AttentiveLearn: Personalized Post-Lecture Support for Gaze-Aware Immersive Learning

2026-03-05

AI Summary

This study addresses the lack of personalized post-class support in existing immersive learning environments, which hinders learners’ sustained motivation and academic outcomes. To bridge this gap, we propose an integrated ecosystem combining VR-based eye tracking with a mobile learning assistant that leverages in-class attention data from virtual classrooms to drive the generation of personalized quizzes and deliver immediate feedback, thereby closing the loop from attention modeling to post-lesson intervention. In a four-week field study involving 36 university students, the system significantly enhanced self-reported motivation and engagement, while also yielding observable positive shifts in attentional behaviors for a subset of participants. These findings contribute to filling a critical research void in personalized post-class interventions within immersive learning contexts.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Immersive learning environments such as virtual classrooms in Virtual Reality (VR) offer learners unique learning experiences, yet providing effective learner support remains a challenge. While prior HCI research has explored in-lecture support for immersive learning, little research has been conducted to provide post-lecture support, despite being critical for sustained motivation, engagement, and learning outcomes. To address this, we present AttentiveLearn, a learning ecosystem that generates personalized quizzes on a mobile learning assistant based on learners'attention distribution inferred using eye-tracking in VR lectures. We evaluated the system in a four-week field study with 36 university students attending lectures on Bayesian data analysis. AttentiveLearn improved learners'reported motivation and engagement, without conclusive evidence of learning gains. Meanwhile, anecdotal evidence suggested improvements in attention for certain participants over time. Based on our findings of the field study, we provide empirical insights and design implications for personalized post-lecture support for immersive learning systems.

Problem

• immersive learning
• post-lecture support
• personalized learning
• virtual reality
• learner engagement

Innovation

• gaze-aware learning
• personalized post-lecture support
• immersive learning
• eye-tracking in VR
• mobile learning assistant