Svitlana Lytvynova
https://orcid.org/0000-0002-5450-6635
SYNERGY OF GENERATIVE ARTIFICIAL INTELLIGENCE AND MOBILE LEARNING: A DYNAMIC MODEL FOR THE CONCEPTUAL TRANSFORMATION OF EDUCATIONAL PROCESSES
Full text (pdf)
Language: Ukrainian
Abstract. The purpose of this article is to theoretically substantiate the transformation of mobile learning under the influence of generative artificial intelligence (GenAI) and to develop a conceptual dynamic model for integrating mobile AI tools into contemporary educational practice. The study examines the shift of the smartphone’s role from a content consumption device to an active cognitive learning tool in conditions of ubiquitous access to generative language models. Based on an analysis of current research, analytical data on students’ use of mobile AI tools in 2025, and the generalization of pedagogical experience, the study identifies the risks of a “new digital divide” caused by unequal access to methodologically supported AI-enhanced learning. It is shown that a significant proportion of students either use AI tools spontaneously or remain excluded from AI-supported learning due to the lack of pedagogical guidance. The article presents an original dynamic model, M-AI-L (Mobile-AI-Learning), grounded in the principles of connectivism, distributed cognition theory, social constructivism, and heutagogy. The model interprets a mobile device with an AI component as a digital intellectual assistant and describes a sequence of sensory-perceptual perception, differentiated processing of information, critical validation, and reflective knowledge synthesis. Special attention is given to the development of AI literacy, critical thinking, students’ learning agency, and digital resilience, as well as to ethical issues related to the use of generative AI in education. The findings may be used for the modernization of educational programmes and the development of methodological guidelines for implementing mobile GenAI-supported learning in secondary and higher education institutions.
Keywords: mobile learning, generative artificial intelligence, AI literacy, critical thinking, digital resilience, educational innovation, M-AI-L.
https://doi.org/10.32987/2617-8532-2026-2-42-60
Keywords: mobile learning, generative artificial intelligence, AI literacy, critical thinking, digital resilience, educational innovation, M-AI-L.
https://doi.org/10.32987/2617-8532-2026-2-42-60
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6. Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng. M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of AIEd. Computers and Education: Artificial Intelligence, 4, 100118. DOI: https://doi.org/10.1016/j.caeai.2022.100118.
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12. Ali, J. K. M., Shamsan, M. A. A., Hezam, T. A., & Mohammed A. A. Q. (2023). Impact of ChatGPT on learning motivation: Teachers and students’ voices. Journal of English Studies in Arabia Felix, 2(1), 41-49. DOI: https://doi.org/10.56540/jesaf.v2i1.51.
13. Wang, X., Pang, H., Wallace, M. P., Wang, Q., & Chen, W. (2024). Learners’ perceived AI presences in AI-supported language learning: a study of AI as a humanized agent from community of inquiry. Computer Assisted Language Learning, 37(4), 814–840. DOI: https://doi.org/10.1080/09588221.2022.2056203.
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28. Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227-268. DOI: https://doi.org/10.1207/S15327965PLI1104_01.
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31. Chiu, T. K. F., Chen, Y., Yau, K. W., Chai, C. S., Meng, H., King, I., …& Yam, Y. (2024). Developing and validating measures for AI literacy tests: From self-reported to objective measures. Computers and Education: Artificial Intelligence, 7, 100282. DOI: https://doi.org/10.1016/j.caeai.2024.100282.
Received January 27, 2026
Accepted May 14, 2026
Published May 28, 2026
Accepted May 14, 2026
Published May 28, 2026
