Keywords
Complex Thinking, Included Third
Transdisciplinarity
Chemistry
Ecology
Ethics
Generative Artificial Intelligence
Chemistry Didactics
Cognitive Friction
How to Cite
Abstract
This article presents the NEXUS-RELOAD-MIL Model, an emergent pedagogical architecture designed to reconfigure the chemistry classroom in Bogotá's public secondary education as a reflective system in response to the disruption of Generative Artificial Intelligence (GenAI). Objective: To design, implement, and evaluate the NEXUS-RELOAD-MIL Model to reconfigure chemistry classrooms in Bogotá’s public secondary schools as a reflective and critical system in response to the emergence of Generative Artificial Intelligence. Methodology: The model emerged from a doctoral study using a mixed methodology (N=100 students, 6 teachers, two public schools in Bogotá, March–October 2025), structured around the Transdisciplinary Design-Research Ecology (EIDT), which integrates Participatory Action Research (PAR) and Design-Based Research (DBR). The article outlines the model's genesis—rooted in the diagnosis of three structural classroom crises: unidisciplinary blindness, the sociotechnological equity gap, and the uncritical use of GenAI—its five-phase interdependent didactic architecture (NEXUS: Narrative, Exploration, X-LAB, Upgrade, and Sinapsis), the continuous improvement engine (RELOAD), and the transdisciplinary compass grounded in Media and Information Literacy (MIL). Results: Empirical results confirm that the model eradicates algorithmic subordination: 78% of students reject uncritical transcription of AI outputs (mean: 1.91/5.0), 90% achieve high levels of epistemic criticality, and 89% integrate chemistry, ecology, and ethics in territorial socio-environmental challenges. Conclusions: It is concluded that the NEXUS-RELOAD-MIL Model constitutes an original and transferable educational proposal.
References
Acero, C., González, M., & Santamaría, R. (2025). Journey through the MILtiverse: Toolkit for youth organizations. UNESCO. https://doi.org/10.58338/FVKH3606
Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431-440. https://doi.org/10.1007/s43681-021-00096-7
Anderson, T., & Shattuck, J. (2012). Design-based research: A decade of progress in education research? Educational Researcher, 41(1), 16-25. https://doi.org/10.3102/0013189X11428813
ATLAS.ti Scientific Software Development GmbH. (2022). ATLAS.ti 22 [Computer software]. ATLAS.ti Scientific Software Development GmbH. https://atlasti.com
Bachelard, G. (2000). La formación del espíritu científico (23.ª ed.). Siglo XXI.
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of FAccT 2021, 610-623. https://doi.org/10.1145/3442188.3445922
Blonder, R., & Feldman-Maggor, Y. (2024). Generative AI in chemistry education: Cultural biases and pedagogical implications. Journal of Chemical Education, 101(8), 3201-3210. https://doi.org/10.1021/acs.jchemed.4c00311
Brown, A. L. (1992). Design experiments: Theoretical and methodological challenges in creating complex interventions in classroom settings. Journal of the Learning Sciences, 2(2), 141-178. https://doi.org/10.1207/s15327809jls0202_2
Chen, B. (2025). Generative AI as epistemic infrastructure: Implications for human epistemic agency in education. Educational Technology Research and Development. https://doi.org/10.1007/s11423-025-10478-9
Creswell, J. W., & Clark, V. L. P. (2018). Designing and conducting mixed methods research (3.ª ed.). SAGE.
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22. https://doi.org/10.1186/s41239-023-00392-8
DeLuca, C., Bolden, B., Chan, J., Poulton, P., & Searchfield, C. (2014). Collaborative inquiry for learning in education. McGill Journal of Education, 49(3), 643-664. https://doi.org/10.7202/1029328ar
Edelson, D. C. (2002). Design research: What we learn when we engage in design. Journal of the Learning Sciences, 11(1), 105-121. https://doi.org/10.1207/S15327809JLS1101_4
Estrada, A., Agudelo, P., & Flórez, R. (2025). Inteligencia artificial en la enseñanza de la química: revisión sistemática de tendencias y desafíos. Tecné, Episteme y Didaxis, 57(1), 45-68. https://doi.org/10.17227/ted.num57-18327
Fals Borda, O. (1985). Conocimiento y poder popular: Lecciones con campesinos de Nicaragua, México y Colombia. Siglo XXI.
