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Adaptive Virtual Reality Museum: A Closed-Loop Framewor for Engagement-Aware Cultural Heritage

Joseph Damouni, Wadia Tanus, Naomi Unkelos-Shpigel

Abstract

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.

Adaptive Virtual Reality Museum: A Closed-Loop Framewor for Engagement-Aware Cultural Heritage

Abstract

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.
Paper Structure (29 sections, 1 equation, 3 figures, 4 tables)

This paper contains 29 sections, 1 equation, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Adaptive Interfaces in VR
  4. Implicit Behavioral Signals
  5. Generative Content Adaptation
  6. Method
  7. Engagement Modeling
  8. Signal Pre-processing
  9. Sensor Fusion and Weighting
  10. Safety Gates
  11. Design Goals
  12. Participants
  13. System Implementation
  14. Sensing and Inference
  15. Asynchronous Content Pipeline
  16. ...and 14 more sections

Figures (3)

  • Figure 1: Signal Pre-processing and Sensor Fusion. Normalized behavioral signals are aggregated into a composite engagement score $E_{raw}$.
  • Figure 2: Temporal Smoothing and Safety Gates. The smoothed score passes through velocity-based safety gates before mapping to discrete engagement states.
  • Figure 3: System implementation overview: (a) participant wearing the Meta Quest 3 headset, (b) the virtual museum environment, (c) the achievements and gamification interface, and (d) an example object information panel showing adaptive content.