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Generative AI in the screen and live performance industries: A conceptual framework and prospects for future research

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journal contribution
posted on 2025-09-29, 09:00 authored by Jie HuangJie Huang, Graham HitchenGraham Hitchen, Safak DoganSafak Dogan
<p dir="ltr">Generative Artificial Intelligence (AI) has drawn widespread attention, with its capabilities and transformative potential drawing significant public and academic interest. Despite the growing industry and scholarly focus, the existing literature remains fragmented, lacking a cohesive framework to understand its complexity and underlying dynamics. This study addresses this gap by presenting a conceptual framework derived from a review of 98 peer-reviewed articles and conference papers published since 2020. It provides an overview of generative AI’s role across film, television, gaming, animation, extended reality (XR), and live performance sectors, offering an initial snapshot of the state of research and laying a foundation for future studies in this evolving field. Drawing on this analysis, the comprehensive review maps the key themes of the generative AI literature and explores their conceptual relationships. It identifies areas of progress and highlights gaps in the existing knowledge, proposing directions for further research to deepen the understanding of the field. By providing an in-depth examination, this study contributes to a more nuanced and informed perspective on generative AI’s integration and impact within the screen and live performance industries.</p>

Funding

AHRC CoSTAR programme (AH/Y007433/1)

History

School

  • Loughborough University, London

Published in

Convergence: the international journal of research into new media technologies

Pages

(35)

Publisher

International Council for Adult Education

Version

  • VoR (Version of Record)

Rights holder

© The Author(s)

Publisher statement

This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).

Acceptance date

2025-09-13

Publication date

2025-09-25

Copyright date

2025

ISSN

1354-8565

eISSN

1748-7382

Language

  • en

Depositor

Dr Safak Dogan. Deposit date: 26 September 2025

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