Thesis Description & Objectives
Problem:
Traditional Virtual Tours often lack real-time responsiveness to user behavior. Virtual guides often fail to ensure group cohesion or tailor content dynamically, leading to disengagement and fragmented experiences.
Objective:
To develop AI-powered tools capable of tracking user behavior, maintaining group coherence, and adapting content delivery to individual and collective engagement patterns in digital museum and showroom tours.
Approach: The system will integrate real-time user tracking, behavioral modeling, either to adapt content presentation or to offer valuable hint and suggestion to the designated guide. Machine learning algorithms will infer user intent and attention, enabling the guide to adjust pacing, highlight relevant artifacts, and ensure no participant is left behind.
Expected Impact:
This research is expected to improve user engagement and retention in digital cultural experiences by enabling intelligent, behavior-aware guidance. It will support more inclusive and personalized tours, where virtual curators
adapt dynamically to group cohesion and individual interests. The outcomes may inform future design strategies for scalable, AI-enhanced museum and showroom applications.
