1. DSpace KNU
  2. Наукові публікації
  3. 5. Факультет інформаційних технологій
  4. Кафедра професійної та соціально-гуманітарної освіти
Будь ласка, використовуйте цей ідентифікатор, щоб цитувати або посилатися на цей матеріал: http://ds.knu.edu.ua/jspui/handle/123456789/9311
Повний запис метаданих
Поле DCЗначенняМова
dc.contributor.authorMarienko, Maiia V.-
dc.contributor.authorMarkova, Oksana M.-
dc.contributor.authorSemerikov, Serhiy O.-
dc.date.accessioned2026-07-12T09:23:38Z-
dc.date.available2026-07-12T09:23:38Z-
dc.date.issued2026-05-14-
dc.identifier.citationAI literacy in secondary education: framework, assessment, and professional development in the Ukrainian context / Maiia V. Marienko, Oksana M. Markova, Serhiy O. Semerikov // Computers and Education: Artificial Intelligence. – 2026. – Volume 10. – Article 100605. – DOI : https://doi.org/10.1016/j.caeai.2026.100605uk_UA
dc.identifier.issn2666-920X-
dc.identifier.urihttps://doi.org/10.1016/j.caeai.2026.100605-
dc.identifier.urihttp://ds.knu.edu.ua/jspui/handle/123456789/9311-
dc.descriptionCalatrava, A., Asorey, H., Astalos, J., Azevedo, A., Benincasa, F., Blanquer, I., Bobak, M., Brasileiro, F., Codó, L., del Cano, L., Esteban, B., Ferret, M., Handl, J., Kerzenmacher, T., Kozlov, V., Křenek, A., Martins, R., Pavesio, M., Rubio-Montero, A. J., & Sánchez-Ferrero, J. (2023). A survey of the European open science cloud services for expanding the capacity and capabilities of multidisciplinary scientific applications. Computer Science Review, 49, 100571. https://doi.org/10.1016/j.cosrev.2023.100571 Chee, H., Ahn, S., & Lee, J. (2025). A competency framework for AI literacy: Variations by different learner groups and an implied learning pathway. British Journal of Educational Technology, 56, 2146–2182. https://doi.org/10.1111/bjet.13556 Chen, R., Lee, V. R., & Lee, M. G. (2025). A cross-sectional look at teacher reactions, wor­ries, and professional development needs related to generative AI in an urban school district. Education and Information Technologies, 30, 16045–16082. https://doi.org/10.1007/s10639-025-13350-w Chiu, T. K. F., Ahmad, Z., Ismailov, M., & Sanusi, I. T. (2024). What are artifi­cial intelligence literacy and competency? A comprehensive framework to support them. Computers and Education Open, 6, 100171. https://doi.org/10.1016/j.caeo.2024.100171 Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37–46. https://doi.org/10.1177/001316446002000104 Cohen, J. ed). (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates. https://doi.org/10.4324/9780203771587 Creswell, J. W., & Plano Clark, V. L. ed). (2018). Designing and conducting mixed methods research (3rd ed.). SAGE. https://bayanbox.ir/view/236051966444369258/9781483344379-Designing-and-Conducting-Mixed-Methods-Research-3e.pdf. Divya, G. S. (2025). Impact of GenAI on student outcomes. In M. Elkhodr, & E. Gide (Eds.), Generative artificial intelligence empowered learning: A new frontier in educational tech­nology (pp. 139–175). New York: Chapman and Hall/CRC. https://doi.org/10.1201/9781003422433-7 Eusebio, E. J. G., Baldera, P. R., Patiam, A. M. C., Villanueva, E. F., Gaa, N. A., Solis, A. M. F., Soriano, M. L. C., & Ribon, A. L. (2025). AI in the classroom: A systematic review of barriers to educator acceptance. International Journal of Learning, Teaching and Educational Research, 24, 126–147. https://doi.org/10.26803/ijlter.24.9.7 Fetters, M. D., Curry, L. A., & Creswell, J. W. (2013). Achieving integration in mixed methods designs—principles and practices. Health Services Research, 48, 2134–2156. https://doi.org/10.1111/1475-6773.12117 Geels, F. W. (2004). From sectoral systems of innovation to socio-technical systems: Insights about dynamics and change from sociology and institutional theory. Research Policy, 33, 897–920. https://doi.org/10.1016/j.respol.2004.01.015 Granström, M., & Oppi, P. (2025). Assessing teachers’ readiness and perceived usefulness of AI in education: An Estonian perspective. Frontiers in Education, 10, 1622240. https://doi.org/10.3389/feduc.2025.1622240 Hmoud, M., Swaity, H., Hamad, N., Karram, O., & Daher, W. (2024). Higher education students’ task motivation in the generative artificial intelligence context: The case of