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A review of 2010 to 2025 research maps how generative AI is used in film, music and games

Study title: Transforming Entertainment Through AI-Driven Creativity: Applications and Industry Impacts

This peer-reviewed study reviews literature from 2010 to 2025, including journal articles, industry reports and case studies, using thematic analysis. It maps how generative models such as GANs, VAEs and GPT-style systems are used across film, music, gaming and visual arts.

Read the study abstract

Generative Artificial Intelligence (AI) is emerging as a transformative force in the entertainment industry, automating creative processes across film, music, gaming, and visual arts by leveraging advanced architectures such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Generative Pre-trained Transformers (GPTs) to produce original, high-fidelity content. This study synthesizes and critically evaluates literature from 2010–2025, including peer-reviewed articles, industry reports…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-2249· cite & verify
Peer-reviewed study: INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Trace impact readingContested
P 70The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.

Both sides are scored from claims and sources, not community votes.

G 66The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.

In brief

The findings matter because the reported efficiency gains sit alongside unresolved questions over ownership, employment, misuse and bias. Because the figures come from a synthesis of existing sources rather than a new measurement, the size of these effects in any given studio or market is uncertain.

Main points

  1. The review covers literature from 2010 to 2025, including peer-reviewed articles, industry reports and case studies.
  2. Reported benefits include production timelines up to 40% faster and operational costs up to 30% lower.
  3. Applications named include AI-assisted scriptwriting, virtual actor creation, procedural game world generation and AI-composed soundtracks.
  4. Ethical risks named include copyright ambiguity, job displacement, deepfake misuse and algorithmic bias.

The gain

A literature synthesis reports that generative AI can shorten entertainment production timelines and lower operating costs while enabling new creative formats.

The problem

The same review identifies copyright ambiguity, job displacement, deepfake misuse and algorithmic bias as significant ethical challenges of generative AI in entertainment.

The rundown

The study treats generative AI as a force automating creative processes across several entertainment sectors, using architectures including Generative Adversarial Networks, Variational Autoencoders and Generative Pre-trained Transformers to produce original, high-fidelity content.

On the recommendations side, the authors call for robust governance frameworks, inclusive training datasets and transparent AI usage policies. They say future research should prioritize cross-sector regulatory harmonization and explainable AI models to foster responsible adoption.

Sources

  1. Peer-reviewedINTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT2025-08-21

The debate