Abstract as per original application
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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.
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