Generative AI is transforming education by enabling personalized, on-demand learning experiences. However, current AI systems lack awareness of the learner's cognitive state, limiting their adaptability. Meanwhile, electroencephalography (EEG)-based neuroadaptive systems have shown promise in enhancing engagement through real-time physiological feedback. This paper presents NeuroChat, a neuroadaptive AI tutor that integrates real-time EEG-based engagement tracking with generative AI to adapt its responses. NeuroChat continuously monitors a learner's cognitive engagement and dynamically adjusts content complexity, tone, and response style in a closed-loop interaction. In a within-subjects study (n=24), NeuroChat significantly increased both EEG-measured and self-reported engagement compared to a non-adaptive chatbot. However, no significant differences in short-term learning outcomes were observed. These findings demonstrate the feasibility of real-time cognitive feedback in LLMs, highlighting new directions for adaptive learning, AI tutoring, and deeper personalization in human-AI interaction.

翻译：生成式人工智能正在通过实现个性化、按需学习体验来变革教育领域。然而，当前的人工智能系统缺乏对学习者认知状态的感知，限制了其适应性。与此同时，基于脑电图（EEG）的神经自适应系统已显示出通过实时生理反馈提升参与度的潜力。本文提出了NeuroChat，一种集成了基于EEG的实时参与度追踪与生成式人工智能的神经自适应AI导师，能够据此调整其回应。NeuroChat持续监测学习者的认知参与度，并在闭环交互中动态调整内容复杂度、语气和回应风格。在一项被试内研究（n=24）中，与非自适应聊天机器人相比，NeuroChat显著提升了EEG测量和自我报告的参与度。然而，在短期学习成果方面未观察到显著差异。这些发现证明了在大型语言模型中实现实时认知反馈的可行性，为自适应学习、AI辅导以及人机交互中更深层次的个性化指明了新的方向。