CONCEPTUAL MODEL OF GENERATIVE ARTIFICIAL INTELLIGENCE INTEGRATION INTO THE PROCESS OF DEVELOPING INFORMATION AND DIGITAL COMPETENCE IN STUDENTS AT THE BASIC SECONDARY EDUCATION LEVEL
DOI:
https://doi.org/10.31110/fmo2025.v40i5-06Keywords:
information and digital competence, generative artificial intelligence (GAI), students, basic secondary education, conceptual model of GAI integrationAbstract
The article considers the integration of generative artificial intelligence (GAI) into developing students' information and digital competence (IDC) in basic secondary education. The relevance of creating a conceptual model that combines pedagogical, cognitive, technological, and ethical dimensions based on the DigComp 2.2 framework and UNESCO recommendations is emphasized.
Formulation of the problem. IDC is becoming increasingly important in the digital society, but traditional approaches in schools do not provide the proper level of its formation. Students often face difficulties in searching and critically analyzing information, and the educational process requires individualization and a broader use of digital tools. The spread of GSI highlights the need for its conceptual implementation, considering pedagogical feasibility, ethics, and security.
Materials and methods. The analysis of scientific and methodological literature, international documents on digital literacy, the results of pedagogical observations, and content analysis of educational practices were employed. The modeling method and structural-functional analysis were employed to construct the model, enabling it to identify the relationships between its components.
Results. A conceptual model of integrating GSI into the development of students' ICT has been developed, with a multi-level implementation. At the student level - personalization of educational trajectories, chat bots, recommendation systems, development of critical thinking; at the teacher level - optimization of routine tasks, analysis of achievements, creation of individual plans; at the management level - analytics for predicting results and correcting programs; at the social level - solving issues of accessibility, data protection, overcoming algorithmic bias. The model encompasses target, subject-object, content, technological, methodological, organizational, ethical, and result-evaluation components, forming a holistic system.
Conclusions. Integrating GSI into teaching opens up new opportunities for developing critical thinking, digital creativity, and student autonomy. The model has both theoretical and practical significance, and can be used to update educational standards, create methodological materials, and inform teacher training programs. Its implementation contributes to the formation of students' readiness for conscious interaction with intelligent technologies in the context of digital transformation.
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References
Annapureddy, R., Fornaroli, A., & Gatica-Pérez, D. (2025). Generative AI literacy: Twelve defining competencies. Digital Government: Research and Practice / ACM, 6, 1(13), 1-21. https://doi.org/10.1145/3685680
Ateş, Н., & Gündüzalp, C. (2025). Proposing a conceptual model for the adoption of artificial intelligence by teachers in STEM education. Interactive Learning Environments, 10, 1-27. https://doi.org/10.1080/10494820.2025.2457350
Casal-Otero, L., Cataka, A., Fernando-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023). AI literacy in K-12: A systematic literature review. International Journal of STEM Education, 10, 29. https://doi.org/10.1186/s40594-023-00418-7
Cheah, Y. H., Lu, J., & Kim, J. (2025). Integrating generative artificial intelligence in K-12 education: Examining teachers’ preparedness, practices, and barriers. Computers and Education: Artificial Intelligence, 8, 100363. https://doi.org/10.1016/j.caeai.2025.100363
Guidance for generative AI in education and research. (2023). In UNESCO eBooks. https://doi.org/10.54675/ewzm9535
Heintz, F., (2021). Three interviews about K-12 AI education in America, Europe, and Singapore. KI – Künstliche Intelligenz, 35(2), 233-237. https://doi.org/10.1007/s13218-021-00730-w
Holmes, W., & Porayska-Pomsta, K. (Eds.). (2022). The ethics of artificial intelligence in education: Practices, challenges, and debates. Routledge. https://doi.org/10.4324/9780429329067
Holmes, W., Porayska-Pomsta, K., et al. (2021). Ethics of AI in education: Towards a community-wide approach. International Journal of Artificial Intelligence in Education,32, 504-526. https://doi.org/10.1007/s40593-021-00239-1
Jiang, B., Dai, J., Zhou, A., Dong, X., Liu, X., Hong, D., Jiang, F., Zheng, L., Zhao, J., Zhang, H., Liu, Y., & Yuan, Z. (2023). Artificial Intelligence Curriculum Guidelines for Primary and Secondary Schools. Journal of East China Normal University (Educational Sciences), 41(3), 121-134. https://doi.org/10.16382/j.cnki.1000-5560.2023.03.013
Kamali, J., Alpat, M. F., & Bozkurt, A. (2024). AI ethics as a complex and multifaceted challenge. International Journal of Educational Technology in Higher Education, 21, 62. https://doi.org/10.1186/s41239-024-00496-9
Lytvynova, S., Vodopian, N., & Sysoeva, O. (2024). Artificial intelligence in secondary education: An innovative teacher’s tool to ensure individualised learning for students. In New media pedagogy: Research trends, methodological challenges, and successful implementations (pp. 393-412). Springer. https://doi.org/10.1007/978-3-031-63235-8_26
Micheuz, P. (2020). Approaches to artificial intelligence as a subject in school education. In T. Brinda, D. Passey, & T. Keane (Eds.), Empowering teaching for digital equity and agency. OCCE 2020. IFIP Advances in Information and Communication Technology (Vol. 595). Springer. https://doi.org/10.1007/978-3-030-59847-1_1
Owoeye, G. (2024). The influence of artificial intelligence on employment trends in the United States (US). International Journal of Innovative Science and Research Technology, 9(6), 1368-1372. https://doi.org/10.38124/ijisrt/ijisrt24jun328
Touretzky, D., Gardner-McCune, C., Breazeal, C., Martin, F., & Seehorn, D. (2019). A year in K-12 AI education. AI Magazine, 40(4), 88-90. https://doi.org/10.1609/aimag.v40i4.5289
Van Audenhove, L., Vermeire, L., Van den Broeck, W., & Demeulenaere, A. (2024). Data literacy in the new EU DigComp 2.2 framework: How DigComp defines competences on artificial intelligence, internet of things, and data. Information & Learning Sciences, 125 (5-6): 406-436. https://doi.org/10.1108/ILS-06-2023-0072
Vuorikari, R., Kluzer, S., & Punie, Y. (2022). DigComp 2.2: The digital competence framework for citizens – With new examples of knowledge, skills and attitudes (EUR 31006 EN). Publications Office of the European Union. https://doi.org/10.2760/115376
Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252(Part A), 124167. https://doi.org/10.1016/j.eswa.2024.124167
Zhou, X., & Schofield, L. (2024). Developing a conceptual framework for artificial intelligence (AI) literacy in higher education. Journal of Learning Development in Higher Education, 31. https://doi.org/10.47408/jldhe.vi31.1354
UNESCO. (2024a). AI competency framework for teachers. Publications Office. https://doi.org/10.2766/153756
UNESCO. (2024b). AI competency framework for students. UNESCO. https://doi.org/10.54675/JKJB9835
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Copyright (c) 2025 Світлана Литвинова, Юлія Носенко, Катерина Осадча, Ольга Пінчук, Наталя Рашевська, Аліса Сухіх
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