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

arXiv: https://arxiv.org/abs/2603.10285 · HTML: https://mlsanity.com/paper/2603.10285/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 41 references that are in the map.
- First posted on arXiv 11 Mar 2026.

## Builds on (most cited first, 3 of 3)

- [NarraGuide: an LLM-based Narrative Mobile Robot for Remote Place Exploration](https://mlsanity.com/paper/2508.01235/) (2025)
- [Using a Large Language Model as Design Material for an Interactive Museum Installation](https://mlsanity.com/paper/2503.22345/) (2025)
- [Visual-Conversational Interface for Evidence-Based Explanation of Diabetes Risk Prediction](https://mlsanity.com/paper/2507.02920/) (2025)

## Abstract (arXiv)

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.