Design and Development of an AI Chatbot-Assisted Learning Module on Magnetic Fields for Alternative Delivery Mode (ADM) in Education in Emergencies (EiE)
DOI:
https://doi.org/10.5281/zenodo.20431692Keywords:
AI chatbot, alternative delivery mode, magnetic fields, physics motivation, self-regulated learning, cognitive load theory, education in emergenciesAbstract
Due to its conceptual complexity, senior high school students find learning abstract physics concepts, such as magnetic fields, challenging adding the frequent instructional disruptions. To maintain instructional continuity, the Department of Education widely used the Alternative Delivery Mode (ADM) modules, but existing materials often lack interactivity and scaffolding, limiting engagement and learning outcomes. With this, the present study examined the effectiveness of an AI Chatbot-Assisted Learning Module on Magnetic Fields for ADM in Education in Emergencies (EiE), focusing on students’ physics performance, motivation, and engagement. Using an explanatory sequential mixed-methods design, data were collected from 10 Grade 12 STEM students through AI-guided learning activities and the Physics Motivation Questionnaire II. Quantitative results showed high student performance across all criteria with an average score of 66.2/80, marked as Excellent and generally positive motivation (M = 3.00, SD = 0.87). Correlation analysis indicated a weak, non-significant relationship between performance and motivation (r = 0.277, p = 0.529). Qualitative analysis revealed that AI guidance reduced cognitive load, provided step-by-step explanations and relatable examples, and adopted self-regulated learning strategies such as planning, monitoring, and reflection. These findings suggest that AI-integrated ADM modules can enhance conceptual understanding, self-regulation, and engagement in physics, offering a learner-centered, and interactive instructional approach. The study provides practical support for integrating AI chatbots into secondary science education to improve both learning outcomes and motivation.
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Abadiano, M. N., & Turner, J. J. (2021). Students’ experiences with self-learning modules in distance education. Asian Journal of Education and Social Studies, 19(4), 1–10. https://www.journalajess.com/index.php/AJESS/article/view/30462
Becker, E., Wünsche, J., Veith, J. M., Schrader, J., & Bitzenbauer, P. (2025). From cognitive relief to affective engagement: An empirical comparison of AI chatbots and instructional scaffolding in physics education. arXiv. https://arxiv.org/abs/2508.06254
Centre for Education Statistics and Evaluation. (2017). Cognitive load theory in practice: Examples for the classroom. NSW Department of Education. https://education.nsw.gov.au/content/dam/main-education/about-us/educational-data/cese/2017-cognitive-load-theory-practice-guide.pdf
Dantic, M. J. P., & Fularon, A. R. (2022). PhET interactive simulation approach in teaching electricity and magnetism among science teacher education students. Journal of Science and Education, 2(2), 88–98. https://doi.org/10.56003/jse.v2i2.101
Dangle, Y. R. P., & Sumaoang, J. D. (2020). The implementation of modular distance learning in Philippine secondary public schools. International Journal of Pedagogical Development and Lifelong Learning, 1(1), Article ep2007. https://doi.org/10.30935/ijpdll/9611
Department of Education. (2020). Adoption of the basic education learning continuity plan for school year 2020–2021 (DepEd Order No. 012, s. 2020). https://www.deped.gov.ph/wp-content/uploads/2020/06/DO_s2020_012.pdf
DepEd e-Saliksik. (2022). Utilization of the self-learning modules (SLMs) as assessed by master teachers. Department of Education. https://e-saliksik.deped.gov.ph/
Fonseca, J. A., & Costa, M. F. (2023). Challenges in learning physics in high school public schools: A literature review. Research, Society and Development, 12(6), Article e42440642440. https://doi.org/10.33448/rsd-v12i6.42440
Glynn, S. M., Brickman, P., Armstrong, N., & Taasoobshirazi, G. (2020). Science motivation questionnaire II: Validation with science majors and nonscience majors. Journal of Research in Science Teaching, 56(2), 237–268. https://doi.org/10.1002/tea.21442
Hernández, E., Campos, E., Barniol, P., & Zavala, G. (2025). Students’ understanding of electric flux and magnetic circulation and the role of the superposition principle in Gauss’s and Ampère’s laws. Physical Review Physics Education Research, 21(1), Article 010120. https://doi.org/10.1103/PhysRevPhysEducRes.21.010120
Hidayah, N., Talakua, P., Abdul Azis, D., & Maipauw, M. (2024). Development of interactive learning media based on augmented simulation using PhET for magnetic field material. Jurnal Pendidikan dan Ilmu Fisika, 5(1), 12–25. https://doi.org/10.52434/jpif.v5i1.42588
Jiang, Z., & Jiang, M. (2024). Beyond answers: Large language model powered tutoring system in physics education for deep learning and precise understanding. arXiv. https://arxiv.org/abs/2406.10934
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for education: Opportunities and challenges. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274
Katz, Y. J., Tepper, R., & Kim, J. (2024). Artificial intelligence tutors and student learning outcomes: A meta-analysis. Computers & Education, 195, Article 104693. https://doi.org/10.1016/j.compedu.2023.104693
Maries, A., Brundage, M. J., & Singh, C. (2023). Using the conceptual survey of electricity and magnetism to investigate progression in student understanding from introductory to advanced levels. arXiv. https://arxiv.org/abs/2311.17170
Okonkwo, C. W., & Ade-Ibijola, A. (2021). Chatbots applications in education: A systematic review. Computers and Education: Artificial Intelligence, 2, Article 100033. https://doi.org/10.1016/j.caeai.2021.100033
Rafon, J. E. (2023). Self-regulated interactive learning modules in physics: Improving students’ problem-solving skills (Doctoral dissertation, De La Salle University). Animo Repository. https://animorepository.dlsu.edu.ph/etdd_scied/29/
Robinos, R. (2024). Teacher and student perspectives on AI integration in Philippine education. Journal of Innovative Teaching and Learning, 3(1), 45–62.
Rodriguez, V. R., Dueñas, Z. D., & Collado, Z. C. (2024). Perceived effectiveness of self-learning modules in modular distance learning. Journal of Learning and Development Studies, 4(2), 115–128.
Schunk, D. H., & DiBenedetto, M. K. (2020). Motivation and social cognitive theory. Contemporary Educational Psychology, 60, Article 101832. https://doi.org/10.1016/j.cedpsych.2019.101832
Suba, M. A., & Manlapig, E. F., Jr. (2025). Development and evaluation of experiential learning with digital simulation (ELDS) modules in electricity & magnetism. Journal of Research in Education and Pedagogy, 2(2), 296–308. https://doi.org/10.70232/jrep.v2i2.39
Thomann, J., et al. (2025). Scaffolding through prompts in digital learning: A review. Educational Technology Research Journal, 8(1), 12–38.
Tong, T., Pi, F., Zheng, S., Zhang, Y., & Wang, J. (2025). Exploring the effect of mathematics skills on student performance in physics problem-solving: A structural equation modeling analysis. Research in Science Education, 55(3), 489–509. https://doi.org/10.1007/s11165-024-10201-5
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2
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