Abstract as per original application
(English/Chinese): |
As artificial intelligence (AI) becomes increasingly embedded in society, cultivating AI literacy at an early age, particularly in K–12 education, has become an urgent priority. Current research efforts often emphasize technical fluency with AI tools and investigate teachers’ perceptions and students’ usage of them, lacking focus on sustainable curricular transformation. AI literacy encompasses not only technical skills but also conceptual knowledge and critical values and attitudes, including ethical considerations. These dimensions remain underexplored, particularly in subject-specific contexts like mathematics education. In mathematics classrooms, AI integration should move beyond merely tool usage, toward supporting authentic connections between AI literacy and mathematics learning outcomes.
In response, this design-based study envisions a mathematics curriculum centered on data handling and probability (DHP), embedding real-world AI contexts into conventional mathematical topics. Rather than focusing on introducing new and rapidly evolving technologies, this study adapts existing curricular materials to reflect students’ everyday experiences with AI. Topics such as statistical charts, central tendency, data dispersion, and probability are reframed using familiar AI-related scenarios. For instance, probability problems are redesigned around systems like YouTube’s recommendation algorithms, making AI processes more transparent and relatable. This approach allows students to implicitly engage with AI concepts, processes, and ethics within regular mathematics lessons.
The study will iteratively co-design and refine curricular materials in collaboration with 8 mathematics teachers, implementing them with grade 7–10 students (n=200) across 4 secondary schools in Hong Kong. Videotaped classroom enactments will be analyzed to examine how students and teachers interact with the designed materials. Particular attention will be paid to how students articulate AI concepts, identify biases through mathematical reasoning, and how teachers facilitate classroom discourse that bridges mathematics and AI literacy. The pedagogical approach draws on the framework of Realistic Mathematics Education to ensure contextual relevance and accessibility.
Theoretically, the study contributes to understanding AI’s interdisciplinary nature through the lens of boundary crossing, emphasizing the foundational yet often implicit connections between AI and mathematics. Practically, it offers an immediate solution and model for integrating AI into mathematics education without requiring extensive retraining or new technologies. By leveraging existing curricular structures and aligning them with contemporary AI applications, this study supports the broader STEM education goal of preparing students to critically engage with AI in society. Ultimately, it will enhance the relevance of mathematics education and promotes technological competence through meaningful, context-rich mathematics learning experiences.
隨著人工智能（AI）日益融入社會，在基礎教育階段儘早培養學生的AI素養成為一項緊迫任務。現有研究往往關注AI工具的熟練運用，並考察教師對這些工具的看法以及學生的使用情況，卻較少探討可持續的課程轉型。AI素養不僅涉及技術技能，還涵蓋概念性知識以及批判性的價值觀與態度，其中包括倫理考量。然而，這些維度仍未得到充分探討，在數學教育等具體學科情境中尤其如此。在數學課堂中，AI的融入不應僅停留在工具使用層面，而應促進AI素養與數學學習成效之間形成實質性聯繫。對此，本研究採用設計研究方法，提出一種以數據處理與概率（DHP）為核心的數學課程構想，旨在將現實世界中的AI情境融入常規數學內容。其重點並非引入快速演進的新興技術，而是改編既有課程材料，使其貼近學生日常生活中與AI接觸的經驗。統計圖表、集中趨勢、數據離散程度和概率等主題，均藉助學生熟悉的AI相關場景重新組織。例如，概率問題圍繞YouTube等平台的推薦算法進行重新設計，使AI的運作過程更加透明，也更貼近學生的日常經驗。這種方法將AI的概念、運作過程和倫理問題隱性地融入常規數學課堂，使學生能夠在數學學習過程中自然地接觸並思考這些內容。本研究將與8名數學教師合作，通過多輪迭代共同設計並完善課程材料。這些材料隨後將在香港4所中學的課堂中實施，參與者為200名中一至中四學生。研究將分析課堂教學錄像，考察師生如何使用這些課程材料，以及如何圍繞材料展開互動。其中重點關注學生如何表達對AI概念的理解、如何運用數學推理識別偏差，以及教師如何組織和引導課堂話語，進而在數學學習與AI素養之間建立聯繫。該教學路徑借鑒現實數學教育（RME）的理論框架，以確保教學情境既貼近學生的生活經驗，又便於學生理解和參與。在理論層面，本研究有助於從邊界跨越的視角深化對AI跨學科屬性的理解，並凸顯AI與數學之間的基礎性關聯，而這種關聯往往未被明確指出。在實踐層面，本研究旨在提出一種可直接實施的AI融入數學教育模式，且無須開展大規模的教師再培訓或引入新技術。本研究立足現有課程結構，將其與當前的AI應用相結合，從而有助於實現更廣泛的STEM教育目標——培養學生在社會生活中批判性地理解和應對AI的能力。最終，這一課程設計將增強數學教育與現實生活之間的聯繫，並通過有意義、情境豐富的數學學習體驗提升學生的技術能力。
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