利用生成性人工智能来促进元认知和自我指导的学习.
Brandon Lowry1, Samantha McGrath1, Chad Eitel1
1Department of Biomedical Sciences, Colorado State University, Fort Collins, Colorado, USA.
Journal of microbiology & biology education
|November 26, 2025
概括
生成型人工智能 (AI) 可以促进STEM研究生的元认知和自主学习技能. 与人工智能模型的定期,简短的互动显著改善了学生的学习.
科学领域:
- 教育 技术 技术 教育 技术
- 教育中的人工智能
- 在STEM教育方面.
背景情况:
- 越来越多的数据需要自主学习 (SDL) 才能获得学生的成功,特别是在科学,技术,工程和数学 (STEM) 领域.
- STEM学科面临着很高的消耗率,部分原因是学生们对SDL的斗争和丧,加剧了缺乏课程机会来发展这些技能.
- 超认知与SDL准备密切相关,这表明了提高学生自主性和在具有挑战性的学术领域的坚持的潜在途径.
研究的目的:
- 探索生成人工智能 (AI) 模型在培养元认知和促进研究生中SDL准备的有效性.
- 利用人工智能的可定制性来促进学生与人工智能的对话,旨在提高元认知意识和SDL技能.
- 调查人工智能辅助元认知发展的最佳交互频率和持续时间.
主要方法:
- 使用生成性AI模型,让研究生参与旨在促进元认知的对话互动.
- 超认知意识清单 (MAI) 和自导学习工具 (SDLI) 作为前后评估进行.
- 学生每周两次与AI模型互动,在一定的时间内进行10分钟的会议.
主要成果:
- 从评估前到评估后,在元认知意识和SDL准备度得分上都观察到显著的改善.
- 干预表明,有针对性的,简短的AI互动可以在关键学习能力方面产生可衡量的收益.
- 这项研究建立了一个可行的模型,用于为学生提供人工智能驱动的个人发展支持.
结论:
- 生成型人工智能为培养元认知和提高研究生SDL准备能力提供了一种新且有效的工具,特别是在STEM领域.
- 人工智能可以充当支持性流程促进者,超越其仅仅作为答案生成工具的角色.
- 这项研究有助于了解人工智能如何被整合到教育环境中,以促进学生的基本技能和减少消耗.
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