Abstract

This thesis presents the design and evaluation of an AI-powered multimodal tour guide that uses image recognition and personalised storytelling to enhance cultural heritage experiences. Traditional approaches to learning about monuments rely on static plaques, generic tour content, or manual web searches, which limit personalisation, interactivity, and accessibility. To address these limitations, the study implemented a “Multimodal Monument Explorer” that allows users to upload a photo of a landmark or describe it in natural language and then receive rich, context-aware explanations in both text and audio form. The system integrates a persistent vector database of monument images, OpenCLIP-based visual embeddings, and a large multimodal language model to identify visually similar landmarks and generate tailored narratives. Multiple guide personas (e.g., historian, epic storyteller, comedian) adapt the narrative style to user preferences while preserving factual accuracy, and a Streamlit interface orchestrates retrieval, multimodal reasoning, and real-time text-to-speech synthesis. The research followed a design science and mixed- methods evaluation strategy. A quantitative study on 100 image–query pairs showed high retrieval performance (P@1 = 0.87, P@3=0.94, P@5=0.98, MRR=0.91) with end-to-end response times under eight seconds for both text and image interactions. A user study with 20 participants yielded an average System Usability Scale score of 88.5 (“Excellent”) and very positive ratings for narrative quality, engagement, and persona enhancement. The findings demonstrate the technical feasibility and user value of multimodal, persona-driven AI tour guides and offer practical design insights for future AI-driven cultural heritage applications at the intersection of data analytics, human–AI interaction, and digital tourism.

Library of Congress Subject Headings

Tour guides (Manuals)--Interactive multimedia--Design; Tour guides (Manuals)--Automation; Multimodal user interfaces (Computer systems)--Design

Publication Date

12-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Advisor

Sanjay Modak

Advisor/Committee Member

Khalid Ezzeldeen

Campus

RIT Dubai

Plan Codes

PROFST-MS

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