Large Language Models (LLMs) have emerged as powerful learning tools, but they lack awareness of learners' cognitive and physiological states, limiting their adaptability to the user's learning style. Contemporary learning techniques primarily focus on structured learning paths, knowledge tracing, and generic adaptive testing but fail to address real-time learning challenges driven by cognitive load, attention fluctuations, and engagement levels. Building on findings from a formative user study (N=66), we introduce GuideAI, a multi-modal framework that enhances LLM-driven learning by integrating real-time biosensory feedback including eye gaze tracking, heart rate variability, posture detection, and digital note-taking behavior. GuideAI dynamically adapts learning content and pacing through cognitive optimizations (adjusting complexity based on learning progress markers), physiological interventions (breathing guidance and posture correction), and attention-aware strategies (redirecting focus using gaze analysis). Additionally, GuideAI supports diverse learning modalities, including text-based, image-based, audio-based, and video-based instruction, across varied knowledge domains. A preliminary study (N = 25) assessed GuideAI's impact on knowledge retention and cognitive load through standardized assessments. The results show statistically significant improvements in both problem-solving capability and recall-based knowledge assessments. Participants also experienced notable reductions in key NASA-TLX measures including mental demand, frustration levels, and effort, while simultaneously reporting enhanced perceived performance. These findings demonstrate GuideAI's potential to bridge the gap between current LLM-based learning systems and individualized learner needs, paving the way for adaptive, cognition-aware education at scale.

翻译：大型语言模型（LLM）已成为强大的学习工具，但其缺乏对学习者认知与生理状态的感知能力，限制了其对用户学习风格的适应性。当前的学习技术主要关注结构化学习路径、知识追踪和通用自适应测试，但未能解决由认知负荷、注意力波动和参与度驱动的实时学习挑战。基于一项形成性用户研究（N=66）的发现，我们提出了GuideAI——一个多模态框架，通过整合包括眼动追踪、心率变异性、姿势检测和数字笔记行为在内的实时生物传感反馈，来增强LLM驱动的学习。GuideAI通过认知优化（基于学习进度标记调整复杂度）、生理干预（呼吸指导和姿势矫正）和注意力感知策略（利用视线分析重定向焦点）动态调整学习内容与节奏。此外，GuideAI支持跨不同知识领域的多样化学习模态，包括基于文本、图像、音频和视频的教学。一项初步研究（N=25）通过标准化评估考察了GuideAI对知识保持和认知负荷的影响。结果显示，在问题解决能力和基于回忆的知识评估方面均取得了统计学上的显著提升。参与者在关键NASA-TLX指标（包括心理需求、挫败感和努力程度）上亦体验到显著降低，同时报告了感知绩效的增强。这些发现证明了GuideAI在弥合当前基于LLM的学习系统与个体化学习者需求之间差距的潜力，为大规模、自适应且具备认知意识的教育铺平了道路。