Understanding differences in student engagement with AI in art education through a cluster analysis approach

Yerlan Baikulakov; Anarbek  Kozybay; Rabiga  Kenzhebekova; Moldabek  Kulakhmet; Moldir  Kumisbekova

doi:10.32674/29brhf21

Authors

Yerlan Baikulakov Abai Kazakh National Pedagogical University
Anarbek Kozybay https://orcid.org/0000-0002-4043-7771
Rabiga Kenzhebekova
Moldabek Kulakhmet https://orcid.org/0000-0003-3251-8819
Moldir Kumisbekova https://orcid.org/0009-0009-1787-2263

DOI:

https://doi.org/10.32674/29brhf21

Keywords:

Art Education, artificial intelligence, Cluster Analysis, Student Engagement

Abstract

This study explores how university students in Kazakhstan experience and engage with artificial intelligence (AI) tools in their art education. Focusing on students’ creative thinking, digital self-confidence, and attitudes toward AI, we gathered responses from 248 participants studying in design and art-related programs across four universities. Using a self-developed survey, we analysed the data with clustering techniques to identify four different student profiles, each reflecting different levels of creativity and comfort with AI: Practical Technophiles, Creative Traditionalists, Disengaged Learners, and Balanced Creatives. The findings reveal that students vary widely in how they perceive and use AI in their creative processes. While some embrace AI as a helpful and inspiring tool, others remain cautious, preferring traditional artistic approaches. These differences indicate the importance of flexible teaching strategies that respect students' diverse perspectives. By taking these findings into account, educators can create more inclusive and effective art education practices that integrate digital tools without losing sight of artistic identity and student agency.

References

Aldazharova, S., Issayeva, G., Maxutov, S., & Balta, N. (2024). Assessing AI’s problem solving in physics: Analyzing reasoning, false positives and negatives through the force concept inventory. Contemporary Educational Technology, 16(4), ep538. https://doi.org/10.30935/cedtech/15592

Bertrand, M. G., Namukasa, I. K., & Li, L. (2023). STEAM camp: Teaching middle school students mathematics, science and coding through digital designs. Journal of Research in Science, Mathematics and Technology Education, 6(SI), 47-67. https://doi.org/10.31756/jrsmte.213SI

Carceller, A. T. (2024). The ARTificial Revolution: Challenges for Redefining Art Education in the Paradigm of Generative Artificial Intelligence. Digital Education Review, 45, 84–90.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

de los Ángeles Domínguez-González, M., Hervás-Gómez, C., Díaz-Noguera, M. D., & Reina-Parrado, M. (2023). Attention to diversity from artificial intelligence. The European Educational Researcher, 6(3), 101-115. https://doi.org/10.31757/euer.633

Erişti, S. D. B., & Freedman, K. (2024). Integrating Digital Technologies and AI in Art Education: Pedagogical Competencies and the Evolution of Digital Visual Culture. Participatory Educational Research, 11(Prof. Dr. H. Ferhan Odabaşı Gift Issue), 57-79.

El Bedewy, S., Lavicza, Z., Sabitzer, B., Houghton, T., & Nurhasanah, F. (2024). Exploring transdisciplinary, technology-assisted, and architectural modelling STEAM practices through a cultural lens. European Journal of Science and Mathematics Education, 12(2), 211-235. https://doi.org/10.30935/scimath/14304

Garcia, M. B. (2024). The paradox of artificial creativity: Challenges and opportunities of generative AI artistry. Creativity Research Journal, 1–14.

Gil-Glazer, Y. A. (2020). Visual culture and critical pedagogy: From theory to practice. Critical Studies in Education, 61(1), 66–85.

Hanshaw, S., & Miller, M. D. (2024). Exploring the effectiveness of AI course assistants on student performance and motivation. Open Praxis, 16(4), 719. https://doi.org/10.55982/openpraxis.16.4.719

Hare, R., Ferguson, S., & Tang, Y. (2025). Enhancing student experience and learning with iterative design in an intelligent educational game. British Journal of Educational Technology, 56(2), 551–568.

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.

Irbite, A., & Strode, A. (2021, May). Artificial intelligence vs designer: The impact of artificial intelligence on design practice. In SOCIETY. INTEGRATION. EDUCATION. Proceedings of the International Scientific Conference (Vol. 4, pp. 539–549).