ChatGPT. Information, 15, 33. https://doi.org/10.3390/info15010033 Jauhiainen, J. S., & Garagorry Guerra, A. (2024). Generative AI and education: Dynamic personalization of pupils’ school learning material with ChatGPT. Frontiers in Education, 9, 1288723. https://doi.org/10.3389/feduc.2024.1288723 Jordan, K. (2015). Massive open online course completion rates revisited: Assessment, length and attrition. International Review of Research in Open and Distributed Learning, 16, 341–358. https://doi.org/10.19173/irrodl.v16i3.2112 Kaplia, O., Ostapenko, E., Tanko, Y., Kaleniuk, S., & Dulibskyy, A. (2024). Digital trans­ formation in education: Navigating its impact amidst war. Multidisciplinary Science Journal, 6, 2024ss0723. https://doi.org/10.31893/multiscience.2024ss0723 Kim, J. (2025). Perceptions and preparedness of K-12 educators in adopting generative AI. Research in Learning Technology, 33, https://doi.org/10.25304/rlt.v33.3448 Kuzheliev, M., Zherlitsyn, D., Nechyporenko, A., Lutkovska, S., & Mazur, H. (2023). Distance learning as a tool for enhancing university academic management processes during the war. Problems and Perspectives in Management, 21, 23–30. https://doi.org/10.21511/ppm.21(2-si).2023.04 Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design con­siderations. In Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1–16). New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3313831.3376727 Marienko, M., & Shyshkina, M. (2023). The design and implementation of the cloud-based system of open science for teachers’ training. In M. E. Auer, W. Pachatz, & T. Rüütmann (Eds.), Learning in the age of digital and green transition (pp. 337–344). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-26876-2_31 Miao, F., & Cukurova, M. (2024). AI competency framework for teachers. Paris: United Nations Educational, Scientific and Cultural Organization. https://doi.org/10.54675/ZJTE2084 Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108, 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x Nagae, Y., Zhang, L., & Farias Herrera, L. (2025). The effects of professional development training on teachers’ AI literacy. In A. I. Cristea, E. Walker, Y. Lu, O. C. Santos, & S. Isotani (Eds.), Artificial intelligence in education (Vol. 15877, pp. 368–380). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-98414-3_26 Lecture Notes in Computer Science. Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041 Ng, D. T. K., Luo, W., Chan, H. M. Y., & Chu, S. K. W. (2022). Using digital story writing as a pedagogy to develop AI literacy among primary students. Computers and Education: Artificial Intelligence, 3, 100054. https://doi.org/10.1016/j.caeai.2022.100054 Ahmad, Z., Sultana, A., Latheef, N. A., Siby, N., Sellami, A., & Abbasi, S. A. (2025). Measuring students’ AI competence: Development and validation of a multidimen­ sional scale integrating educational psychology perspectives. Acta Psychologica, 259, 105446. https://doi.org/10.1016/j.actpsy.2025.105446 AI4K12. 2023. Five big ideas in artificial intelligence v.2. https://bit.ly/ai4k12-five-big-ideas. Bahari, A., & Liu, Y. (2025). AI integration in EFL teacher development: A mixed-methods evaluation of digital competency, professional trajectories, and pedagogical inno­vation within adaptive learning ecosystems. Interactive Learning Environments, https://doi.org/10.1080/10494820.2025.2591251 Balta, N. (2024). Artificial intelligence pedagogical content knowledge. The European Educational Researcher, 1–3. https://doi.org/10.31757/euer.811 Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3, 77–101. https://doi.org/10.1191/1478088706qp063oa Nguyen, T. P. N., & Pham, T. H. (2025). Challenges and opportunities for digital learning resource development: An analysis of AI application in Vietnamese general education. Advances in Artificial Intelligence and Machine Learning, 5, 239–255. https://doi.org/10.54364/AAIML.2025.53239 Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, 100020. https://doi.org/10.1016/j.caeai.2021.100020 Ovcharuk, O. V. (2020). Attitude of Ukrainian educators toward the use of digital tools for teaching and professional development: Survey results. In