Latent profiles of AI learning conditions among university students: Implications for educational intentions

Izida I. Ishmuradova; Alexey A. Chistyakov; Tatyana A. Brodskaya; Nikolay N. Kosarenko; Natalia V. Savchenko; Natalya N. Shindryaeva

doi:10.30935/cedtech/15907

Izida I. Ishmuradova ¹ ^* , Alexey A. Chistyakov ², Tatyana A. Brodskaya ³, Nikolay N. Kosarenko ⁴, Natalia V. Savchenko ⁵, Natalya N. Shindryaeva ⁶

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CONT ED TECHNOLOGY, Volume 17, Issue 2, Article No: ep565

https://doi.org/10.30935/cedtech/15907

Submitted: 29 September 2024, Published Online: 30 January 2025

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Abstract

This investigation aimed to ascertain latent profiles of university students predicated on fundamental factors influencing their intentions to acquire knowledge in artificial intelligence (AI). The study scrutinized four dimensions: supportive social norms, facilitating conditions, self-efficacy in AI learning, and perceived utility of AI. Through the utilization of latent profile analysis (LPA), the investigation endeavored to unveil distinct subgroups of students delineated by unique amalgamations of these factors. The study was carried out with a cohort of 391 university students from diverse academic disciplines. LPA disclosed five unique subgroups of students: Cautious Participants, Enthusiastic Advocates, Reserved Skeptics, Pragmatic Acceptors, and Disengaged Critics. These categories showed somewhat different goals to learn AI; Enthusiastic Advocates showed the highest intention while Disengaged Critics showed the lowest. The findings enhance the growing corpus of research on AI education in higher education by providing a sophisticated knowledge of the variation among university students about their attitudes and preparedness to learn AI. Subgroups of students show that learners need unique educational strategies and interventions to meet their diverse needs and attitudes. AI is changing many fields, therefore college students must learn about it and prepare for it. The findings advance AI education research and impact curriculum and policy.

Keywords: artificial intelligence, higher education, latent profile analysis, situated expectancy-value theory, intention to learn AI

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Citation

Ishmuradova, I. I., Chistyakov, A. A., Brodskaya, T. A., Kosarenko, N. N., Savchenko, N. V., & Shindryaeva, N. N. (2025). Latent profiles of AI learning conditions among university students: Implications for educational intentions. Contemporary Educational Technology, 17(2), ep565. https://doi.org/10.30935/cedtech/15907

Ishmuradova, I. I., Chistyakov, A. A., Brodskaya, T. A., Kosarenko, N. N., Savchenko, N. V., and Shindryaeva, N. N. (2025). Latent profiles of AI learning conditions among university students: Implications for educational intentions. Contemporary Educational Technology, 17(2), ep565. https://doi.org/10.30935/cedtech/15907

Ishmuradova II, Chistyakov AA, Brodskaya TA, Kosarenko NN, Savchenko NV, Shindryaeva NN. Latent profiles of AI learning conditions among university students: Implications for educational intentions. CONT ED TECHNOLOGY. 2025;17(2), ep565. https://doi.org/10.30935/cedtech/15907

Ishmuradova, Izida I., Alexey A. Chistyakov, Tatyana A. Brodskaya, Nikolay N. Kosarenko, Natalia V. Savchenko, and Natalya N. Shindryaeva. "Latent profiles of AI learning conditions among university students: Implications for educational intentions". Contemporary Educational Technology 2025 17 no. 2 (2025): ep565. https://doi.org/10.30935/cedtech/15907

Ishmuradova, Izida I. et al. "Latent profiles of AI learning conditions among university students: Implications for educational intentions". Contemporary Educational Technology, vol. 17, no. 2, 2025, ep565. https://doi.org/10.30935/cedtech/15907

Ishmuradova II, Chistyakov AA, Brodskaya TA, Kosarenko NN, Savchenko NV, Shindryaeva NN. Latent profiles of AI learning conditions among university students: Implications for educational intentions. CONT ED TECHNOLOGY. 2025;17(2):ep565. https://doi.org/10.30935/cedtech/15907