Museum audio guides generation using visitor categories and large language models

Museums widely use audio guides, yet these are delivered identically to all visitors regardless of their profile. This study addresses that gap by combining GPT-4 with Falk and Dierking’s visitor categorization framework to generate personalized audio guides and examine whether LLM-generated content can effectively meet the distinct needs of five visitor types: Explorers, Experience Seekers, Professionals/Hobbyists, Facilitators, and Rechargers. Personalized and general audio guides were generated for three artworks using a prompt-chain approach encoding visitor-specific needs. A user study with 56 self-identified participants evaluated the content, complemented by expert validation assessing factual accuracy and domain-specific quality. Personalized guides outperformed general ones across most categories and needs, with notable gains in engagement for Experience Seekers (+8.9%), curiosity stimulation for Explorers (+8.8%), and work/hobby relevance for Professionals/Hobbyists (+10.1%). Preference, however, was moderated by artwork characteristics. Expert review confirmed factual soundness while identifying gaps in domain-specific terminology and tonal alignment. These findings directly motivated our Curator-AI collaborative framework, which organizes content production into four phases, with the curator as the governing intelligence. This work demonstrates the potential of LLMs to support personalization in cultural heritage contexts through established visitor categorization frameworks. It also highlights the importance of a human-in-the-loop approach, in which curators remain actively involved in supervising, validating, and refining AI-generated content. Overall, the proposed framework suggests a viable path toward scalable and personalized museum experiences that balance automation with curatorial oversight.