O. Sokolov, G. Zholtkevych, V. Yakovyna, Y. Tarasich, V. Kharchenko, V. Kobets, O. Burov, S. Semerikov, & H. Kravtsov (Eds.), Proceedings of the 16th international conference on ICT in education, research and industrial Applications. Integration, harmonization and knowledge Transfer. Volume II: workshops, Kharkiv, Ukraine, October 06–10, 2020 (pp. 746–755). CEUR-WS.org. https://ceur-ws.org/Vol-2732/20200746.pdf. Piaget, J., & Inhelder, B. (2000). The psychology of the child. New York: Basic Books. https://www.alohabdonline.com/wp-content/uploads/2020/05/The-Psychology-Of-The-Child.pdf. Ramos, R. F., De Angel, R. M., Ruetas, A. P., Enrile, J. C., Calimbo, A. L., & Vargas, P. C. (2024). Impact assessment of ChatGPT and AI technologies integration in student learning: An analysis for academic policy formulation. In Proceedings - 2024 6th in­ternational workshop on artificial intelligence and education, WAIE 2024 (pp. 87–92). https://doi.org/10.1109/WAIE63876.2024.00023 Rawat, S., Mittal, S., Nehra, D., Sharma, C., & Kamboj, D. (2024). Exploring the poten­tial of ChatGPT to improve experiential learning in education. In Proceedings of the 5th international conference on information management & machine intelligence (pp. 83). New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3647444.3647910. ICIMMI ’23. Sattelmaier, L., & Pawlowski, J. (2025). Be AIware! An AI competency model for K-12 education. Social Sciences & Humanities Open, 12, 101838. https://doi.org/10.1016/j.ssaho.2025.101838 Semerikov, S. O., Vakaliuk, T. A., Mintii, I. S., Bondarenko, O. V., & Kanevska, O. B. (2026). Mapping the emergence: A bibliometric analysis of the convergence gap in GeoAI teacher education research. SN Computer Science, 7, 114. https://doi.org/10.1007/s42979-026-04731-0 Shyshkina, M. (2024). The methodology for using the cloud-based open science systems in higher education institutions. In M. E. Auer, U. R. Cukierman, E. Vendrell Vidal, & E. Tovar Caro (Eds.), Towards a hybrid, flexible and socially engaged higher education (Vol. 899, pp. 287–294). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-51979-6_30 Lecture Notes in Networks and Systems. Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2, https://www.itdl.org/Journal/Jan_05/article01.htm. Somabut, A., Tuamsuk, K., Lowatcharin, G., Traiyarach, S., & Kwangmuang, P. (2025). Preparing for the AI era: Science teachers’ readiness and professional development needs for generative AI integration in secondary education. Social Sciences and Humanities Open, 12, 102259. https://doi.org/10.1016/j.ssaho.2025.102259 Tan, Q. (2025). Factors influencing the adoption of generative AI in education: A sys­tematic review, proposed framework and future research agenda. British Educational Research Journal, https://doi.org/10.1002/berj.70059 Tedre, M., Toivonen, T., Kahila, J., Vartiainen, H., Valtonen, T., Jormanainen, I., & Pears, A. (2021). Teaching machine learning in K–12 classroom: Pedagogical and technolog­ical trajectories for artificial intelligence education. IEEE Access, 9, 110558–110572. https://doi.org/10.1109/ACCESS.2021.3097962 Termenzhy, O., Kozhevnikova, A., & Susukailo, V. (2025). Preparing pre-service teach­ ers for the digital era: Cyberethics, cybersafety, and cybersecurity skills as a core AI competency. In I. R. Opirskyy, M. P. Karpinski, O. Kochan, A. Sikora, T. Maksymyuk, M. Podpora, S. Gnatyuk, & B. Issac (Eds.), Proceedings of the cyber security and data protection, CSDP 2025, Lviv, Ukraine, July 31, 2025 (pp. 132–141). CEUR-WS.org. https://ceur-ws.org/Vol-4042/paper10.pdf. Unal, A., & Unal, Z. (2024). Evaluating the integration of artificial intelligence (AI) in K-12 education. Journal of Interactive Learning Research, 35, 353–387. https://doi.org/10.70725/546995xiatpy Vorotnykova, I. P., Morze, N. V., & Hrynevych, L. M. (2023). Digital transformation of secondary education of Ukraine and the quality of teaching natural and mathemat­ical sciences in the conditions of war. In T. A. Vakaliuk, V. V. Osadchyi, & O. P. Pinchuk (Eds.), Proceedings of the 2nd workshop on digital transformation of education (DigiTransfEd 2023) co-located with 18th international conference on ICT in education, research and industrial applications (ICTERI 2023), Ivano-Frankivsk, Ukraine, September 