Cognitive empathy, the ability to understand others‘ perspectives, is essential for effective communication, reducing biases, and constructive negotiation. However, this skill is declining in a performance-driven society, which prioritizes efficiency over perspective-taking. Here, the training of cognitive empathy is challenging because it is a subtle, hard-to-perceive soft skill. To address this, we developed CoEmpaTeam, a VR-based system that enables users to train their cognitive empathy by using LLM-driven avatars with different personalities. Through dynamic role play, users actively engage in perspective-taking, experiencing situations through another person’s eyes. CoEmpaTeam deploys three avatars who significantly differ in their personality, validated by a technical evaluation and an online experiment (n=90). Next, we evaluated the system through a lab experiment with 32 participants who performed three sessions across two weeks, followed by a one-week diary study. Our results showed a significant increase in cognitive empathy, which, according to participants, transferred into their real lives.

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.

Mixed Reality (MR) has demonstrated its potential in various learning contexts. MR-based learning environments empower users to actively explore learning content visualized in multiple formats, such as 3D models, videos, and images. Nonetheless, the sophisticated visualizations in MR learning environments may result in potential visual overload, posing a challenge for users in efficiently allocating their attention. In this paper, we present AF-Mix, a learning support system that leverages eye tracking sensors in Microsoft HoloLens 2 to offer attention feedback for learners. Aiming to design AF-Mix, we conducted a participatory design study and integrated the attention feedback into our system, following users’ needs and suggestions. Furthermore, we evaluated AF-Mix in an evaluation study (n = 22) following a quantitative analysis of users’ visual behavior, as well as a qualitative analysis of interview transcripts. Our findings show that providing feedback to support the learning process can be achieved effectively with eye tracking. In specific, attention feedback assists learners in retrieving previously missed information and encourages learners to reallocate their attention in the review process. Moreover, providing personalized feedback based on previous attention allocation is more effective in supporting users than a self-review approach without gaze-aware assistance in MR. Such feedback facilitates users in managing their limited attentional resources better and supports the reflection of their learning journey more effectively. • Human-centered design of attention feedback in Mixed Reality (MR) learning systems. • Evaluation of the system using qualitative and quantitative methods. • Attention feedback in MR supports users in attention management and self-reflection. • Design recommendations for supporting self-reflection in MR learning systems.