Overview of adaptive learning in STEM higher education: A techno-pedagogical analysis of experiences, challenges, and opportunities for implementation
Abstract
Following the pandemic, there has been a marked increase in the use of digital adaptive learning platforms, particularly in higher education. Within the STEM (Science, Technology, Engineering and Mathematics) disciplines, the adoption of adaptive learning systems has demonstrated notable improvements in conceptual understanding and student autonomy, although their integration encounters both pedagogical and technological challenges. This study aims to systematically analyze documented experiences of implementing adaptive learning in STEM higher education, identifying the primary technical and pedagogical obstacles as well as opportunities for improvement, thereby enabling the proposal of a technopedagogical maturity model. The research was conducted using a systematic review methodology guided by the PRISMA model. Results indicate that adaptive learning significantly enhances academic performance and student engagement, reduces dropout rates, and promotes active participation, with the most pronounced effects observed in fields such as mathematics, programming, and chemistry. Furthermore, several technical and pedagogical challenges were identified, primarily related to curriculum design, self-regulation, and student motivation. In conclusion, the implementation of adaptive learning in STEM demonstrates strong potential; however, the impact of these innovations is fully realized only when institutional planning coordinates technical, training, and educational management aspects. Additional limitations were noted, including resistance to change, stress associated with intensive platform use, and infrastructure gaps, which constrain the generalizability of the findings.
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References
Chan Arceo, C., y Canto Herrera, P. J. (2022). Concepto y términos relacionados con el desarrollo profesional docente: una revisión sistemática. Revista de Educación, 0(25.1), 231-250. https://fh.mdp.edu.ar/revistas/index.php/r_educ/article/view/5843
Clark, R. M., Kaw, A. K., y Braga Gomes, R. (2022). Adaptive learning: Helpful to the flipped classroom in the online environment of COVID? Computer Applications in Engineering Education, 30(2), 517–531. https://doi.org/10.1002/CAE.22470
Contrino, M. F., Reyes-Millán, M., Vázquez-Villegas, P., y Membrillo-Hernández, J. (2024). Using an adaptive learning tool to improve student performance and satisfaction in online and face-to-face education for a more personalized approach. Smart Learning Environments, 11(1), 1–24. https://doi.org/10.1186/S40561-024-00292-Y
Díaz, B., y Aizman, A. (2024). Design and impact of a stoichiometry voluntary online course for entering first-year STEM college students. Chemistry Education Research and Practice, 25(1), 11–24. https://doi.org/10.1039/D3RP00179B
Fischer, H. A., Preston, K., Staus, N., y Storksdieck, M. (2022). Course assessment for skill transfer: A framework for evaluating skill transfer in online courses. Frontiers in Education, 7, 960430. https://doi.org/10.3389/FEDUC.2022.960430
Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., y Koedinger, K. R. (2022). Ethics of AI in Education: Towards a Community-Wide Framework. International Journal of Artificial Intelligence in Education, 32(3), 504–526. https://doi.org/10.1007/S40593-021-00239-1
Johnson, L., Adams, S., Cummins, M., y Estrada, V. (2012). Technology Outlook for STEM+ Education 2012-2017: An NMC Horizon Report Sector Analysis. Austin, Texas: The New Media Consortium.
OCDE. (2019). Estrategia de Competencias de la OCDE 2019: Competencias para construir un futuro mejor, OECD Publishing, Paris/Fundación Santillana, Madrid. https://doi.org/10.1787/e3527cfb-es.
