[论文解读] GuideAI: A Real-time Personalized Learning Solution with Adaptive Interventions

Ananya Shukla, Chaitanya Modi+2 more|arXiv (Cornell University)|Jan 28, 2026

Visual and Cognitive Learning Processes被引用 0

一句话总结

GuideAI 提供一个生物传感器增强的实时自适应学习系统，使用多模态数据推断认知-情感状态并在文本、图像、音频和视频模态中提供个性化干预。初步研究显示基于大型语言模型的辅导能提高记忆保持并降低认知负荷。

ABSTRACT

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.

研究动机与目标

• 识别当前基于大语言模型的辅导在实时学习者状态感知与多模态适应方面的局限性。
• 提出一个生物传感器增强的闭环学习框架，从多模态信号推断认知-情感状态。
• 开发一个支持文本、图像、音频和视频模态的多模态 GuideAI 系统。
• 通过形成性研究和初步用户研究评估 GuideAI 的学习成效和认知负荷。
• 开源 GuideAI 代码库，促进可重复性与进一步研究。

提出的方法

• 以 N=66 进行形成性研究，识别学习者需求与设计含义。
• Three-module GuideAI 架构：传感模块（生物特征与行为数据）、处理模块（信号处理）、推理模块（状态估计与干预）。
• 生物传感数据流包括眼动追踪、HRV、姿态与记笔习惯；信号通过 Lab Streaming Layer (LSL) 实时对齐。
• 状态推断从归一化、基线调整后的特征中计算六个认知维度（认知负荷、注意力、投入、理解、压力、疲劳）。
• 干预通过 LLM/VLM/音频 LLM 进行，具备语调调整与模态特异策略。
• 干预阈值为与基线相关的 Z 分数，阈值 |z|≥1.0 表示中等偏离，|z|≥1.5 表示明显偏离，且在 10 秒持续窗口内强制执行。

Figure 1 . GuideAI’s Comprehensive Adaptive Learning Interface. The image illustrates the multi-modal, biosensor-driven approach of the personalized system across different learning modes—text-based, image-based, audio-based and video-based. Each panel demonstrates real-time adaptation to the learne

实验结果

研究问题

• RQ1 生物传感器增强的 LLM 系统能否在多模态下实时推断出认知-情感学习者状态？
• RQ2 基于推断状态的实时自适应干预是否比非个性化基线提高学习成效并降低认知负荷？
• RQ3 如何在文本、图像、音频和视频模态上有效定制干预以维持学习 FLOW？
• RQ4 跨设备、实时教育平台聚合凝视、HRV、姿态与笔记数据的可行性如何？
• RQ5 GuideAI 方法是否可扩展且能在不同知识领域和学习者群体中泛化？

主要发现

• 初步研究（N=25）显示在解题与基于记忆的知识评估方面，与非个性化基线相比有统计显著的改进。
• 参与者报告 NASA-TLX 指标（如心理需求、挫败感和努力）下降，同时感知表现提升。
• 生物特征与行为线索使得实时自适应有效，如内容节奏、难度调节与生理干预（如箱式呼吸、姿态提示）。
• GuideAI 支持四种学习模态（文本、图像、音频、视频），并具备针对认知负荷与投入水平的模态特异干预。
• 形成性研究为多模态支持、实时适应、生理感知与个性化学习路径设计提供了启示。

Figure 2 . Participant openness to working with a personalized AI learning assistant on a scale from 1-10. The distribution shows strong receptivity, with 50 out of 66 participants (75.7%) rating their openness at 7 or higher, and a median rating of 8.