A CONCEPTUAL FRAMEWORK FOR MATHEMATICS LEARNING DESIGN IN THE AGE OF ARTIFICIAL INTELLIGENCE
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Abstract
This academic article aims to develop a conceptual framework for mathematics learning design in the age of artificial intelligence by synthesizing Constructivism, Cognitive Load Theory, Self-Regulated Learning, Metacognition, TPACK, and SAMR. The article responds to a conceptual gap in current discussions of AI in education, where AI is often addressed in broad terms or through isolated perspectives on technology integration, without sufficiently linking learning theory, cognitive support, learner regulation, and pedagogically meaningful use in mathematics education. Using a conceptual synthesis of scholarly literature on learning theories, cognitive processes, self-regulation, technology integration, and AI in mathematics education, the article analyzes the distinctive contributions of the six frameworks and their interrelationships in informing mathematics learning design. The synthesis organizes these frameworks into four integrative dimensions: meaningful knowledge construction, appropriate cognitive support, learner regulation and reflection, and pedagogically aligned AI integration. These dimensions are further synthesized into five design principles: purposeful meaning construction, appropriate cognitive support, learner regulation and reflection, pedagogically aligned AI integration, and transformative use of AI to enhance the quality of learning activities. The framework provides a conceptual foundation for teachers, curriculum developers, and researchers seeking to design AI-supported mathematics learning in ways that are reflective, critical, and consistent with the epistemic nature of mathematical thinking.
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