Trial reportBMC medical education2026
The effect of artificial intelligence-based scenarios on the clinical education of rehabilitation students: an anatomy-based randomized controlled study.
Trial report in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparison of artificial intelligence assisted training and traditional learning paths in clinical simulation skills training: meta-analysis of randomized controlled trials.Frontiers in medicine · 2026Pooled it
- AI-assisted learning in exercise physiology: a quasi-experimental study using PhysioExercise GPT.Frontiers in physiology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aims to investigate the effects of using patient scenarios generated by artificial intelligence in rehabilitation education through different training models (artificial intelligence-based, internet-supported + traditional, and traditional only) on students’ digital competence, clinical self-efficacy, and attitudes towards artificial intelligence.
methodsNinety volunteer students were included in the study and divided into three groups using block randomisation: (1) artificial intelligence-supported (2), internet-supported + traditional method, and (3) traditional method only. An 8-week training programme was conducted for each scenario, consisting of weekly 90-minute sessions that alternated between assessment and treatment applications. The Digital Competence Self-Assessment Scale, Clinical Self-Efficacy Scale, and Artificial Intelligence Attitude Scale were administered before and after the intervention. One-way ANOVA or Kruskal–Wallis tests were used for between-group comparisons, and paired t-tests were used for within-group changes (α = 0.05).
resultsIn the intra-group analyses, a significant increase was observed in clinical self-efficacy and artificial intelligence attitude scores in all groups (p < .05). Digital competence increased in the AI-supported and internet-supported + traditional groups (p < .05). Intergroup comparisons revealed significant differences in digital competence and AI attitude scores (p < .05). The increase in clinical self-efficacy scores was not significant at the intergroup level (p > .05).
conclusionArtificial intelligence-based scenario training increases the level of digital competence in rehabilitation students and develops positive attitudes towards artificial intelligence. The findings indicate that this method can be integrated into educational programmes to strengthen clinical training processes. Further studies with larger samples, longer-term interventions, and objective performance measures are recommended to understand the effects on clinical skills.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.