Perceived artificial intelligence readiness in medical and health sciences education: a survey study of students in Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: As artificial intelligence (AI) becomes increasingly integral to healthcare, preparing medical and health sciences students to engage with AI technologies is critical. OBJECTIVES: This study investigates the perceived AI readiness of medical and health sciences students in Saudi Arabia, focusing on four domains: cognition, ability, vision, and ethical perspectives, using the Medical Artificial Intelligences Readiness Scale for Medical Students (MAIRS-MS). METHODS: A cross-sectional survey was conducted between October and November 2023, targeting students from various universities and medical schools in Saudi Arabia. A total of 1,221 students e-consented to participate. Data were collected via a 20-minute Google Form survey, incorporating a 22-item MAIRS-MS scale. Descriptive and multivariate statistical analyses were performed using Stata version 16.0. Cronbach alpha was calculated to ensure reliability, and least squares linear regression was used to explore relationships between students' demographics and their AI readiness scores. RESULTS: The overall mean AI readiness score was 62 out of 110, indicating a moderate level of readiness. Domain-specific scores revealed generally consistent levels of readiness: cognition (58%, 23.2/40), ability (57%, 22.8/40), vision (54%, 8.1/15) and ethics (57%, 8.5/15). Nearly 44.5% of students believed AI-related courses should be mandatory whereas only 41% reported having such a required course in their program. CONCLUSIONS: Medical and health sciences students in Saudi Arabia demonstrate moderate AI readiness across cognition, ability, vision, and ethics, indicating both a solid foundation and areas for growth. Enhancing AI curricula and emphasizing practical, ethical, and forward-thinking skills can better equip future healthcare professionals for an AI-driven future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".