Karwowski, M., Lebuda, I., Wisniewska, E., & Gralewski, J. (2013). Big Five personality traits as the predictors of creative self-efficacy and creative personal identity: Does gender matter? The Journal of Creative Behavior, 47(3), 215–232. https://doi.org/10.1002/jocb.32

Kim, K. H. (2006). Can we trust creativity tests? A review of the Torrance Tests of Creative Thinking (TTCT). Creativity Research Journal, 18(1), 3–14. https://doi.org/10.1207/s15326934crj1801_2

Lazkani, O. (2024). Revolutionizing education of art and design through ChatGPT. In Artificial Intelligence in Education: The Power and Dangers of ChatGPT in the Classroom (pp. 49–60). Cham: Springer Nature Switzerland.

Lee, Y. F., Lin, C. J., Hwang, G. J., Fu, Q. K., & Tseng, W. H. (2023). Effects of a mobile-based progressive peer-feedback scaffolding strategy on students’ creative thinking performance, metacognitive awareness, and learning attitude. Interactive Learning Environments, 31(5), 2986–3002.

Leonard, N. (2023). Review of Post-Digital, Post-Internet Art and Education: The Future Is All-Over. Studies in Art Education, 64(4), 491–497.

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.

McCormack, J., Gifford, T., & Hutchings, P. (2019). Autonomy, authenticity, authorship and intention in computer generated art. The Journal of Creative Music Systems, 2(1).

McCormack, J., Gifford, T., & Hutchings, P. (2019, April). Autonomy, authenticity, authorship and intention in computer generated art. In International conference on computational intelligence in music, sound, art and design (part of EvoStar) (pp. 35–50). Cham: Springer International Publishing.

Mikrouli, P., Tzafilkou, K., & Protogeros, N. (2024). Applications and learning outcomes of game based learning in education. International Educational Review, 2(1), 25-54. https://doi.org/10.58693/ier.212

Nathan, L. F. (2017). Creativity, innovation and the power of arts in education: One path forward. Retrieved from https://lindanathan.com/wp-content/uploads/2022/11/Creativity-Innovation-and-the-Power-of-Arts-in-Education-One-Path-Forward_Linda-Nathan.pdf

National Art Education Association (NAEA). (2023). Position statement on the use of artificial intelligence (AI) and AI-generated imagery in visual arts education. https://www.arteducators.org/advocacy-policy/articles/1303-naea-position-statement-on-use-of-artificial-intelligence-ai-and-ai-generated-imagery-in-visual-arts-education

Okwara, V., & Henrik Pretorius, J. P. (2023). The STEAM vs STEM Educational Approach: The Significance of the Application of the Arts in Science Teaching for Learners’ Attitudes Change. Journal of Culture and Values in Education, 6(2), 18-33. https://doi.org/10.46303/jcve.2023.6

Park, Y. S. (2023). Creative and critical entanglements with AI in art education. Studies in Art Education, 64(4), 406–425.

Pente, P., Adams, C., & Yuen, C. (2022). Artificial Intelligence, ethics, and art education in a posthuman world. In Global media arts education: Mapping global perspectives of media arts in education (pp. 197–211). Cham: Springer International Publishing.

Runco, M. A., & Jaeger, G. J. (2012). The standard definition of creativity. Creativity Research Journal, 24(1), 92–96. https://doi.org/10.1080/10400419.2012.650092

Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.

Torrance, E. P. (1974). Torrance Tests of Creative Thinking: Norms-technical manual. Scholastic Testing Service.

Ogurlu, U., & Mossholder, J. (2023). The Perception of ChatGPT among Educators: Preliminary Findings. Research in Social Sciences and Technology, 8(4), 196-215. https://doi.org/10.46303/ressat.2023.39

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

Verganti, R., Vendraminelli, L., & Iansiti, M. (2020). Innovation and design in the age of artificial intelligence. Journal of Product Innovation Management, 37(3), 212–227.

Wang, S., Sun, Z., & Chen, Y. (2023). Effects of higher education institutes’ artificial intelligence capability on students’ self-efficacy, creativity and learning performance. Education and Information Technologies, 28(5), 4919–4939. https://doi.org/10.1007/s10639-022-11338-4

Yilmaz, H., Maxutov, S., Baitekov, A., & Balta, N. (2023). Student attitudes towards Chat GPT: A technology acceptance model survey. International Educational Review, 1(1), 57-83.

Yu, C., Wang, X., Mascarinas, A., Rakthin, C., Namtubtim, N., Shen, Y., & Anwar, K. (2024). The Implementation of Art Appreciation Courses in Chinese University General Education: A Case Study. Journal Of Curriculum Studies Research, 6(1), 60-82. https://doi.org/10.46303/jcsr.2024.5

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16, 39. https://doi.org/10.1186/s41239-019-0171-0