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.

翻译：虚拟现实文化遗产中的静态信息呈现常导致认知过载或刺激不足。本文提出一种闭环自适应界面，通过隐式多模态感知技术，依据访客实时行为调整内容深度。该方法持续监测凝视驻留、头部运动学与位移数据，通过基于透明规则的分类器推断参与度，进而驱动大型语言模型动态调节解说复杂度，且不中断探索过程。我们在贝拉特民族志博物馆实现了概念验证，并开展初步评估（N=16）以对比自适应与静态内容。结果显示：自适应组参与者的阅读参与度与探索时长提升2-3倍，同时保持高可用性（系统可用性量表评分=84.3）。技术验证证实，在消费级VR硬件上可实现亚毫秒级参与度推断延迟。这些初步发现为更大规模研究提供了依据，并引发关于参与度验证、人工智能透明度及生成模型在遗产场景中应用的思考。本文呈现这项进行中的工作，旨在激发关于沉浸式文化体验中隐式人工智能驱动自适应机制的讨论。