We study experiences of students attending classes remotely from home using a social VR platform, considering both desktop-based and headset-based viewing of remote lectures. Ratings varied widely. Headset viewing produced higher presence overall. Strong negative correlations between headset simulator sickness symptoms and overall experience ratings, and some other ratings, suggest that the headset experience was much better for comfortable users than for others. Reduced sickness symptoms, and no similar correlations, were found for desktop viewing. Desktop viewing appears to be a good alternative for students not comfortable with headsets. Future VR systems are expected to provide more stable and comfortable visuals, providing benefits to more users.

We present an architecture to make a VR pedagogical agent responsive to shifts in user attention monitored by eye tracking. The behavior-based AI includes low-level sensor elements, sensor combiners that compute attention metrics for higher-level sensors called generalized hotspots, an annotation system for arranging scene elements and responses, and its response selection system. We show that the techniques can control the playback of teacher avatar clips that point out and explain objects in a VR oil rig for training.

In educational virtual reality, it is important to deal with problems of student inattention to presented content. We are developing attention-restoring visual cues for display when gaze tracking detects that student focus shifts away from critical objects. These cues include novel aspects and variations of standard cues that performed well in prior work on visual guidance. For the longer term, we propose experiments to compare various cues and their parameters to assess effectiveness and tradeoffs, and to assess the impact of eye tracking. Eye tracking is used to both detect inattention and to control the appearance and location of cues.