Adaptive Virtual Reality Museum: A Closed-Loop Framewor for Engagement-Aware Cultural Heritage
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917341189636096 |
|---|---|
| author | Damouni, Joseph Tanus, Wadia Unkelos-Shpigel, Naomi |
| author_facet | Damouni, Joseph Tanus, Wadia Unkelos-Shpigel, Naomi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_13639 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adaptive Virtual Reality Museum: A Closed-Loop Framewor for Engagement-Aware Cultural Heritage Damouni, Joseph Tanus, Wadia Unkelos-Shpigel, Naomi Human-Computer Interaction Multimedia Software Engineering 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. |
| title | Adaptive Virtual Reality Museum: A Closed-Loop Framewor for Engagement-Aware Cultural Heritage |
| topic | Human-Computer Interaction Multimedia Software Engineering |
| url | https://arxiv.org/abs/2603.13639 |