Key points
- PRISMA systematic review of 78 peer-reviewed studies on artificial intelligence in fine arts education, covering 2019 to 2024.
- Categorises the AI techniques most used in the field, spanning generative adversarial networks, convolutional neural networks, large language models and VR/AR.
- Finds AI adoption concentrated in visual arts education, with gaps in non-visual arts domains and in primary and secondary teaching.
Summary
This PRISMA systematic literature review, published open access in SAGE Open on 13 May 2026, examines 78 peer-reviewed studies (69 empirical, 9 reviews) on AI in fine arts education from 2019 to 2024. It is the broadest review in the cluster and the only one framed around fine arts as a whole, including non-generative techniques (GANs, CNNs) alongside large language models and VR/AR. It maps which fine arts specialisations are adopting AI, finds adoption concentrated in the visual arts, and identifies gaps in non-visual arts domains and school-level education.
Related items
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- Empowering Student Learning in HE with Generative AI Art Applications: Systematic Review
- Generative AI in higher education assessment: a scoping review
Source
Source: SAGE Open ↗ (Research)
Cite this item
Yance Zeng, Harrinni Md Noor & Muhammad Faiz Sabri (2026). ‘Artificial Intelligence in Fine Arts Education: A Systematic Literature Review’, SAGE Open. Available at: https://journals.sagepub.com/doi/10.1177/21582440261447959
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