Use of Artificial Intelligence in the Learning of Newtonian Mechanics: A Didactic Intervention in First-Year Undergraduate Students
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Abstract
This article presents the preliminary effects of a didactic intervention based on the use of artificial intelligence (AI) for teaching Newtonian mechanics concepts to first-year students in the Bachelor of Physics Education program at the University of Guayaquil. This intervention corresponds to the first phase of a quasi-experimental study conducted during the May–August 2024 semester, involving two parallel groups: a control group using traditional peer instruction strategies, and an experimental group using AI-guided activities through a web-based platform supported by ChatGPT. Both groups underwent a diagnostic pre-test, which revealed similar levels of initial conceptual knowledge. Following a structured and differentiated teaching process, a post-test was applied to assess the intervention's impact. The results showed a significant improvement in the conceptual understanding of the experimental group compared to the control group. The findings suggest that integrating AI tools in the classroom can positively impact physics learning by helping students overcome alternative conceptions and fostering a deeper understanding of the fundamental principles of classical mechanics. The study highlights the feasibility of using virtual assistants as a complementary resource in science higher education environments.
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References
Amaguaya, M., & Castro, D. (2022). Dificultades en la comprensión del movimiento de proyectiles en estudiantes de Física. Universidad de Guayaquil.
Artamónova, V., Mosquera-Mosquera, C., & Mosquera-Artamónov, D. (2017). Análisis del nivel de comprensión conceptual en mecánica clásica a través del FCI en universidades latinoamericanas. Revista Latinoamericana de Física Educativa, 11(2), 45–53.
Budini, P., Etchegoyen, A., Mazza, G., & Romero, M. (2019). Estudio del rendimiento conceptual en Física I en estudiantes universitarios. Enseñanza de las Ciencias, 37(1), 135–153.
Castillo, G., Tapia, A., Placencia, L., & Pavón, C. (2024). Implementación incipiente de inteligencia artificial en la educación universitaria ecuatoriana. Revista Ecuatoriana de Tecnología Educativa, 8(1), 22–39.
Chen, Y., Chen, L., & Zhijian, L. (2020). Artificial Intelligence in Education: Applications and Challenges. International Journal of Educational Development, 77, 102249. https://doi.org/10.1016/j.ijedudev.2020.102249
Chi, M. T. H. (2005). Commonsense conceptions of emergent processes: Why some misconceptions are robust. Journal of the Learning Sciences, 14(2), 161–199.
Crouch, C. H., & Mazur, E. (2001). Peer Instruction: Ten years of experience and results. American Journal of Physics, 69(9), 970–977. https://doi.org/10.1119/1.1374249
Flores-García, R., Carrascosa-Alís, J., Dreyfus, B., Hoehn, J., Elby, A., Finkelstein, N., & Gupta, A. (2008). Concepciones alternativas sobre la fuerza y el movimiento en estudiantes universitarios. Revista de Enseñanza de la Física, 21(1), 5–20.
Gómez, D., Del Pozo, E., Martínez, L., & Martín del Campo, J. (2020). Panorama de la inteligencia artificial en la educación en América Latina. Revista Iberoamericana de Educación, 84(2), 115–138.
Hernández, R., Fernández, C., & Baptista, P. (2014). Metodología de la investigación (6.ª ed.). McGraw-Hill.
Hestenes, D., Wells, M., & Swackhamer, G. (1992). Force Concept Inventory. The Physics Teacher, 30(3), 141–158. https://doi.org/10.1119/1.2343497
Kasinathan, G., Mustapha, A., & Medi, S. (2017). Adaptive Learning Systems and Personalized Learning in Higher Education: A Case Study. International Education Studies, 10(12), 50–59. https://doi.org/10.5539/ies.v10n12p50
Mazur, E. (1997). Peer Instruction: A User’s Manual. Prentice Hall.
Moreno, S. (2019). Inteligencia artificial en la educación: personalización del aprendizaje en física. Revista de Tecnología y Didáctica, 5(2), 33–45.
Peñaherrera, P., Cunuhay, A., Nata, Y., & Moreira, D. (2022). El rol de la inteligencia artificial en la educación superior ecuatoriana. Revista Científica del Ecuador, 6(1), 77–94.
Rosales, D., & Sulaiman, T. (2020). Effects of AI-Supported Modules in the Teaching of Newtonian Mechanics. International Journal of Learning and Teaching, 12(4), 334–341.
Smith, M. K., Wood, W. B., Adams, W. K., Wieman, C., Knight, J. K., Guild, N., & Su, T. T. (2009). Why peer discussion improves student performance on in-class concept questions. Science, 323(5910), 122–124. https://doi.org/10.1126/science.1165919
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221.
Varas, M., Villalva, E., & Nieto, M. (2018). Diagnóstico del conocimiento conceptual en mecánica clásica en estudiantes de nivelación y educación superior en Ecuador. Revista de Ciencias Físicas y Aplicadas, 10(2), 123–138.
Woolf, B. P. (2010). Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning. Morgan Kaufmann.