Authors :
Sumiyah Binti Yahaya; Dr. Abdul Aziz Zalay
Volume/Issue :
Volume 10 - 2025, Issue 7 - July
Google Scholar :
https://tinyurl.com/pzspvp7e
Scribd :
https://tinyurl.com/bdz6crxt
DOI :
https://doi.org/10.38124/ijisrt/25jul848
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Note : Google Scholar may take 30 to 40 days to display the article.
Abstract :
This mini-review examines the current trends, benefits, challenges, and future directions of integrating Artificial
Intelligence (AI) in art education. The primary purpose of the review is to explore how AI technologies are reshaping teaching,
learning, and creative practices within visual arts education. A structured literature search was conducted using Scopus and
Google Scholar, focusing on peer-reviewed publications from 2018 to 2024. Boolean search strings incorporating terms such as
"artificial intelligence," "machine learning," "visual arts," "creative education," "curriculum," and "student engagement"
were used to identify relevant studies. Inclusion criteria comprised original research, systematic reviews, meta-analyses, case
studies, and scholarly commentaries written in English and explicitly addressing AI in the context of art education. The findings
reveal that AI applications—including generative adversarial networks (GANs), image recognition, and intelligent tutoring
systems—are being used to support student creativity, personalize learning experiences, and streamline assessment processes.
While these technologies offer promising enhancements to pedagogical practice, the review also identifies significant challenges,
such as ethical concerns, disparities in access, and insufficient teacher training. A notable limitation in the current body of
research is the scarcity of longitudinal studies evaluating the sustained impact of AI on learners' creative development and
instructional outcomes. In conclusion, the integration of AI in art education presents transformative potential but requires
careful implementation supported by ethical frameworks, educator preparedness, and inclusive design. Future research should
prioritize empirical evaluations, interdisciplinary collaboration, and the development of practical guidelines for educators to
effectively integrate AI tools into creative and instructional processes.
Keywords :
AI in Art Education; Generative AI; Educational Ethics; Creative Learning; Teacher Training.
References :
- Fang, F., & Jiang, X. (2024). The analysis of artificial intelligence digital technology in art education under the Internet of Things. IEEE Access https://ieeexplore.ieee.org/iel7/6287639/10380310/10423763.pdf
- Grassini, S. (2023). Shaping the future of education: Exploring the potential and consequences of AI and ChatGPT in educational settings. Education Sciences, 13(7), 692. https://www.mdpi.com/2227-7102/13/7/692
- Guettala, M., Bourekkache, S., & Kazar, O. (2024). Generative artificial intelligence in education: Advancing adaptive and personalized learning. Acta Informatica Pragensia https://www.ceeol.com/search/article-detail?id=1258972
- Guo, J., Ma, Y., Li, T., Noetel, M., Liao, K., & Greiff, S. (2024). Harnessing artificial intelligence in generative content to enhance motivation in learning. Learning and Individual Differences https://www.sciencedirect.com/science/article/pii/S1041608024001407
- He, S. F., Ismail, A. I., & Zhang, M. C. (2025). Enhancing children’s artistic creativity through artificial intelligence technology in Chinese primary schools. Journal of Digital System Development https://e-journal.uu
- Hsiao, K. T., Lee, Y. C., & Su, C. Y. (2023). Application of GAN-based personalized painting system to enhance student creativity in art education. Education and Information Technologies, 28, 4023–4041. https://doi.org/10.1007/s10639-023-11676-5
- Kaban, A., Taş, N., & Polat, H. (2024). Reimagining education with generative artificial intelligence. ISTE https://www.istes.org/public/storage/01JGPTZYMCJRJTEGA44N6FJD00.pdf
- Khare, A., Ahuja, A., & Duhan, M. (2023). Artificial Intelligence in Education: Role, Benefits, and Challenges. Education and Information Technologies, 28, 1363–1385.
- Li, X., & Tang, F. (2022). Interdisciplinary potential of AI in creative education: A conceptual framework. Journal of Educational Technology & Society, 25(3), 41–52.
- Lim, C. P., & Lee, J. (2022). Artificial Intelligence in art education: Opportunities and ethical considerations. Computers & Education, 187, 104556. https://doi.org/10.1016/j.compedu.2022.104556
- Liu, J., Wang, C., Liu, Z., Gao, M., Xu, Y., & Chen, J. (2024). A bibliometric analysis of generative AI in education: Current status and development. Asia Pacific Journal of Education https://www.tandfonline.com/doi/abs/10.1080/02188791.2024.2305170
- Oprea, S. V., & Bâra, A. (2025). Transforming education with large language models: Trends, themes, and untapped potential. IEEE Access https://ieeexplore.ieee.org/abstract/document/11006075/
- Rahimi, F., Sadeghi-Niaraki, A., & Choi, S. M. (2025). Generative AI meets virtual reality: A comprehensive survey of applications, challenges, and future directions. IEEE Access https://ieeexplore.ieee.org/abstract/document/11017583/
- Rosário, A. T. (2024). Generative AI and generative pre-trained transformer applications: Challenges and opportunities. In Making art with generative AI tools. IGI G https://www.igi-global.com/chapter/generative-ai-and-generative-pre-trained-transformer-applications/343418
- Wang, Y., Zhang, L., & Chen, H. (2021). Ethical implications of AI-generated art in education. Journal of Visual Art Practice, 20(1), 73–88.
- Zhang, M., & Yang, Q. (2022). Teachers' digital readiness for AI integration in visual arts education: A survey study. International Journal of Art & Design Education, 41(2), 235–250.
This mini-review examines the current trends, benefits, challenges, and future directions of integrating Artificial
Intelligence (AI) in art education. The primary purpose of the review is to explore how AI technologies are reshaping teaching,
learning, and creative practices within visual arts education. A structured literature search was conducted using Scopus and
Google Scholar, focusing on peer-reviewed publications from 2018 to 2024. Boolean search strings incorporating terms such as
"artificial intelligence," "machine learning," "visual arts," "creative education," "curriculum," and "student engagement"
were used to identify relevant studies. Inclusion criteria comprised original research, systematic reviews, meta-analyses, case
studies, and scholarly commentaries written in English and explicitly addressing AI in the context of art education. The findings
reveal that AI applications—including generative adversarial networks (GANs), image recognition, and intelligent tutoring
systems—are being used to support student creativity, personalize learning experiences, and streamline assessment processes.
While these technologies offer promising enhancements to pedagogical practice, the review also identifies significant challenges,
such as ethical concerns, disparities in access, and insufficient teacher training. A notable limitation in the current body of
research is the scarcity of longitudinal studies evaluating the sustained impact of AI on learners' creative development and
instructional outcomes. In conclusion, the integration of AI in art education presents transformative potential but requires
careful implementation supported by ethical frameworks, educator preparedness, and inclusive design. Future research should
prioritize empirical evaluations, interdisciplinary collaboration, and the development of practical guidelines for educators to
effectively integrate AI tools into creative and instructional processes.
Keywords :
AI in Art Education; Generative AI; Educational Ethics; Creative Learning; Teacher Training.