STEM Education and AI Applications in Chemistry Teaching: From Pedagogical Ecosystems to the Self-Efficacy of Secondary School Teachers

Cao Thi Van Giang¹ and Cao Cu Giac^2,

¹Chemistry Department, Hanoi National University of Education, Vietnam

²Chemistry Department, Vinh University, Vietnam

Cite this paper:
Cao Thi Van Giang and Cao Cu Giac. STEM Education and AI Applications in Chemistry Teaching: From Pedagogical Ecosystems to the Self-Efficacy of Secondary School Teachers. World Journal of Chemical Education. 2026; 14(3):51-57. doi: 10.12691/wjce-14-3-3

Abstract

This study explores the integration of STEM education and artificial intelligence (AI) in chemistry teaching to foster a sustainable pedagogical ecosystem. Current secondary school practices reveal three critical operational gaps: a deficiency in interdisciplinary integration methodologies at the lower secondary level, intense high-stakes national examination pressures driving technological risk aversion at the upper secondary level, and pervasive administrative formalism in teacher professional development. To address these challenges, the research combines conceptual framework construction with an exemplary case study of a chemistry STEM project entitled "Chemical Fertilizers and Smart Agriculture". This project is structured across three progressive technical layers: direct soil pH and nutrient analysis using IoT sensors in field environments (Layer 1); the integration of large language models (such as ChatGPT) as personalized learning co-pilots alongside virtual laboratories to optimize learning trajectories (Layer 2); and the process-oriented evaluation of 21st-century competencies via a Digital Portfolio platform (Layer 3). Based on these empirical findings, the study proposes a bipolar set of breakthrough solutions: activating teachers' internal "self-efficacy" through digital self-directed learning, and reforming the "transformative mission" of teacher education institutions through the development of smart campus laboratories and professional learning networks. Finally, the study recommends institutionalizing Digital Portfolios parallel to traditional academic transcripts to alleviate high-stakes examination pressures and establish a genuine symbiotic connection between teacher education institutions and secondary schools in the AI era.

Keywords:
STEM education artificial intelligence (AI) chemistry education teacher self-efficacy digital portfolio

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	Khan, M., Ahmed, A., & Sharif, M. M. (2026). AI infused business model innovation for competitive advantage in the era of big data and digital transformation. Open Access Journal of Data Science and Artificial Intelligence, 4(1), 1-21.
[2]	Bhutoria, A. (2022). Personalized education and artificial intelligence in the United States, China, and India: A systematic review using a human-in-The-Loop model. Computers and Education: Artificial Intelligence, 3, 100068.
[3]	Schwartz, R. S., Lederman, N. G., & Crawford, B. A. (2004). Developing views of nature of science in an authentic context: An explicit approach to bridging the gap between nature of science and scientific inquiry. Science Education, 88(4), 610-645.
[4]	Costa, M. C., Domingos, A. M., Teodoro, V. D., & Vinhas, É. M. (2022). Teacher professional development in STEM education: An integrated approach with real-world scenarios in Portugal. Mathematics, 10(21), 3944.
[5]	Thi Van Giang, C., Thi Thu Hiep, L., & Cu Giac, C. (2026). A five-step framework for designing augmented reality laboratories in pre-service chemistry teacher education: A case study on essential oil extraction. World Journal of Chemical Education, 14(2), 26-35.
[6]	Stillman, J. (2011). Teacher learning in an era of high-stakes accountability: Productive tension and critical professional practice. Teachers College Record: The Voice of Scholarship in Education, 113(1), 133-180.
[7]	Anderson, J., & Tully, D. (2020). Designing and evaluating an integrated STEM professional development program for secondary and primary school teachers in Australia. Advances in STEM Education, 403-425.
[8]	Mete, P. (2021). Structural relationships between coping strategies, self-efficacy, and fear of losing one’s self-esteem in science class. International Journal of Technology in Education and Science, 5(3), 375-393.
[9]	Kurhak, V. G., & Karbivska, U. (2025). Productivity of sowed cereal grass depends on the doses and ratio of nitrogen, phosphorus and potassium fertilizers. Agriculture and plant sciences: theory and practice, (2), 72-81.
[10]	García Ramos, J., De Souza Júnior, R. S., & Borges, E. M. (2025). How digital images are transforming chemical education: A review of laboratory-based applications. ACS Omega, 10(30), 32651-32672.
[11]	Bennett, D., Rowley, J., Dunbar-Hall, P., Hitchcock, M., & Blom, D. (2014). Electronic portfolios and learner identity: An ePortfolio case study in music and writing. Journal of Further and Higher Education, 40(1), 107-124.
[12]	Giac, C. C., Hang, N. T., & Giang, C. T. (2025). STEM education in natural science teaching to secondary school students: Case study of making a pH measuring pen in soil application of IoT technology. Journal of Chemical Education, 102(4), 1518-1528.
[13]	Kamduri, V. R., Gupta, P., & El Kari, C. (2026). AgriGen: A prompt-tuned, multilingual LLM-based Q&A system for smarter agriculture. Lecture Notes in Computer Science, 190-200.
[14]	Branchetti, L., Nipyrakis, A., Satanassi, S., Bitsaki, C., Pipitone, C., Stavrou, D., & Levrini, O. (2026). Theoretical underpinnings and empirical findings for designing interdisciplinary boundary zones for teacher education. Science & Education.
[15]	Giac, C. C., Giang, C. T., Hoang, L. H., & Ngan, T. T. (2025). A study on teachers' acceptance of digital technology in Vietnamese secondary education: An assessment using the technology acceptance model. International Journal of Learning, Teaching and Educational Research, 24(2), 38-62.
[16]	Cao, G. C., & Le, H. T. (2024). Organizing activities for chemistry pedagogy students to research and practice extracting cajeput essential oils from Melaleuca leaves using the CDIO approach. Vietnam Journal of Education, 121-137.
[17]	Laurillard, D. (2008). The teacher as action researcher: Using technology to capture pedagogic form. Studies in Higher Education, 33(2), 139-154.
[18]	Magaji, A., & Ade-Ojo, G. (2023). Trainee teachers’ classroom assessment practices: Towards evaluating trainee teachers’ learning experience in a teacher education programme.
[19]	Ilmudeen, A. (2022). Artificial intelligence, big data analytics and big data processing for IoT-based sensing data. Transforming Management with AI, Big-Data, and IoT, 247-259.
[20]	Ahn, H., & Kim, H. (2025). Impact of teaching practicum experiences through teaching portfolios on the agency of pre-service science teachers. Brain, Digital, & Learning, 15(2), 191-203.
[21]	Lam, R., & Moorhouse, B. L. (2022). Digital portfolios for assessment. Using Digital Portfolios to Develop Students’ Writing, 37-53.