在使用人工智能聊天机器人辅助数学时,影响学习者的学习兴趣的因素倾向于高等教育
1School of Economics and Management, Zhejiang University of Water Resources and Electric Power, Hangzhou, China.
Frontiers in psychology
|December 22, 2025
概括
努力期望,绩效期望和自我效能显著推动了对人工智能数学教育的兴趣. 预期的努力影响最大,这表明人工智能工具可以通过专注于易用性和感知的好处来提高学生的参与度.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 学习心理学学习的心理学.
背景情况:
- 了解影响学生参与AI支持的学习环境的因素至关重要.
- 现有的UTAUT和社会认知理论等模型为技术接受提供了框架.
- 在人工智能驱动的数学教育中研究动机因素解决了认知要求高的学科领域的差距.
研究的目的:
- 检查本科生对人工智能支持的数学教育感兴趣的决定因素.
- 测试一个结构模型,将技术接受结构与激励因素整合起来.
- 探索数学对焦虑,自我效能,绩效预期和努力预期的作用.
主要方法:
- 一项涉及247名中国本科生的定量研究.
- 使用部分最小平方结构方程建模 (PLS-SEM) 进行数据分析.
- 基于技术接受和使用统一理论 (UTAUT) 和社会认知理论的结构模型测试.
主要成果:
- 绩效预期,努力预期和自我效能显著预测了学习兴趣.
- 努力预期表现出对学习兴趣产生最强烈的积极影响.
- 数学焦虑没有显著预测自我效能或学习兴趣;学术专业调节了这种关系.
- 自我有效性通过感知系统的有用性和易用性间接影响了学习兴趣.
结论:
- 人工智能支持的数学教育可以通过专注于感知到的有用性和易用性来增强,这是由努力和绩效预期驱动的.
- 学习者参与人工智能数学教育受到自我效能和努力/绩效期望的积极影响.
- 人工智能环境可能会减轻数学焦虑,影响因学术专业而异,突出需要量身定制的干预措施和以用户为中心的设计.
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