FROM PASSIVE TO PERSONALIZED: AN AI-DRIVEN FRAMEWORK FOR ENHANCING SCIENCE AND MATHEMATICS TEACHER PROFESSIONAL DEVELOPMENT

Abstract

This paper presents an innovative AI-driven framework designed to transform Mathematics Teacher Professional Development (PD) by addressing the shortcomings of traditional models. Acknowledging the persistent weak impact of conventional PD on teacher efficacy, recent studies highlight a critical need for reform. This research proposes a structured, four-phase framework to enhance teachers' Pedagogical Content Knowledge (PCK) and teaching efficacy through, interactive, and sustained learning experiences. Phase One uses AI to conduct diagnostic assessments that identify individual knowledge gaps and generate personalised PD pathways. Phase Two introduces interactive, adaptive learning modules that engage teachers in scenario-based simulations, aligning with constructivist principles. Phase Three provides AI-simulated mastery experiences, allowing teachers to practise new strategies in a risk-free environment to foster self-efficacy. Finally, Phase Four ensures sustained support via AI coaching and facilitated Professional Learning Communities (PLCs), while also considering challenges such as infrastructure, digital literacy, and the ethical use of AI in education. By cultivating a skilled and innovative teaching workforce, this framework aims to bridge the knowledge-belief gap and shift the perception of AI from a mere tool to a transformative agent in mathematics education.