Parra Rojas, B. A. (2023). Metodología de aprendizaje adaptativo en el área de las matemáticas. Revista Docencia Universitaria, 24(2), 31–57. https://doi.org/10.18273/REVDU.V24N2-2023003
Pilotti, M. A. E., Abdelsalam, H., Anjum, F., Muhi, I., Nasir, S., Daqqa, I., Gunderson, G. D., y Latif, R. M. (2022). Adaptive Individual Differences in Math Courses. Sustainability 2022, 14(13), 8197. https://doi.org/10.3390/SU14138197
Prada Segura, J. A., y Beltrán Gómez, A. (2024). Aprendizaje Adaptativo para Moodle desde la IA. Ciencia Latina Revista Científica Multidisciplinar, 8(5), 14173–14194. https://doi.org/10.37811/CL_RCM.V8I5.15241
Ramírez, M. H., y León, F. P. (2023). M-learning como herramienta para el aprendizaje adaptativo: Una propuesta para la educación superior. HUMAN REVIEW. International Humanities Review / Revista Internacional De Humanidades, 17(5), 1–14. https://doi.org/10.37819/REVHUMAN.V17I5.1591
Rodríguez Aroca, W. G. (2024). Aprendizaje Adaptativo en Educación Superior: Análisis de Plataformas Digitales y su Impacto en el Aprendizaje Personalizado. Ciencia Latina: Revista Multidisciplinar, ISSN-e 2707-2215, ISSN 2707-2207, 8(5), 6599–6607. https://doi.org/10.37811/cl_rcm.v8i5.14079
Sánchez-Serrano, S., Pedraza-Navarro, I., y Donoso-González, M. (2022). How to conduct a systematic review under PRISMA protocol? Uses and fundamental strategies for its application in the educational field through a practical case study. Bordon. Revista de Pedagogia, 74(3), 51–66. https://doi.org/10.13042/Bordon.2022.95090
Simó, V. L., Lagarón, D. C., y Rodríguez, C. S. (2020). Educación STEM en y para el mundo digital: El papel de las herramientas digitales en el desempeño de prácticas científicas, ingenieriles y matemáticas. Revista de Educación a Distancia (RED), 20(62), 31–34. https://doi.org/10.6018/RED.410011
Sockalingam, N., Lo, K., Teo, J., Wei, C. C., Jiet, D. C. J., Herremans, D., Jun, M. L. M., Kurniawan, O., Wang, Y., y Leong, P. K. (2025). Towards the future of education: cyber-physical learning. Discover Education, 4(1), 1–16. https://doi.org/10.1007/S44217-025-00474-X/FIGURES/7
Sung, G., Guillain, · Léonore, Schneider, · Bertrand, Guillain, L., y Schneider, B. (2025). Using AI to Care: Lessons Learned from Leveraging Generative AI for Personalized Affective-Motivational Feedback. International Journal of Artificial Intelligence in Education 2025, 1–40. https://doi.org/10.1007/S40593-024-00455-5
Tang, D., y Odeleye, O. (2023). Students’ Perceptions on the Impact of Online Homework Systems on Their Performance in a General Chemistry Course. Journal of Science Education and Technology, 32(5), 710–721. https://doi.org/10.1007/S10956-023-10061-0/TABLES/6
Organización de las Naciones Unidas para la Educación, la Ciencia y la Cultura [Unesco]. (2023). Qué necesita saber acerca del aprendizaje digital y la transformación. https://www.unesco.org/es/digital-education/need-know
Vyas, V. S., Kemp, B., y Reid, S. A. (2021). Zeroing in on the best early-course metrics to identify at-risk students in general chemistry: an adaptive learning pre-assessment vs. traditional diagnostic exam. International Journal of Science Education, 43(4), 552–569. https://doi.org/10.1080/09500693.2021.1874071;SUBPAGE:STRING:FULL
Wu, T. T., Lee, H. Y., Wang, W. S., Lin, C. J., y Huang, Y. M. (2023). Leveraging computer vision for adaptive learning in STEM education: effect of engagement and self-efficacy. International Journal of Educational Technology in Higher Education, 20(1), 1–26. https://doi.org/10.1186/S41239-023-00422-5/METRICS
Xia, X., y Qi, W. (2024). The construction of knowledge graphs based on associated STEM concepts in MOOCs and its guidance for sustainable learning behaviors. Education and Information Technologies, 29(15), 20757–20794. https://doi.org/10.1007/S10639-024-12653-8/FIGURES/6
Yan, H., Lin, F., y Kinshuk. (2024). Adaptive Practicing Design to Facilitate Self-Regulated Learning. Canadian Journal of Learning and Technology, 50(3), 1–22. https://doi.org/10.21432/CJLT28768
Zairon, I. Y., Wook, T. S. M. T., Salleh, S. M., y Dahlan, H. A. (2025). User Model for Virtual Learning based on Adaptive Gamification. IEEE Access. https://doi.org/10.1109/ACCESS.2025.3537599
Zawacki-Richter, O., Marín, V. I., Bond, M., y Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/S41239-019-0171-0/TABLES/7
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