通过AI驱动的个性化学习探索基础教育中的分数理解与学习兴趣
Exploring Fraction Comprehension and Interest in Elementary Education Through AI-Powered Personalized Learning
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中文总结 AI 辅助
该论文通过系统综述和准实验研究，评估AI个性化学习平台Mathbot在小学分数教学中的效果，发现其可适度提升学生分数理解，且未取代教师教学角色，为自适应AI系统在小学数学中的应用提供了课堂实证。
中文摘要 AI 辅助
人工智能系统能够根据个体学习者调整教学，目前正越来越多地被应用于K-12课堂，但关于其在真实小学环境中效果的实证证据仍然有限，尤其是针对有数学学习困难的学生。本论文研究了小学分数教学中AI驱动的个性化学习，分数教学是后续数学和STEM成就的基础领域。第一篇论文对2020年至2024年间发表的数学教育领域人工智能相关研究进行了系统综述。第二篇论文报告了一项准实验研究，评估了基于聊天机器人的个性化学习平台Mathbot与常规课堂教学的效果对比。采用重复测量方差分析评估分数理解和情境兴趣在各时间点的变化。结果表明，与传统教学相比，使用Mathbot的学生在分数理解上有适度提升，而情境兴趣的变化未达到统计学显著性。研究结果表明，自动化个性化教学并未取代教师的教学角色，教师的决策对学生成绩仍至关重要。本研究为正在进行的关于自适应AI系统在小学数学领域的能力与局限的讨论，以及此类系统用于残疾学生时的可及性与公平性考量，提供了课堂层面的实证证据。
英文摘要
Artificial intelligence systems that adapt instruction to individual learners are increasingly deployed in K-12 classrooms, yet empirical evidence on their effects in authentic elementary settings remains limited, particularly for students with mathematics learning difficulties. This dissertation examines AI-powered personalized learning during primary school fraction instruction, a domain that is foundational to later mathematics and STEM achievement. The first manuscript presents a systematic review of research on artificial intelligence in mathematics education published between 2020 and 2024. The second manuscript reports a quasi-experimental study evaluating Mathbot, a chatbot-based personalized learning platform, against business-as-usual classroom instruction. Repeated measures ANOVA was used to assess change in fraction comprehension and situational interest across time points. Results indicated modest improvements in fraction comprehension for students using Mathbot relative to traditional instruction, while changes in situational interest were not statistically significant. Findings suggest that automated personalization did not displace the instructional role of the teacher and that teacher decision-making remained central to student outcomes. The work contributes classroom-based evidence to ongoing discussion about the capabilities and limits of adaptive AI systems in elementary mathematics, and about accessibility and equity considerations when such systems are used with students with disabilities.
