Reframing Artificial Intelligence in Art Education: A Systematic Review of Learning, Creativity and Opportunities in Visual Arts

ABSTRACT The rapid expansion of Artificial Intelligence in Education has significantly reshaped teaching, learning and assessment across mainstream educational domains. However, the integration of AI within Visual Art Education remains comparatively underexplored, fragmented and often positioned at the periphery of dominant educational technology discourses. Addressing this gap, this systematic review critically examines how AI‐driven and digital technologies are conceptualised, implemented and evaluated in visual art education, with particular attention to their implications for creativity, pedagogy and sustainability. Following PRISMA guidelines, a comprehensive search was conducted across major academic databases, including Scopus, Web of Science and ERIC, covering peer‐reviewed publications from 2020 to 2025. An initial pool of 685 records was identified, of which 33 studies met the inclusion and quality criteria after rigorous screening and independent review. The findings reveal weak integration between technological innovation, established learning theories and the epistemological foundations of artistic practice. This fragmentation constrains the development of coherent, discipline‐sensitive AI pedagogies in the arts. By situating visual art education within the broader landscape of AI in education research, this review provides a critical synthesis that not only maps current trends but also exposes structural gaps in knowledge production. It calls for more theoretically grounded, context‐sensitive and interdisciplinary approaches to ensure that art education is not marginalised in the evolving AI‐driven educational paradigm.

Authors

Institutions

Publication Details

Journal
European Journal of Education
Published
2026-05-16
DOI
https://doi.org/10.1111/ejed.70676
Primary Topic
Art Education and Development
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Reframing Artificial Intelligence in Art Education: A Systematic Review of Learning, Creativity and Opportunities in Visual Arts

Tong Hong
European Journal of Education
Art Education and Development
article

Reframing Artificial Intelligence in Art Education: A Systematic Review of Learning, Creativity and Opportunities in Visual Arts

Tong Hong
article en

Abstract

ABSTRACT The rapid expansion of Artificial Intelligence in Education has significantly reshaped teaching, learning and assessment across mainstream educational domains. However, the integration of AI within Visual Art Education remains comparatively underexplored, fragmented and often positioned at the periphery of dominant educational technology discourses. Addressing this gap, this systematic review critically examines how AI‐driven and digital technologies are conceptualised, implemented and evaluated in visual art education, with particular attention to their implications for creativity, pedagogy and sustainability. Following PRISMA guidelines, a comprehensive search was conducted across major academic databases, including Scopus, Web of Science and ERIC, covering peer‐reviewed publications from 2020 to 2025. An initial pool of 685 records was identified, of which 33 studies met the inclusion and quality criteria after rigorous screening and independent review. The findings reveal weak integration between technological innovation, established learning theories and the epistemological foundations of artistic practice. This fragmentation constrains the development of coherent, discipline‐sensitive AI pedagogies in the arts. By situating visual art education within the broader landscape of AI in education research, this review provides a critical synthesis that not only maps current trends but also exposes structural gaps in knowledge production. It calls for more theoretically grounded, context‐sensitive and interdisciplinary approaches to ensure that art education is not marginalised in the evolving AI‐driven educational paradigm.

European Journal of EducationVol. 61(2)
Hangzhou Vocational and Technical College (CN)
Quality Education
Openalex Percentile: Top 1%
Art Education and Development
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.