Sunmboye Kehinde, Strafford Hannah, Noorestani Samina, Wilison-Pirie Malena
University Hospitals of Leicester, Leicester, UK.
University of Leicester, Leicester, UK.
BMC Med Educ. 2025 Apr 17;25(1):570. doi: 10.1186/s12909-025-07084-z.
With the integration of Artificial Intelligence (AI) into educational systems, its potential to revolutionize learning, particularly in content personalization and assessment support, is significant. Personalized learning, supported by AI tools, can adapt to individual learning styles and needs, thus transforming how medical students approach their studies. This study aims to explore the relationship between the use of AI for self-directed learning among undergraduate medical students in the UK and variables such as year of study, gender, and age.
This cross-sectional study involved a sample of 230 undergraduate medical students from UK universities, collected through an online survey. The survey assessed AI usage in self-directed learning, including students' attitudes towards AI accuracy, perceived benefits, and willingness to mitigate misinformation. Data were analyzed using descriptive statistics and linear logistic regression to examine associations between AI usage and demographics.
The analysis revealed that age significantly influenced students' willingness to pay for AI tools (p = 0.012) and gender was linked to concerns about AI inaccuracies (p = 0.017). Female students were more likely to take steps to mitigate risks of misinformation (p = 0.045). The study also found variability in AI usage based on the year of study, with first-year students showing a higher reliance on AI tools.
AI has the potential to greatly enhance personalized learning for medical students. However, issues surrounding accuracy, misinformation, and equitable access need to be addressed to optimize AI integration in medical education. Further research is recommended to explore the longitudinal effects of AI usage on learning outcomes.
随着人工智能（AI）融入教育系统，其在彻底改变学习方式方面的潜力巨大，尤其是在内容个性化和评估支持方面。由人工智能工具支持的个性化学习能够适应个体的学习风格和需求，从而改变医学生的学习方式。本研究旨在探讨英国本科医学生在自主学习中使用人工智能与学习年份、性别和年龄等变量之间的关系。
这项横断面研究通过在线调查收集了来自英国大学的230名本科医学生样本。该调查评估了人工智能在自主学习中的使用情况，包括学生对人工智能准确性的态度、感知到的益处以及减轻错误信息影响的意愿。使用描述性统计和线性逻辑回归分析数据，以检验人工智能使用与人口统计学特征之间的关联。
分析显示，年龄对学生为人工智能工具付费的意愿有显著影响（p = 0.012），性别与对人工智能不准确的担忧有关（p = 0.017）。女学生更有可能采取措施减轻错误信息的风险（p = 0.045）。研究还发现，根据学习年份不同，人工智能的使用存在差异，一年级学生对人工智能工具的依赖程度更高。
人工智能有潜力极大地增强医学生的个性化学习。然而，需要解决围绕准确性、错误信息和公平获取等问题，以优化人工智能在医学教育中的整合。建议进一步开展研究，以探索人工智能使用对学习成果的长期影响。
