# GuideAI: A Real-time Personalized Learning Solution with Adaptive Interventions

arXiv: https://arxiv.org/abs/2601.20402 · HTML: https://mlsanity.com/paper/2601.20402/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 66 references that are in the map.
- First posted on arXiv 28 Jan 2026.

## Builds on (most cited first, 12 of 12)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Large Language Models for Education: A Survey and Outlook](https://mlsanity.com/paper/2403.18105/) (2024)
- [Pupil: An Open Source Platform for Pervasive Eye Tracking and Mobile Gaze-based Interaction](https://mlsanity.com/paper/1405.0006/) (2014)
- [EduAgent: Generative Student Agents in Learning](https://mlsanity.com/paper/2404.07963/) (2024)
- [Beyond Final Answers: Evaluating Large Language Models for Math Tutoring](https://mlsanity.com/paper/2503.16460/) (2025)
- [Large Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A Survey of Early Trends, Datasets, and Challenges](https://mlsanity.com/paper/2407.07196/) (2024)
- [Empowering Private Tutoring by Chaining Large Language Models](https://mlsanity.com/paper/2309.08112/) (2023)
- [AI and personalized learning: bridging the gap with modern educational goals](https://mlsanity.com/paper/2404.02798/) (2024)
- [Leveraging LLM Tutoring Systems for Non-Native English Speakers in Introductory CS Courses](https://mlsanity.com/paper/2411.02725/) (2024)
- [LLMs in Education: Novel Perspectives, Challenges, and Opportunities](https://mlsanity.com/paper/2409.11917/) (2024)
- [Intelligent Interaction Strategies for Context-Aware Cognitive Augmentation](https://mlsanity.com/paper/2504.13684/) (2025)

## Built on by (most cited first, 1 of 1)

- [MAIC-UI: Making Interactive Courseware with Generative UI](https://mlsanity.com/paper/2604.25806/) (2026)

## Abstract (arXiv)

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.