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

Yiyuan Wang, Andrew Johnston, Zoë Sadokierski, Rhiannon Stephens, Shane T. Ahyong

TL;DR

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, enabling fast, real-time interaction with extensive yet frequently updated datasets.

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.

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

TL;DR

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, enabling fast, real-time interaction with extensive yet frequently updated datasets.

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.
Paper Structure (30 sections, 10 figures, 3 tables)

This paper contains 30 sections, 10 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Chatbot-Assisted Human-Data Interaction
  4. Configuring LLMs for Grounded Data Retrieval
  5. Interaction Design for Museum Engagement
  6. Human-Centred Design Process
  7. Focus Groups at Australian Museum
  8. Translating Findings into Design Goals
  9. Design Iterations
  10. Design Iteration I: Initial Prototype and Expert Consultation
  11. Design Iteration II: Testing a Proof-of-Concept Prototype
  12. Implementing Australian Museum Collection Explorer
  13. System Architecture
  14. Data Source
  15. Conversational Agent Implementation
  16. ...and 15 more sections

Figures (10)

  • Figure 1: Development process of the Australian Museum Collection Explorer.
  • Figure 2: Photos taken at the workshops (top) and interview questions in focus-group activities (bottom).
  • Figure 3: The first interactive prototype we developed (left); ALA Lens (middle); Museum Animal Factsheets (right).
  • Figure 4: The second interactive prototype we developed: a bird collection explorer consisting of an initial version of the interactive map (left) and the conversational agent (right).
  • Figure 5: An overview of the system architecture for the Australian Museum Collection Explorer.
  • ...and 5 more figures