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.
@article{arxiv.2603.05324,
title = {AttentiveLearn: Personalized Post-Lecture Support for Gaze-Aware Immersive Learning},
author = {Shi Liu and Martin Feick and Linus Bierhoff and Alexander Maedche},
journal= {arXiv preprint arXiv:2603.05324},
year = {2026}
}
Comments
Accepted to appear in the Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (CHI 2026)