Improving instructional design proficiency of master's students in mathematics education through intelligent educational technologies

Research on smart education has predominantly focused on macro-level concepts and frameworks, such as core principles and system architectures, while offering limited guidance for discipline-specific integration and practical implementation. This study addresses two critical gaps: (1) how an M.Ed. course can be enhanced through intelligent educational technologies, and (2) whether a smart classroom teaching mode can effectively improve M.Ed. students’ instructional design competence, particularly in terms of precision and professionalism. Adopting a pedagogically oriented approach rather than an algorithmic one, this study proposes a three-dimensional framework encompassing learning effectiveness, information and communication technology (ICT), and classroom organization. Based on this framework, a set of smart classroom characteristics aligned with M.Ed. program objectives is developed. Using a mathematics M.Ed. course as a case study, the study integrates intelligent educational technologies—such as automated scoring, personalized recommendations, and multi-AI feedback—across the pre-class, in-class, and post-class stages to construct a technology-enhanced teaching loop. Results from a quasi-experimental design and quantitative analysis indicate that this approach significantly improves students' ability to formulate precise and professionally grounded instructional objectives. Building on these findings, the study proposes the “D–T–E Model” (Disciplinary Demand–Technological Empowerment–Evaluation Loop) as a transferable pedagogical framework for professional master's education. The findings provide practical implications for the design and implementation of AI-supported smart classrooms in teacher education contexts.