Implementation of Generative Artificial Intelligence in a Virtual Reality Exhibition

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Research ID 5D22F

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Abstract

Background: Exhibitions are an activity that is often attended by many companies around the world because of its benefits as a promotional event. Companies can build the image of their company and products and interact directly with buyers through an exhibition. With recent advances in technology, virtual exhibitions have begun being created as an alternative to conventional face-to-face exhibitions. This presents an opportunity to implement other technologies within these new type of exhibition, namely generative artificial intelligence.

Methods: This research involved the use of the Unity game engine to create a virtual environment resembling an exhibition space. Within this environment users can navigate their surroundings and view the products displayed at the exhibition. Generative artificial intelligence was then implemented to generate content based on the products available in the exhibition. A head mounted display and virtual reality controllers were used to view and control the application. Participants were instructed to test the application. Once done, they then filled out a user acceptance test in order to evaluate their experience of using the application and how the use of generative artificial intelligence impacted that experience.

Results: Five participants tested and evaluated the application. The implementation of generative artificial intelligence had a positive impact on the quality of the information provided by the application. In addition, it did not impact the immersion of the experience.

Conclusions: Generative artificial intelligence can be used in a virtual exhibition setting to dynamically generate content regarding available products.

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Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

Not applicable

Data Availability

The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

Funding

This work did not receive any external funding.

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  • Classification

    DCC Code: 006.3, 006.802

  • Version of record

    v1.0

  • Issue date

    05 December 2025

  • Language

    en

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