AI-SUPPORTED TRANSFORMATIVE LESSON MODEL FOR DEVELOPING MATHEMATICAL MODELLING SKILLS IN SECONDARY STUDENTS

Abstract

Mathematical modelling is a cognitively demanding and educationally vital competency in secondary mathematics, requiring learners to translate complex real-world situations into structured mathematical forms and critically interpret their solutions. Conventional classrooms, however, rarely provide the reflective, disorienting, and meaning-making conditions that genuine modelling development requires. This paper introduces the AI-Supported Transformative Lesson Model (ATLM), a novel pedagogic framework that integrates Artificial Intelligence tools within teaching design, lesson planning, and feedback practices, grounded explicitly in Jack Mezirow's Transformative Learning Theory (TLT). The ATLM involves five pedagogic phases: Disorientation, Critical Reflection, Discourse, Reframing, and Integration, all supported through AI tools that scaffold, challenge, and respond to student thinking in real-time. AI-powered lesson planners design tasks to deliberately surface cognitive conflict, while intelligent scaffolding agents help students to critically review their assumptions. AI-driven formative feedback systems also provide tailored, process-level feedback to support reflective engagement with each stage of the mathematical modelling cycle (problem formulation, abstraction, representation, validation, and interpretation). The paper ends with the proposal of the AI-Supported Transformative Lesson Model, a conceptual framework that maps the intersection between AI-supported pedagogy and Transformative Learning Theory phases for building mathematical modelling skills. This model contributes to the broader conversation on transformative, subject-specific AI integration in secondary education, and offers a potential basis for future empirical validation and classroom application.