GuideAI: A Real-time Personalized Learning Solution with Adaptive Interventions
Ananya Shukla, Chaitanya Modi, Satvik Bajpai, and Siddharth Siddharth

TL;DR
GuideAI is a multi-modal, real-time adaptive learning framework that personalizes educational content by integrating biosensory feedback to improve engagement, reduce cognitive load, and enhance knowledge retention.
Contribution
It introduces a novel multi-modal system that dynamically adapts learning based on real-time physiological and behavioral data, advancing personalized AI-driven education.
Findings
Significant improvements in problem-solving and recall assessments.
Reductions in mental demand, frustration, and effort levels.
Participants reported enhanced perceived performance.
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…
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Taxonomy
TopicsVisual and Cognitive Learning Processes · Gaze Tracking and Assistive Technology · Intelligent Tutoring Systems and Adaptive Learning
