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The Rise of the AI Scientist: Unleashing the Potential of Chat-GPT Powered Avatars in Virtual Reality Digital-twin Laboratories

Taylor, Mae and Muwaffak, Zaid and Penny, Matthew and Szulc, Blanka R. and Brown, Steven and Merritt, Andy and Hilton, Stephen (2023) The Rise of the AI Scientist: Unleashing the Potential of Chat-GPT Powered Avatars in Virtual Reality Digital-twin Laboratories. [Preprint] (doi:10.26434/chemrxiv-2023-t4vg7) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:115211)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
https://doi.org/10.26434/chemrxiv-2023-t4vg7

Abstract

Digital twin laboratories, accessible via the use of low-cost and portable virtual reality (VR) headsets, have emerged as an immensely powerful tool for chemical education and research collaboration. Having an immersive environment identical to that of a laboratory can provide scientists with a unique platform in which to plan future experiments, conduct laboratory tours, and train on specialist equipment. However, what digital twin laboratories currently lack is on-hand support of co-workers to assist with tasks such as locating chemicals, aiding with machine set-up, and issuing reminders regarding local laboratory health and safety rules Here we show how this key gap can be overcome with the use of knowledge-loaded Chat-GPT avatars in VR. We trained three different chat avatars to perform specialist functions crucial to working in a laboratory and obtained accurate and useful responses in up to 95% of cases, using a range of evaluation metrics including Human Evaluation, Set-Based F1 Scoring, and BERTScore. Our findings demonstrate the vast potential of this technology in harnessing the capabilities of AI assistants for scientists and enhancing immersive digital twin environments within VR settings.

Item Type: Preprint
DOI/Identification number: 10.26434/chemrxiv-2023-t4vg7
Refereed: No
Name of pre-print platform: ChemRvix
Subjects: Q Science
Institutional Unit: Schools > School of Natural Sciences
Schools > School of Natural Sciences > Biosciences
Former Institutional Unit:
There are no former institutional units.
Depositing User: Blanka Hilton
Date Deposited: 15 May 2026 13:41 UTC
Last Modified: 15 Jun 2026 13:21 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/115211 (The current URI for this page, for reference purposes)

University of Kent Author Information

Szulc, Blanka R..

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