THE CAPACITY OF PRE-SERVICE STEM TEACHERS FOR PERSONALIZED LEARNING IN INCLUSIVE EDUCATION UNDER UNSTABLE INTERNET ACCESS
DOI:
https://doi.org/10.31110/INTEPES.03Keywords:
personalization of learning, STEM education, future teachers, inclusive education, soft skills, offline learning, unstable Internet, differentiated instruction, digital competence, pedagogical diagnosticsAbstract
The chapter substantiates the capacity of pre-service STEM teachers to personalize learning as an integrated soft skill that combines methodological flexibility, inclusive sensitivity, digital-pedagogical maturity, emotional support, and readiness to act under conditions of unstable or absent Internet access. The problem is relevant because personalized learning in STEM education is often associated with digital platforms, adaptive systems, and artificial intelligence tools, whereas effective pedagogical practice requires maintaining individualized support even in contexts with limited digital infrastructure. Particular attention is paid to inclusive education, where personalization is a condition for the accessibility of mathematical, scientific, and technological content for learners with different learning capacities, work pace, cognitive characteristics, and special educational needs. The purpose of the study was to theoretically substantiate and empirically describe the capacity of pre-service STEM teachers to personalize learning in an inclusive, offline-oriented environment. The methodology combined the analysis of current scholarly sources and a pilot quasi-experimental study based on the design “pre-test – formative intervention – post-test.” The study involved 38 future mathematics teachers who formed one pilot group. The dynamics of their capacity for personalizing learning were examined by comparing results before and after a short-term formative intervention. The results showed clear positive dynamics after the formative intervention: participants more often proposed specific offline adaptations, identified learning difficulties more accurately, varied explanations, adapted the pace, and used emotionally supportive communication. It has been demonstrated that offline conditions may function not only as a technical limitation, but also as a diagnostic environment that reveals the depth of pedagogical readiness for personalization. The practical significance of the results lies in the possibility of using the proposed approach in the methodological training of future STEM teachers.
References
Aguhayon, H. H., Tingson, R. J., & Pentang, J. T. (2023). Addressing students’ learning gaps in mathematics through differentiated instruction. International Journal of Educational Management and Development Studies, 4(1), 69–87. https://doi.org/10.53378/352967
Ayanwale, M. A., Adelana, O. P., Molefi, R. R., Adeeko, O., & Ishola, A. M. (2024). Examining artificial intelligence literacy among pre-service teachers for future classrooms. Computers and Education Open, 6, Article 100179. https://doi.org/10.1016/j.caeo.2024.100179
Calandri, E., Mastrokoukou, S., Marchisio, C., Monchietto, A., & Graziano, F. (2025). Teacher emotional competence for inclusive education: A systematic review. Behavioral Sciences, 15(3), Article 359. https://doi.org/10.3390/bs15030359
Chan, C. K. Y., & Lee, K. K. W. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers? Smart Learning Environments, 10, Article 56. https://doi.org/10.1186/s40561-023-00269-3
Chavarría, R. M., González-Montesino, R. H., López-Bastías, J. L., & Vega, M. D. (2023). Universal design for learning and instruction: Effective strategies for inclusive higher education. Education Sciences, 13(6), Article 620. https://doi.org/10.3390/educsci13060620
Chee, H., Ahn, S., & Lee, J. (2024). A competency framework for AI literacy: Variations by different learner groups and an implied learning pathway. British Journal of Educational Technology, 55(6), 3120–3143. https://doi.org/10.1111/bjet.13556
Chiu, T. K. F., Falloon, G., Song, Y., Wong, V., Zhao, L., & Ismailov, M. (2024). A self-determination theory approach to teacher digital competence development. Computers & Education, 223, Article 105017. https://doi.org/10.1016/j.compedu.2024.105017
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE.