Fetters, M. D., Curry, L. A., & Creswell, J. W. (2013). Achieving integration in mixed methods designs: Principles and practices. Health Services Research, 48(6pt2), 2134-2156. https://doi.org/10.1111/1475-6773.12117
Fishman, B., Penuel, W. R., Allen, A. R., Cheng, B. H., & Sabelli, N. (2013). Design-based implementation research: An emerging model for transforming the relationship of research and practice. En B. J. Fishman & W. R. Penuel (Eds.), Design-based implementation research (NSSE Yearbook, Vol. 112, Parte 2, pp. 136-156). National Society for the Study of Education.
Gell-Mann, M. (1994). El quark y el jaguar: Aventuras en lo simple y lo complejo. Tusquets.
Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4), 542-570. https://doi.org/10.1111/ejed.12533
Jacobson, M. J., & Wilensky, U. (2006). Complex systems in education. Journal of the Learning Sciences, 15(1), 11-34. https://doi.org/10.1207/s15327809jls1501_4
Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14-26. https://doi.org/10.3102/0013189X033007014
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Kemmis, S., & McTaggart, R. (2014). Participatory action research. En N. K. Denzin & Y. S. Lincoln (Eds.), Handbook of qualitative research (4.ª ed., pp. 549-603). SAGE.
Kosmyna, N., Hauptmann, E., Yuan, Y. J., Liao, X.-H., Beresnitzky, A., & Braunstein, I. M. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task. ArXiv. https://arxiv.org/abs/2506.08872
Maldonado, C. E. (2024). Inteligencia artificial y ética. Universidad El Bosque.
Max-Neef, M. A. (2005). Foundations of transdisciplinarity. Ecological Economics, 53(1), 5-16. https://doi.org/10.1016/j.ecolecon.2005.01.014
McKenney, S., & Reeves, T. C. (2019). Conducting educational design research (2.ª ed.). Routledge. https://doi.org/10.4324/9781315105642
Merleau-Ponty, M. (1993). Fenomenología de la percepción. Planeta-Agostini.
Miao, F., & Cukurova, M. (2024). AI competency framework for teachers. UNESCO Publishing.
Morin, E. (1999a). Los siete saberes necesarios para la educación del futuro. UNESCO Publishing.
Morin, E. (1999b). La cabeza bien puesta: Repensar la reforma, reformar el pensamiento. Nueva Visión.
Nicolescu, B. (1994). La transdisciplinariedad: Una nueva visión del mundo. Multiversidad Mundo Real Edgar Morin.
Popper, K. (1980). La lógica de la investigación científica. Tecnos.
Salinas, J. (2023). IA y docencia universitaria en ciencias exactas: Retos y perspectivas. Perfiles Educativos, 45(180), 112-129. https://doi.org/10.22201/iisue.24486167e.2023.180.60812
Swindell, J., Greeley, S., Farag, A., & Verdone, J. (2024). Generative AI and the Promethean gap: Rethinking human epistemic agency in the age of algorithmic intelligence. AI & Society. https://doi.org/10.1007/s00146-024-01887-y
UNESCO. (2021). Recomendación sobre la ética de la inteligencia artificial. UNESCO Publishing.
UNESCO. (2024). AI competency framework for students. UNESCO Publishing.
UNESCO. (2025). Media and information literacy: Global standards for MIL practitioners. UNESCO Publishing.
Wang, F., & Hannafin, M. J. (2005). Design-based research and technology-enhanced learning environments. Educational Technology Research and Development, 53(4), 5-23. https://doi.org/10.1007/BF02504682
Yuriev, E., Wink, M., & Holme, T. A. (2024). Generative AI in chemistry education: From personalised learning to ethical pedagogy. Chemical Education Research and Practice, 25(2), 341-358. https://doi.org/10.1039/D3RP00225J.
This work is licensed under a Creative Commons Attribution 4.0 International License.