Abstract:

The education sector has quickly adopted Artificial Intelligence (AI) and Internet of Things (IoT) technologies which focus on creating learning experiences that adapt to each individual student. However, limited research addresses real-time optimization of teaching performance through computational modeling. This paper proposes a Digital Twin–driven AIoT framework designed to dynamically model and optimize instructional delivery in smart classrooms. The framework integrates multimodal IoT sensing, edge-based analytics, and an AI-powered optimization engine to construct a real-time virtual replica of the teaching process. Teaching effectiveness is formulated as a multi-objective optimization problem balancing engagement maximization, cognitive load reduction, interaction density, and environmental comfort. Unlike existing AIoT educational systems that focus on student analytics, the proposed approach introduces a teacher-centric dynamic modeling paradigm. The paper provides system architecture, mathematical formulation, optimization strategy, and implementation scenarios. The framework establishes a foundation for next-generation intelligent instructional ecosystems.

Keywords:

Digital Twin, Smart Classroom, AIoT, Teaching Optimization, Edge Computing, Educational Analytics.