本研究探討教育神經科學在數學教育中的應用潛力與挑戰，並嘗試透過魔術方塊的操作過程，闡述空間推理及問題解構能力等認知歷程與其對應的神經基礎。傳統數學教育研究侷限於外顯行為觀察與結果分析，難以掌握學生即時且內隱的認知歷程；而腦科學技術（如fMRI、EEG、fNIRS）則能深入探索數學學習時的神經機制，彌補傳統方法不足。此外，國際間已有專刊及書籍積極討論神經理解、神經預測及神經介入的跨領域研究架構，反映數學教育與神經科學整合的趨勢。本文進一步提出被動式腦機介面（passive BCI）技術，結合AI分析腦波訊號，可即時診斷學生在數學解題中的多元認知策略與視覺化歷程，提升教師對學生解題歷程的掌握。此文章期望推動台灣在數學教育神經科學的創新發展，提供更精準的個別化教學模式與教學現場實用之重要參考。

This study reports the potential and challenges of educational neuroscience applications in mathematics education. By analyzing cognitive processes such as spatial reasoning and problem solving through the analogy of solving a Rubik's Cube, we elucidate their underlying neural mechanisms. Traditional mathematics education research relies primarily on observable behaviors and outcome analyses, limiting insights into students' real-time and implicit cognitive processes. In contrast, neuroscience techniques (e.g., fMRI, EEG, fNIRS) offer deeper exploration into the neural foundations of mathematical learning, overcoming traditional methodological limitations. Internationally, special journal issues and book chapters actively discuss interdisciplinary frameworks including neuro-understanding, neuro-prediction, and neuro-intervention, reflecting growing trends toward integrating neuroscience into mathematics education. This article further proposes the application of passive Brain-Computer Interface (passive BCI) technology combined with artificial intelligence for real-time analysis of neural signals. This approach aims to diagnose diverse cognitive strategies and visualization processes employed by students during mathematical problem-solving, thereby enhancing teachers' understanding of students' cognitive processes. Ultimately, this paper seeks to foster innovative developments in Taiwanese educational neuroscience research and provide practical insights for individualized mathematics instruction in classroom contexts.

數學教育; 神經革命; 腦科學; 被動式腦機介面

Mathematics Education; Educational Neuroscience; Passive BCI