多智能体支持人机协同学习的认知加工机制探究OA

With the rapid development of generative artificial intelligence(GenAI),multi-agents that integrate large language models with virtual digital humans have gradually evolved into the important collaborative entities for human-machine collaborative learning.However,the intrinsic mechanisms for how learners and multi-agents carry out effective collaboration remains unclear.Accordingly,this paper conducts an experiment on multi-agents human-machine collaborative learning in the"German Speech"course.Thirty German language learners are recruited from Z University to complete two types of human-machine collaborative learning tasks involving declarative knowledge and procedural knowledge,and their electroencephalogram(EEG)signals and human-machine dialogue text data are collected simultaneously.Through EEG analysis and latent dirichlet allocation(LDA)topicanalysis,this paper finds that the main effect of brain regions is significant,and brain-wave power of each frequency band shows certain regional differences.The human-machine dialogue tests are stably clustered into two or three topics:declarative knowledge learning focuses on knowledge interpretation and value judgment,while procedural knowledge learning involves more comparative analysis,meaning construction,and integrated application.Based on this conclusion,this paper constructs an alignment framework for human-machine collaborative cognition and EEG supported by multi-agents,and reveals the cognitive processing mechanism of human-machine collaborative learning:multi-agents-dominated external perception at the information input phase,human-machine primary-auxiliary collaborative meaning construction at the content-comprehension phase,human-machine-jointly dominated information integration at the structural-organization phase,and learner-dominated metacognitive activation at the regulation-reflection phase.Integrating implicit EEG signals with explicit dialogue texts,this paper reveals the cognitive processing characteristics and human-machine role division of human-machine collaborative learning supported by multi-agents at different stages,and can provide references for the development of multi-agent system and the design of human-machine collaborative teaching.