DeCoito, I., & Estaiteyeh, M. (2022). Online teaching during the COVID-19 pandemic: Exploring science/STEM teachers’ curriculum and assessment practices in Canada. Disciplinary and Interdisciplinary Science Education Research, 4, Article 8. https://doi.org/10.1186/s43031-022-00048-z
Donath, J. L., Lüke, T., Graf, E., Tran, U. S., & Götz, T. (2023). Does professional development effectively support the implementation of inclusive education? A meta-analysis. Educational Psychology Review, 35(4), Article 87. https://doi.org/10.1007/s10648-023-09752-2
Doolittle, P. E., Wojdak, K., & Walters, A. L. (2023). Defining active learning: A restricted systemic review. Teaching & Learning Inquiry, 11, Article 25. https://doi.org/10.20343/teachlearninqu.11.25
Drushlyak, M., Semenikhina, O., Kharchenko, I., Mulesa, P., & Shamonia, V. (2023). Effectiveness of digital technologies in inclusive learning for teacher preparation. Journal of Learning for Development, 10(2), 177–195. https://doi.org/10.56059/jl4d.v10i2.777
Estaiteyeh, M., & DeCoito, I. (2023). Planning for differentiated instruction: Empowering teacher candidates in STEM education. Canadian Journal of Science, Mathematics and Technology Education, 23(4), 738–755. https://doi.org/10.1007/s42330-023-00270-5
Gómez-Pablos, V. B., Matarranz, M., Casado-Aranda, L.-A., & Otto, A. (2022). Teachers’ digital competencies in higher education: A systematic literature review. International Journal of Educational Technology in Higher Education, 19, Article 8. https://doi.org/10.1186/s41239-021-00312-8
Henriksen, D., Creely, E., Gruber, N., & Leahy, S. (2025). Social-emotional learning and generative AI: A critical literature review and framework for teacher education. Journal of Teacher Education. Advance online publication. https://doi.org/10.1177/00224871251325058
Hughes, B. S., Corrigan, M. W., Grove, D., Andersen, S., & Wong, J. T. (2022). Integrating arts with STEM and leading with STEAM to increase science learning with equity for emerging bilingual learners in the United States. International Journal of STEM Education, 9, Article 63. https://doi.org/10.1186/s40594-022-00375-7
Ismailov, M., & Chiu, T. K. F. (2022). Catering to inclusion and diversity with universal design for learning in asynchronous online education: A self-determination theory perspective. Frontiers in Psychology, 13, Article 819884. https://doi.org/10.3389/fpsyg.2022.819884
Jardinez, M. J., & Natividad, L. R. (2024). The advantages and challenges of inclusive education: Striving for equity in the classroom. Shanlax International Journal of Education, 12(2), 104–112. https://doi.org/10.34293/education.v12i2.7182
Jose, B., Cherian, J., Verghis, A. M., Varghise, S. M., Mumthas, S., & Joseph, S. (2025). The cognitive paradox of AI in education: Between enhancement and erosion. Frontiers in Psychology, 16, Article 1550621. https://doi.org/10.3389/fpsyg.2025.1550621
Mattar, J., Santos, C. C., & Cuque, L. M. (2022). Analysis and comparison of international digital competence frameworks for education. Education Sciences, 12(12), Article 932. https://doi.org/10.3390/educsci12120932
Ofosu-Asare, Y. (2024). Developing classroom ICT teaching techniques, principles and practice for teachers in rural Ghana without access to computers or internet: A framework based on literature review. The International Journal of Information and Learning Technology, 41(3), 297–313. https://doi.org/10.1108/IJILT-04-2023-0045
Okoye, K., Nwachukwu, N. E., Khan, M. A., Hwang, G.-J., & Alqahtani, M. A. (2023). Impact of digital technologies upon teaching and learning in higher education in Latin America: An outlook on the reach, barriers, and bottlenecks. Education and Information Technologies, 28(12), 15655–15691. https://doi.org/10.1007/s10639-022-11214-1
