Assessing ChatGPT adoption in Jordanian medical education: a UTAUT model approach.

IF 2.7 2区医学 Q1 EDUCATION & EDUCATIONAL RESEARCH

BMC Medical Education Pub Date : 2025-05-22 DOI:10.1186/s12909-025-07336-y

Noura Alqaisi, Sakhr Alshwayyat, Saif Aburumman, Nour Qassim, Noor Almasri, Fatima Algroosh, Mesk Alkhatib, Hamdah Hanifa, Saif Aldeen AlRyalat

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引用次数: 0

Abstract

Background: ChatGPT has shown significant promise in transforming medical education by streamlining research and improving teaching methods. However, its adoption in Middle Eastern medical education has remained underexplored. This study investigated the factors influencing the adoption of ChatGPT in Jordanian medical education using a modified Unified Theory of Acceptance and Use of Technology (UTAUT) framework.

Methods: A cross-sectional survey was conducted with medical students and faculty members at the University of Jordan. A validated 33-item questionnaire distributed online and on campus targeted individuals familiar with the ChatGPT. Structural equation modeling (SEM) assessed the relationships between key constructs, including Performance Expectancy (PE), Effort Expectancy (EE), Perceived Risk (PR), Facilitating Conditions (FC), and attitude (ATT).

Results: Among 127 participants (53% male, mean age 23.2 ± 7.6), ATT was significantly influenced by PE and EE, explaining 37% of its variance. Behavioral Intention (BI) was predicted by ATT and had a significant positive effect on actual usage. FC did not significantly influence EE or BI, suggesting a limited reliance on external support. Contrary to expectations, PR did not negatively affect ATT, indicating that utility outweighed concerns about misinformation or privacy. Overall, the model explained 53% of the variance in BI and 36.5% of the variance in actual usage.

Conclusion: The adoption of ChatGPT in Jordanian medical education is driven by perceived utility and ease of use, with attitudes playing a pivotal role. Addressing misinformation risks and improving trust through tailored strategies can foster broader integration of AI tools, such as ChatGPT, in medical training.

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评估约旦医学教育中ChatGPT的采用：UTAUT模型方法。

背景：ChatGPT通过精简研究和改进教学方法，在改变医学教育方面显示出巨大的希望。然而，它在中东医学教育中的应用仍未得到充分探索。本研究采用改进的技术接受和使用统一理论（UTAUT）框架调查了约旦医学教育中采用ChatGPT的影响因素。方法：对约旦大学的医学生和教师进行横断面调查。一份经过验证的33项问卷在网上和校园分发，目标人群是熟悉ChatGPT的人。结构方程模型（SEM）评估了关键构念之间的关系，包括绩效期望（PE）、努力期望（EE）、感知风险（PR）、促进条件（FC）和态度（ATT）。结果：在127名参与者中（53%为男性，平均年龄23.2±7.6岁），ATT受到PE和EE的显著影响，解释了37%的方差。行为意向（BI）可通过ATT预测，对实际使用有显著的正向影响。FC对EE或BI没有显著影响，表明对外部支持的依赖有限。与预期相反，PR并没有对ATT产生负面影响，这表明实用性超过了对错误信息或隐私的担忧。总的来说，该模型解释了商业智能中53%的差异和实际使用中36.5%的差异。结论：约旦医学教育采用ChatGPT是由感知效用和易用性驱动的，态度起着关键作用。通过量身定制的战略解决错误信息风险并改善信任，可以促进人工智能工具（如ChatGPT）在医疗培训中的更广泛整合。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

BMC Medical Education EDUCATION, SCIENTIFIC DISCIPLINES-

CiteScore

4.90

自引率

11.10%

发文量

795

审稿时长

6 months

期刊介绍： BMC Medical Education is an open access journal publishing original peer-reviewed research articles in relation to the training of healthcare professionals, including undergraduate, postgraduate, and continuing education. The journal has a special focus on curriculum development, evaluations of performance, assessment of training needs and evidence-based medicine.