18–22, 2023 (pp. 57–74). CEUR-WS.org. https://ceur-ws.org/Vol-3553/paper13.pdf. Vovchasta, N., Kan, O., Hlavatska, Y., Sovach, K., & Makukhina, S. (2024). Digitalisation and its role in developing hard skills among university students in Ukraine. Multidisciplinary Reviews, 8, 2024spe070. https://doi.org/10.31893/multirev.2024spe070 Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press. https://doi.org/10.2307/j.ctvjf9vz4 Wolski, M., Martyn, K., & Walter, B. (2022). A recommender system for EOSC. challenges and possible solutions. In R. Guizzardi, J. Ralyté, & X. Franch (Eds.), Research challenges in information science (Vol. 446, pp. 70–87). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-05760-1_5 Lecture Notes in Business Information Processing. Wu, D., Chen, M., Chen, X., & Liu, X. (2024). Analyzing K-12 AI education: A large language model study of classroom instruction on learning theories, pedagogy, tools, and AI literacy. Computers and Education: Artificial Intelligence, 7, 100295. https://doi.org/10.1016/j.caeai.2024.100295 Yue Yim, I. H. (2024). A critical review of teaching and learning artificial intelligence (AI) literacy: Developing an intelligence-based AI literacy framework for primary school education. Computers and Education: Artificial Intelligence, 7, 100319. https://doi.org/10.1016/j.caeai.2024.100319 Zahorodko, P. V., & Semerikov, S. O. (2026). Integrating agile methodologies and AI-assisted learning in web programming education: A theoretical framework for CS cur­riculum transformation. Discover Education, 5, 166. https://doi.org/10.1007/s44217-026-01179-5uk_UA
dc.description.abstractThe rapid proliferation of artificial intelligence tools in educational settings has created an urgent demand for AI literacy among secondary educators, yet empirical research examining this phenomenon in non-Western and crisis-affected contexts remains limited. This multi-study investigation employs a sequential explanatory mixed-methods design to examine AI literacy among Ukrainian secondary educators through national survey analysis (n = 2018), targeted educator surveys (n = 116), professional development evaluation (n = 1130), and systematic mapping of European Open Science Cloud (EOSC) services. Findings reveal that while 84% of surveyed educators report AI use in professional practice, only 11% can identify specialized services beyond ChatGPT, indicating a pattern of high adoption coupled with limited specialized awareness. The study proposes a five-level AI literacy framework (Awareness, Application, Evaluation, Creation, Ethics) integrated with three paradigms of AI in education (AI-directed, AI-supported, AI-empowered). Professional development intervention demonstrates 24% improvement in AI competence, with the largest gains in practical application (+27%) and critical evaluation (+26%). Systematic analysis identifies 22 EOSC AI services applicable to secondary education, particularly in biology and geography. Results provide preliminary evidence suggesting that targeted professional development can help advance AI literacy beyond surface-level tool familiarity toward sophisticated pedagogical integration. The Ukrainian context, marked by crisis-driven digital transformation, offers insights relevant to educational systems worldwide confronting the imperative to prepare teachers for AI-enhanced instruction.uk_UA
dc.language.isoenuk_UA
dc.publisherElsevieruk_UA
dc.subjectAI literacyuk_UA
dc.subjectSecondary educationuk_UA
dc.subjectTeacher professional developmentuk_UA
dc.subjectEducational technologyuk_UA
dc.subjectUkraineuk_UA
dc.subjectEuropean open science clouduk_UA
dc.titleAI literacy in secondary education: framework, assessment, and professional development in the Ukrainian contextuk_UA
dc.typeArticleuk_UA
dc.identifier.doihttps://doi.org/10.1016/j.caeai.2026.100605-
local.submitter.emailsemerikov@ccjourn...uk_UA
Розташовується у зібраннях:Кафедра професійної та соціально-гуманітарної освіти

Файли цього матеріалу:
Файл Опис РозмірФормат 
1-s2.0-S2666920X26000676-main.pdf1.71 MBAdobe PDFПереглянути/Відкрити
Показати базовий опис матеріалу Перегляд статистики


Усі матеріали в архіві електронних ресурсів захищені авторським правом, всі права збережені.