, students strongly endorsed AI education (belief that students and professionals should be trained: 4.11, 95% CI 3.96–4.26) and emphasized evidence and safety requirements (up to 4.04, 95% CI 3.84–4.24). Item-level results suggested “AI label ambiguity”: general AI familiarity showed higher agreement (91/190, 47.9%) than familiarity with specific concepts such as machine learning (20/190, 10.5%) or deep learning (13/190, 6.8%). Interviews aligned with these patterns, indicating rare exposure to explicitly identified AI-supported workflows in placements and describing mainly academic and informal uses of generative tools. Participants emphasized patient safety, accountability, and preservation of human judgment.

Conclusions:

French midwifery students report a substantial AI readiness gap characterized by both low technical preparedness and limited situated exposure during placements, despite strong demand for training and high salience of safety and governance. Findings support implementing a structured, progressive curriculum linked to midwifery-relevant clinical scenarios and aligned with placement ecosystems. Future measurement should explicitly distinguish generative AI practices from regulated clinical AI systems and capture safe-use behaviors to improve construct validity.