Conversational AI-Enhanced Exploration System to Query Large-Scale Digitised Collections of Natural History Museums

2026-03-11

AI Summary

This work proposes a novel system that integrates an interactive map with a natural language conversational agent to facilitate public exploration of large-scale, domain-specific natural history museum collections. Addressing the limitations of traditional keyword- or structured-query approaches, the system leverages a large language model (LLM) with function-calling capabilities to dynamically invoke structured data APIs, enabling semantic, real-time interaction with nearly 1.7 million life science specimen records from the Australian Museum. By translating natural language queries into precise API calls and visualizing results geospatially, the approach significantly lowers the barrier for non-expert users to access and engage with scientific collection data. This study establishes a new paradigm for deploying scientific AI agents in cultural heritage contexts, demonstrating how advanced language models can bridge the gap between complex institutional datasets and public inquiry.

Technology Category

• Natural Language Processing: Conversational AI/Dialog Systems
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Humans and AI: AI for Accessibility

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Recent digitisation efforts in natural history museums have produced large volumes of collection data, yet their scale and scientific complexity often hinder public access and understanding. Conventional data management tools, such as databases, restrict exploration through keyword-based search or require specialised schema knowledge. This paper presents a system design that uses conversational AI to query nearly 1.7 million digitised specimen records from the life-science collections of the Australian Museum. Designed and developed through a human-centred design process, the system contains an interactive map for visual-spatial exploration and a natural-language conversational agent that retrieves detailed specimen data and answers collection-specific questions. The system leverages function-calling capabilities of contemporary large language models to dynamically retrieve structured data from external APIs, enabling fast, real-time interaction with extensive yet frequently updated datasets. Our work provides a new approach of connecting large museum collections with natural language-based queries and informs future designs of scientific AI agents for natural history museums.

Problem

• digitised collections
• public access
• scientific complexity
• data exploration
• natural history museums

Innovation

• Conversational AI
• Function Calling
• Natural Language Interface
• Museum Collections
• Real-time Data Retrieval