Xiaohang: Research on the Construction and Application of Educational Intelligent Agents Based on Large Language Models

This study focuses on developing educational agents capable of understanding and adapting to learners' complex cognitive behaviors. We propose a 'Reverse Turing Test' (RTT) framework to evaluate AI's ability to perceive human cognition and construct an adaptive teaching agent, 'Xiaohang,' based on Multimodal Large Language Models (MLLMs). The research employs four key methods: RTT, multimodal data perception, adaptive teaching strategies, and memory-reflection mechanisms. RTT captures learners' cognitive states through interactive dialogues, multimodal perception collects multidimensional data (text, speech, images), adaptive strategies adjust teaching plans based on real-time feedback, and memory-reflection mechanisms optimize subsequent teaching outcomes. Experiments conducted in project-based learning (PBL) scenarios demonstrate that Xiaohang significantly enhances the quality of problem formulation, creativity in solution design, and task completion efficiency, validating its effectiveness in improving educational outcomes.