Technology-enhanced learning in medical education in the age of artificial intelligence

Forum for education studies., Год журнала: 2025, Номер 3(2), С. 2730 - 2730

Опубликована: Апрель 1, 2025

This paper explores the transformative role of artificial intelligence (AI) in medical education, emphasizing its as a pedagogical tool for technology-enhanced learning. highlights AI’s potential to enhance learning process various inquiry-based strategies and support Competency-Based Medical Education (CBME) by generating high-quality assessment items with automated personalized feedback, analyzing data from both human supervisors AI, helping predict future professional behavior current trainees. It also addresses inherent challenges limitations using AI student assessment, calling guidelines ensure valid ethical use. Furthermore, integration into virtual patient (VP) technology offer experiences encounters significantly enhances interactivity realism overcoming conventional VPs. Although incorporating chatbots VPs is promising, further research warranted their generalizability across clinical scenarios. The discusses preferences Generation Z learners suggests conceptual framework on how integrate teaching supporting learning, aligning needs today’s students utilizing adaptive capabilities AI. Overall, this areas education where can play pivotal roles overcome educational offers perspectives developments education. calls advance theory practice tools innovate practices tailored understand long-term impacts AI-driven environments.

Язык: Английский

Mapping artificial intelligence models in emergency medicine: A scoping review on artificial intelligence performance in emergency care and education

Turkish Journal of Emergency Medicine, Год журнала: 2025, Номер 25(2), С. 67 - 91

Опубликована: Апрель 1, 2025

Artificial intelligence (AI) is increasingly improving the processes such as emergency patient care and medicine education. This scoping review aims to map use performance of AI models in regarding concepts. The findings show that AI-based medical imaging systems provide disease detection with 85%-90% accuracy techniques X-ray computed tomography scans. In addition, AI-supported triage were found be successful correctly classifying low- high-urgency patients. education, large language have provided high rates evaluating exams. However, there are still challenges integration into clinical workflows model generalization capacity. These demonstrate potential updated models, but larger-scale studies needed.

Язык: Английский

The impact of student-generative artificial intelligence interaction on educational interaction in Chinese nursing students: the mediating role of self-regulated learning

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Апрель 10, 2025

Background Generative artificial intelligence (GAI) is reshaping the medical education field. For nursing students, educational interaction, self-regulated learning, and GAI interaction are all particularly important. However, relationships among student-GAI learning as well underlying mechanisms, remain underexplored. Methods In January 2025, 1367 students were recruited from one university in Hunan Province China. The Demographics Questionnaire, Chinese version of Student–GAI scale, Educational Interaction Scale, Self-Regulated Learning Questionnaire administered. SPSS 22.0 AMOS 24.0 software used to analyze data. Results mean scores 15.12 ± 2.23 71.26 8.18, respectively. There significantly positive (all p < 0.01). regression analysis results indicate that both student–GAI (β = 0.280, P 0.001) 0.596, significant predictors interactions. Furthermore, exerted a substantial direct effect on interaction,with value 0.99 (P 0.001). Self-regulated mediated path with indirect 0.85. mediating accounted for 46.19% total Conclusions Student-GAI an important factor influencing interaction. It can directly affect also influence it indirectly through learning.

Язык: Английский