Rehman, N., Huang, X., Mahmood, A., AlGerafi, M. A. M., & Javed, S. (2024). Project-based learning as a catalyst for 21st-century skills and student engagement in the math classroom. Heliyon, 10(23), Article e39988. https://doi.org/10.1016/j.heliyon.2024.e39988
Samsudi, Supraptono, E., Utanto, Y., Rohman, S., & Djafar, T. (2024). Unraveling the Merdeka curriculum: Exploring differentiated instruction’s impact on student learning. Jurnal Ilmiah Peuradeun, 12(2), 681–704. https://doi.org/10.26811/peuradeun.v12i2.1131
Sayed, W. S., Alharthi, A. A., Alotaibi, R. M., Alsubaie, N. M., & Alyami, H. S. (2023). AI-based adaptive personalized content presentation and exercises navigation for an effective and engaging e-learning platform. Multimedia Tools and Applications, 82(30), 46967–46998. https://doi.org/10.1007/s11042-022-13076-8
Semenikhina, O. V., Drushlyak, M. H., & Shyshenko, I. V. (2022). STEM project as a means of learning modeling for pre-service mathematics and computer science teachers. Information Technologies and Learning Tools, 90(4), 46–56. https://doi.org/10.33407/itlt.v90i4.4946
Shyshenko, I. V., Chkana, Y. O., Martynenko, O. V., Udovychenko, O. M., Stotskyi, I. I., & Semenikhina, O. V. (2023). Implementation of a facilitative approach to teaching mathematical disciplines to future mathematics teachers by means of the MAPLE package. In 2023 46th MIPRO ICT and Electronics Convention (MIPRO) (pp. 625–630). IEEE. https://doi.org/10.23919/MIPRO57284.2023.10159878
Shyshenko, I. V., Chkana, Y. O., Martynenko, O. V., Udovychenko, O. M., Udovychenko, I. M., & Semenikhina, O. V. (2024). The use of digital technologies during the study of mathematical analysis as a basis for the development of students’ emotional intelligence in new crisis conditions. In 2024 47th MIPRO ICT and Electronics Convention (MIPRO) (pp. 346–351). IEEE. https://doi.org/10.1109/MIPRO60963.2024.10569618
Shyshenko, I. V., Chkana, Y. O., Martynenko, O. V., Udovychenko, O. M., Udovychenko, I. M., & Semenikhina, O. V. (2025). The impact of digital media on the development of students’ mathematical literacy: Service-learning opportunities. In 2025 48th MIPRO ICT and Electronics Convention (MIPRO) (pp. 709–714). IEEE. https://doi.org/10.1109/MIPRO65660.2025.11132000
Shyshenko, I. V., Martynenko, O. V., Chkana, Y. O., Spas, T. S., Udovychenko, O. M., & Semenikhina, O. V. (2022). A mathematics teacher’s training to create a maker space in mathematics lessons by means of GeoGebra. In 2022 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO) (pp. 909–914). IEEE. https://doi.org/10.23919/MIPRO55190.2022.9803690
Sun, D., Cheng, G., Yu, P., Jia, J., Zheng, Z., & Chen, A. (2025). Personalized STEM education empowered by artificial intelligence: A comprehensive review and content analysis. Interactive Learning Environments. Advance online publication. https://doi.org/10.1080/10494820.2025.2462156
Woodcock, S., Gibbs, K., Hitches, E., & Regan, C. (2023). Investigating teachers’ beliefs in inclusive education and their levels of teacher self-efficacy: Are teachers constrained in their capacity to implement inclusive teaching practices? Education Sciences, 13(3), Article 280. https://doi.org/10.3390/educsci13030280
Yurchenko, A. O., Mulesa, P. P., & Semenikhina, O. V. (2023). Individual educational trajectory building as a successful teacher skill in the digital age. Pedagogy and Education Management Review, 2, 64–72. https://doi.org/10.36690/2733-2039-2023-2-64-72
Zhang, J., & Zhang, Z. (2024). AI in teacher education: Unlocking new dimensions in teaching support, inclusive learning, and digital literacy. Journal of Computer Assisted Learning, 40(4), 1428–1445. https://doi.org/10.1111/jcal.12988
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Artem Yurchenko (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
