Integrating Generative AI and Digital Platforms in Hybrid Learning: A POE-Based Instructional Case Study for Pre-Service Science Teachers
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Abstract
This article presents the AI–Tech Integrated Instructional Pipeline, a five-phase model integrating Generative AI and digital platforms into hybrid science instruction. A practice-based case study involving ten pre-service science teachers (40% online, 60% on-site) was conducted over a 120-minute POE-based activity using a candle-and-inverted-glass experiment. Data from Padlet posts and reflections were analyzed thematically. Students' reflections suggest that experiential engagement deepened their understanding of hybrid learning as a matter of instructional design. Students also perceived that the shared digital platform reduced participatory inequity between online and on-site learners, although infrastructure instability remains a challenge. Practical implications for instructors and teacher development programs are discussed.
Keywords: Generative AI, Hybrid Learning, POE Instructional Approach, Pre-Service Science Teachers
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Bond, M., Bedenlier, S., Marín, V. I., & Händel, M. (2021). Emergency remote teaching in higher education: Mapping the first global online semester. International Journal of Educational Technology in Higher Education, 18, Article 50. https://doi.org/10.1186/s41239-021-00282-x
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Chiu, T. K. F. (2024). The impact of generative AI (GenAI) on practices, policies and research direction in education: A case of ChatGPT and Midjourney. Interactive Learning Environments, 32(10), 6187–6203. https://doi.org/10.1080/10494820.2023.2253861
Crompton, H., & Burke, D. (2024). The educational affordances and challenges of ChatGPT: State of the field. TechTrends, 68, 380–392. https://doi.org/10.1007/s11528-024-00939-0
Deci, E. L., & Ryan, R. M. (1985). Intrinsic Motivation and Self-Determination in Human Behavior. New York: Plenum Press. https://doi.org/10.1007/978-1-4899-2271-7
Dick, W., Carey, L., & Carey, J. O. (2015). The Systematic Design of Instruction (8th ed.). Upper Saddle River, NJ: Pearson.
Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7(2), 95–105. https://doi.org/10.1016/j.iheduc.2004.02.001
Graham, C. R. (2006). Blended learning systems: Definition, current trends, and future directions. In C. J. Bonk & C. R. Graham (Eds.), The handbook of blended learning (pp. 3–21). Pfeiffer.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Imran, R., Fatima, A., Salem, I. E. B., & Allil, K. (2023). Teaching and learning delivery modes in higher education: Looking back to move forward post-COVID-19 era. The International Journal of Management Education, 21(2), Article 100805. https://doi.org/10.1016/j.ijme.2023.100805
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., ... Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274
Koh, J. H. L., & Daniel, B. K. (2022). Shifting online during COVID-19: A systematic review of teaching and learning strategies and their outcomes. International Journal of Educational Technology in Higher Education, 19, Article 56. https://doi.org/10.1186/s41239-022-00361-7
Kolb, D. A. (1984). Experiential Learning: Experience as the Source of Learning and Development. Englewood Cliffs, NJ: Prentice Hall.
Kornpitack, P., & Sawmong, S. (2022). Empirical analysis of factors influencing student satisfaction with online learning systems during the COVID-19 pandemic in Thailand. Heliyon, 8(3), Article e09183. https://doi.org/10.1016/j.heliyon.2022.e09183
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry. Sage Publications.
Means, B., Toyama, Y., Murphy, R., & Baki, M. (2013). The effectiveness of online and blended learning: A meta-analysis of the empirical literature. Teachers College Record, 115(3), 1–47. https://doi.org/10.1177/016146811311500307
Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000386693
Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. https://doi.org/10.1111/j.1467-9620.2006.00684.x
Ogunleye, B., Zakariyyah, K. I., Ajao, O., Olayinka, O., & Sharma, H. (2024). A systematic review of generative AI for teaching and learning practice. Education Sciences, 14(6), 636. https://doi.org/10.3390/educsci14060636
Piaget, J. (1952). The origins of intelligence in children. (M. Cook, Trans.). W. W. Norton & Company. https://doi.org/10.1037/11494-000
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
White, R., & Gunstone, R. (1992). Probing Understanding (1st ed.). Routledge. https://doi.org/10.4324/9780203761342
Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). SAGE Publications.
Zhang, X., Zhang, P., Shen, Y., Liu, M., Wang, Q., Gašević, D., & Fan, Y. (2024). A systematic literature review of empirical research on applying generative artificial intelligence in education. Frontiers of Digital Education, 1(3), 223–245. https://doi.org/10.1007/s44366-024-0028-5
