Transforming museum exhibits using interactive mixed-reality

Ren, Minghao (2026) Transforming museum exhibits using interactive mixed-reality. MRes thesis, University of Glasgow.

Abstract

Museums play a crucial role in preserving and communicating cultural, historical, and scientific knowledge. However, traditional exhibition methods often limit visitor engagement and comprehension. This research investigates how mixed-reality (XR) technologies (integrated with Artificial Intelligence (AI) and Computer Graphics (CG)) can transform visitor experiences within museum environments, with a focus on the Hunterian Museum’s Science Collection.

Building on these insights, a prototype mobile and mixed-reality application was designed and developed to enable immersive exploration of artefacts in 3D, interactive storytelling, and adaptive learning experiences. The system architecture integrated wearable interfaces, edge computing, cloud services, and AI-driven modules for speech recognition, natural language processing, and personalized recommendations.

After finishing the implementation of my App, functional and performance evaluations are operated. It identified optimal solutions for enabling natural user interaction, while user studies demonstrated significant improvements in engagement, comprehension, and satisfaction compared to traditional exhibition methods. The findings indicate that AI-enhanced XR applications can provide inclusive, dynamic, and educational museum experiences, bridging the gap between physical artefacts and visitor understanding. This research contributes a practical framework for integrating immersive technologies into museum practice and offers insights for future developments in adaptive, user-centered digital heritage experiences.

• Item Type: Thesis (MRes)
• Qualification Level: Masters
• Subjects: A General Works > AM Museums (General). Collectors and collecting (General); T Technology > T Technology (General)
• Colleges/Schools: College of Science and Engineering > School of Engineering
• Supervisor's Name: Ghannam, Professor Rami
• Date of Award: 2026
• Depositing User: Theses Team
• Unique ID: glathesis:2026-85719
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 28 Jan 2026 10:05
• Last Modified: 28 Jan 2026 10:05
• Thesis DOI: 10.5525/gla.thesis.85719
• URI: https://theses.gla.ac.uk/id/eprint/85719