Adaptive Virtual Reality Museum: A Closed-Loop Framewor for Engagement-Aware Cultural Heritage
This addresses engagement issues for VR museum visitors, but it is incremental as it builds on existing adaptive and sensing methods.
The paper tackled the problem of cognitive overload or under-stimulation in VR cultural heritage by introducing a closed-loop adaptive interface that tailors content depth to real-time visitor behavior, resulting in adaptive participants showing 2-3x increases in reading engagement and exploration time while maintaining high usability (SUS = 84.3).
Static information presentation in VR cultural heritage often causes cognitive overload or under-stimulation. We introduce a closed-loop adaptive interface that tailors content depth to real-time visitor behavior through implicit multimodal sensing. Our approach continuously monitors gaze dwell, head kinematics, and locomotion to infer engagement via a transparent rule-based classifier, which drives a Large Language Model to dynamically modulate explanation complexity without interrupting exploration. We implemented a proof-of-concept in the Berat Ethnographic Museum and conducted a preliminary evaluation (N=16) comparing adaptive versus static content. Results indicate that adaptive participants demonstrated 2-3x increases in reading engagement and exploration time while maintaining high usability (SUS = 84.3). Technical validation confirmed sub-millisecond engagement inference latency on consumer VR hardware. These preliminary findings warrant larger-scale investigation and raise questions about engagement validation, AI transparency, and generative models in heritage contexts. We present this work-in-progress to spark discussion about implicit AI-driven adaptation in immersive cultural experiences.