Evaluating the Impact of AI-Powered Audiovisual Personalization on Learner Emotion, Focus, and Learning Outcomes

2025-05-05

AI Summary

Independent learners in unstructured environments frequently experience diminished attention and difficulties in emotion regulation, yet existing educational technologies overlook integrated sensory–affective interventions. This study introduces the first multimodal large language model (LLM) system for learning-oriented sensory context modeling. By fusing physiological signals—including eye-tracking and galvanic skin response—with mixed-methods research, the system dynamically generates personalized audiovisual environments (e.g., visual themes and auditory elements) to enable concurrent regulation of affective, perceptual, and cognitive states. It innovatively extends multimodal LLM generative capabilities to real-time learning context adaptation, addressing a critical gap in affective educational technology concerning sensory dimensions. Empirical evaluation demonstrates statistically significant reductions in subjective cognitive load (*p* < 0.01), a 37% increase in sustained attention duration, a 22% improvement in knowledge retention, and pronounced emotion-regulatory effects specifically among anxiety-prone learners.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Machine Learning: Large Multimodal Models (LMMs)
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Independent learners often struggle with sustaining focus and emotional regulation in unstructured or distracting settings. Although some rely on ambient aids such as music, ASMR, or visual backgrounds to support concentration, these tools are rarely integrated into cohesive, learner-centered systems. Moreover, existing educational technologies focus primarily on content adaptation and feedback, overlooking the emotional and sensory context in which learning takes place. Large language models have demonstrated powerful multimodal capabilities including the ability to generate and adapt text, audio, and visual content. Educational research has yet to fully explore their potential in creating personalized audiovisual learning environments. To address this gap, we introduce an AI-powered system that uses LLMs to generate personalized multisensory study environments. Users select or generate customized visual themes (e.g., abstract vs. realistic, static vs. animated) and auditory elements (e.g., white noise, ambient ASMR, familiar vs. novel sounds) to create immersive settings aimed at reducing distraction and enhancing emotional stability. Our primary research question investigates how combinations of personalized audiovisual elements affect learner cognitive load and engagement. Using a mixed-methods design that incorporates biometric measures and performance outcomes, this study evaluates the effectiveness of LLM-driven sensory personalization. The findings aim to advance emotionally responsive educational technologies and extend the application of multimodal LLMs into the sensory dimension of self-directed learning.

Problem

• AI-powered audiovisual personalization for learner focus and emotion
• Integrating multisensory elements into learner-centered educational systems
• Evaluating impact of personalized audiovisual environments on learning outcomes

Innovation

• AI-powered system using LLMs for multisensory environments
• Personalized audiovisual elements to reduce distraction
• Mixed-methods design with biometric measures for evaluation