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

要旨

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.

著者
Shi Liu
Karlsruhe Institute of Technology, Karlsruhe, Germany
Martin Feick
Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Linus Bierhoff
Karlsruhe Institute of Technology , Karlsruhe, Germany
Alexander Maedche
Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: AI, Motivation and Learning

P1 - Room 123
7 件の発表
2026-04-16 20:15:00
2026-04-16 21:45:00