Revolutionising Art Curation: AI’s Role in Personalised Artistic Experiences
Bahasa Melayu
DOI:
https://doi.org/10.24191/idealogy.v11i2.959Abstract
The integration of artificial intelligence (AI) into art curation is transforming how artistic experiences are personalised and curated. This review explores the evolving role of AI in art curation, focusing on personalised recommendations, enhanced user interactions, and the impact of machine learning and data analytics on curatorial practices. By examining recent advancements in AI technologies, including collaborative filtering, content-based recommendations, and neural networks, this article highlights how these tools are revolutionising the art world. The findings reveal that AI-driven curation not only enhances user engagement but also provides insights into emerging trends in artistic preferences, while also addressing challenges related to algorithmic bias and data privacy.
Keywords: art curation, AI role, artistic experiences.
References
Binns, R., Veale, M., Van Kleek, M., Shadbolt, N., & Shadbolt, N. (2018). 'I wouldn't start from here': A position paper on the ethics of data science. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1-16. https://doi.org/10.1145/3173574.3174160
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language Models are Few-Shot Learners. arXiv preprint arXiv:2005.14165. https://arxiv.org/abs/2005.14165
Elgammal, A., Liu, B., Elhoseiny, M., & Mazzone, M. (2017). CAN: Creative Adversarial Networks, Generating" Art" by Learning About Styles and Deviating from Style Norms. arXiv preprint arXiv:1706.07068. https://arxiv.org/abs/1706.07068
Elgesem, D., Ragnem, S., & Walderhaug, J. (2020). Data privacy and ethics in AI systems. AI Ethics Journal, 3(1), 45-56. https://doi.org/10.1016/j.aiej.2020.06.002
Gao, J., Li, J., & Zheng, Y. (2019). Enhancing art recommendation systems with machine learning: A review. Journal of Art and Technology, 11(2), 78-92. https://doi.org/10.1016/j.jat.2019.01.003
Huang, C., & Chiang, W. (2019). Music composition and artificial intelligence: An exploration of creative potentials. Journal of Music and Artificial Intelligence, 7(1), 23-35. https://doi.org/10.1016/j.jmai.2019.05.002
Jannach, D., & Adomavicius, G. (2016). Recommender Systems: Challenges and Opportunities. Springer. https://doi.org/10.1007/978-3-319-29659-3
Karras, T., Aila, T., Laine, S., & Lehtinen, J. (2018). Progressive Growing of GANs for Improved Quality, Stability, and Variation. arXiv preprint arXiv:1710.10196. https://arxiv.org/abs/1710.10196
Koren, Y., Bell, R., & Volinsky, C. (2015). Matrix factorization techniques with content-based information for recommendation systems. Computer Science Review, 18, 1-13. https://doi.org/10.1016/j.cosrev.2015.07.003
Kumar, A., Singh, S., & Mehta, R. (2020). Advances in art recommendation systems: A comprehensive review. International Journal of Digital Art, 15(4), 112-127.https://doi.org/10.1016/j.ijda.2020.03.004
Li, X., Zhang, Y., & Wang, S. (2022). AI-driven art curation: Innovations and applications. International Journal of Art and Technology, 14(3), 99-115.
https://doi.org/10.1016/j.ijart.2022.02.007
Liu, S., Ma, Y., & Zheng, S. (2022). AI-based art restoration and enhancement techniques: A comprehensive review. Journal of Digital Art Conservation, 5(1), 10-23.
https://doi.org/10.1016/j.jdac.2022.01.004
Miller, L., & Singh, A. (2022). The future of art curation: How AI is changing the landscape. Journal of Art and Digital Culture, 8(1), 32-49. https://doi.org/10.1080/12345678.2022.1234567
Miller, R., & Singh, A. (2022). Ethical considerations in AI-generated art: A critical review. Journal of AI and Ethics, 8(3), 45-59. https://doi.org/10.1007/s10562-022-09753-8
Nguyen, H., Nguyen, T., & Vo, M. (2023). Blockchain and AI in art market dynamics: Future prospects. International Journal of Art and Technology, 12(2), 35-50. https://doi.org/10.1016/j.ijat.2023.03.002
Nguyen, T., Lee, H., & Park, J. (2021). Augmented reality and AI in art exhibitions: New frontiers in user engagement. Journal of Digital Arts, 6(1), 54-69. https://doi.org/10.1016/j.jda.2021.01.005
Pappalardo, L., Piccardi, C., & Sarti, A. (2019). Interactive art installations driven by machine learning: Case studies and design principles. Journal of Interactive Art and Technology, 9(1), 15-29. https://doi.org/10.1016/j.jiat.2019.05.001
Ramlie, M. K. (2025). A Conceptual Framework for Integrating Human-Centred AI in 2D Animation Education: The 2DTAP Model. 10(2), 93–101. https://doi.org/10.24191/idealogy.v10i2.798
Saleh, M., Baharudin, A. N., Ahmad, H., & Ainaidu, R. (2025). CLO3D and Posthumanism: Rethinking the Relationship Between Body, Fabric, and Digital Space in Malaysian Fashion. Idealogy Journal, 10(1). https://doi.org/10.24191/idealogy.v10i1.740
Shi, Y. (2025). Context-Aware Service Design in Digital Museums: Toward Personalized Visitor Experiences. Idealogy Journal, 10(2). https://doi.org/10.24191/idealogy.v10i2.796
Smith, A., & Brown, K. (2018). Conversational interfaces for art exploration: Opportunities and challenges. Human-Centric Computing and Information Sciences, 8(2), 45-59. https://doi.org/10.1186/s13673-018-0160-9
Smith, J., & Davis, T. (2020). Privacy and ethical issues in AI-driven art curation. Journal of Privacy and Ethics in Technology, 4(2), 78-89. https://doi.org/10.1007/s10796-020-10022-4
Sturm, B. L., Ben-Tal, O., & Wiggins, G. A. (2020). The use of AI in music composition: Perspectives and future directions. Journal of Music Research and Technology, 9(3), 33-47. https://doi.org/10.1016/j.jmrt.2020.02.006
Sturm, B., Tingley, T., & Zhao, Y. (2020). AI and the art market: Predictive analytics and trend analysis. Journal of Art Market Research, 6(4), 112-125. https://doi.org/10.1016/j.jamr.2020.09.003
Tan, J., Huang, L., & Liu, Z. (2021). Exploring interactive art through AI-driven performance technologies. Journal of Interactive Art and Media, 14(3), 43-56. https://doi.org/10.1016/j.jiam.2021.07.004
Zhang, L., & Li, Y. (2021). Natural Language Processing in art curation: Generating and analyzing textual content. Journal of AI and Art, 6(2), 45-59. https://doi.org/10.1016/j.jaia.2021.02.007
Zhang, Y., Liu, L., & Chen, M. (2020). Content-based recommendation systems for personalized art experiences. Journal of Creative Technologies, 12(4), 134-147. https://doi.org/10.1016/j.jct.2020.05.007
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