人工智能增强的自适应测试与认知诊断反及其与本科外科教育绩效的关联:试点研究
Nuno Silva Gonçalves1,2, Carlos Collares1,3,4,5,6, José Miguel Pêgo1,2,4,7
1Life and Health Sciences Research Institute (ICVS), University of Minho, Braga, Portugal.
Frontiers in behavioral neuroscience
|January 22, 2026
概括
人工智能 (AI) 增强的计算机自适应测试 (CAT) 在外科教育中显示出前景,与更高的测试成绩相关. 在这个人工智能驱动的评估模型中,记忆能力显著预测了学生的表现.
科学领域:
- 医疗教育 技术 技术 医学教育
- 医疗保健中的人工智能
- 认知评估 认知评估
背景情况:
- 有效的认知反对于外科培训至关重要,但面临着资源和格式限制.
- 人工智能 (AI) 提供创新的解决方案,用于手术教育中的自动反,诊断和项目生成.
研究的目的:
- 评估人工智能辅助计算机自适应测试 (CAT) 对外科实习生的表现的影响.
- 评估认知领域 (记忆,分析,决策) 与外科教育中的总结结果之间的关系.
主要方法:
- 开发了一个由人工智能生成的项目库,包含150个多选题.
- 在总结性进度测试之前,在内存,分析和决策领域进行了人工智能辅助的形成性CAT.
- 分析了116名参与手术学生的表现相关性,组对比和回归.
主要成果:
- 完成形成性CAT的学生表现出更高的进步测试成绩 (p=0.021),即使在调整之前的表现后 (ANCOVA p=0.041).
- 记忆能力是总结结果最强的预测因素 (β=0.425),其次是分析能力 (β=0.283).
- 在这项研究中,决策能力与总结结果没有显著的关联.
结论:
- 使用人工智能增强的CAT与认知诊断建模 (CDM) 是本科外科教育中可行的形成性评估工具.
- 这种由人工智能驱动的方法与改进的总结性能有关,并提供个性化的诊断反.
- 进一步完善反机制和决策评估可以提高AI在外科培训中的教育实用性.
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