Generative AI is transforming education by enabling personalized, on-demand learning experiences. However, AI tutors lack the ability to assess a learner's cognitive state in real time, limiting their adaptability. Meanwhile, electroencephalography (EEG)-based neuroadaptive systems have successfully enhanced engagement by dynamically adjusting learning content. This paper presents NeuroChat, a proof-of-concept neuroadaptive AI tutor that integrates real-time EEG-based engagement tracking with generative AI. NeuroChat continuously monitors a learner's cognitive engagement and dynamically adjusts content complexity, response style, and pacing using a closed-loop system. We evaluate this approach in a pilot study (n=24), comparing NeuroChat to a standard LLM-based chatbot. Results indicate that NeuroChat enhances cognitive and subjective engagement but does not show an immediate effect on learning outcomes. These findings demonstrate the feasibility of real-time cognitive feedback in LLMs, highlighting new directions for adaptive learning, AI tutoring, and human-AI interaction.

本研究针对传统AI辅导缺乏实时评估学习者认知状态的问题，提出了一种新颖的神经自适应AI辅导机器人NeuroChat。该系统结合实时EEG监测与生成性AI，动态调整学习内容的复杂性和节奏，初步研究结果显示NeuroChat在提高认知和主观参与感方面有效，但对学习结果的直接影响尚不明显。这项研究展示了在大型语言模型中实现实时认知反馈的可行性，为适应性学习和人机交互开辟了新方向。

神经聊天：一种神经自适应AI聊天机器人用于定制学习体验