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Background <p>This 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.</p> Methods <p>Ninety 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).</p> Results <p>In the intra-group analyses, a significant increase was observed in clinical self-efficacy and artificial intelligence attitude scores in all groups (<i>p</i> &lt; .05). Digital competence increased in the AI-supported and internet-supported + traditional groups (<i>p</i> &lt; .05). Intergroup comparisons revealed significant differences in digital competence and AI attitude scores (<i>p</i> &lt; .05). The increase in clinical self-efficacy scores was not significant at the intergroup level (<i>p</i> &gt; .05).</p> Conclusion <p>Artificial 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.</p>

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The effect of artificial intelligence–based scenarios on the clinical education of rehabilitation students: an anatomy-based randomized controlled study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月14日

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  • Rıdvan Yıldız,
  • Onur Seçgin Ni̇şanci

摘要

Background

This 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.

Methods

Ninety 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).

Results

In 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).

Conclusion

Artificial 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.