Simulation of Teaching behaviours in Intelligent Tutoring Systems: A Review Using Large Language Models

Intelligent Tutoring Systems (ITS) and allied digital platforms now constitute core infrastructure in many classrooms, where they automate formative assessment, personalise pacing and supply fine-grained analytics that would otherwise exceed human capacity. Against this backdrop, Large Language Models (LLMs) have emerged as a disruptive layer of functionality, expanding educational AI from rulebased tutoring to open-ended dialogue, generative content and realtime adaptation. Early classroom prototypes already leverage multiagent LLM frameworks to orchestrate teacher-student and peer interactions, demonstrating richer discourse patterns and enhanced engagement when benchmarked with established observation rubrics. Most consequential, however, is the accelerating shift towards full simulation of teacher work. Emerging evidence suggests that prompting an LLM to rehearse lessons, generate reflective commentary, and iteratively revise materials can raise the quality of teaching plans to a level comparable to those crafted by expert educators. While the narrative highlights practical applications and pedagogical implications, this review is grounded in a systematic methodology combined with narrative analysis, ensuring analytical depth and thematic cohesion.