Title: NeuroChat: A Neuroadaptive AI Chatbot for Customizing Learning Experiences Show Chinese title

Title: NeuroChat：一种用于定制学习体验的神经自适应AI聊天机器人

Abstract: 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. Show Chinese abstract

Abstract: 生成式人工智能通过提供个性化和按需学习体验正在改变教育。 然而，人工智能导师缺乏实时评估学习者认知状态的能力，限制了它们的适应性。 同时，基于脑电图（EEG）的神经适应系统已成功通过动态调整学习内容来提高参与度。 本文介绍了NeuroChat，这是一种将实时基于EEG的参与度跟踪与生成式人工智能相结合的神经适应性AI导师的概念验证。 NeuroChat持续监测学习者的认知参与度，并通过闭环系统动态调整内容复杂度、回应风格和节奏。 我们在一项试点研究（n=24）中评估了这一方法，将NeuroChat与标准的基于大型语言模型（LLM）的聊天机器人进行比较。 结果表明，NeuroChat提高了认知和主观参与度，但并未立即影响学习成果。 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，突出了自适应学习、人工智能辅导和人机交互的新方向。