Tendencias de innovación curricular basadas en Pensamiento Computacional e Inteligencia Artificial: un análisis documental.
Contenido principal del artículo
Resumen
El artículo analiza las tendencias de innovación curricular sustentadas en el pensamiento computacional y la inteligencia artificial en distintos niveles educativos, con el propósito de identificar sus rasgos conceptuales, metodológicos y estructurales entre 2020 y 2025. Metodológicamente, se desarrolló una investigación documental de alcance interpretativo, guiada por el protocolo PRISMA. La búsqueda se realizó en Scopus y Google Académico mediante ecuaciones booleanas relacionadas con innovación curricular, pensamiento computacional, inteligencia artificial y educación. De 94 registros iniciales, tras depuración, cribado y lectura a texto completo, se seleccionaron 35 estudios científicos. Los resultados evidencian dos grandes núcleos analíticos: el pensamiento computacional como componente de innovación curricular y la inteligencia artificial como eje de transformación curricular adaptativa. En el primer caso, predominan estudios sobre delimitación conceptual, evaluación de habilidades, formación docente e integración didáctica mediante robótica, programación, gamificación y enfoques STEAM. En el segundo, destacan investigaciones sobre personalización del aprendizaje, analítica de datos, diseño curricular adaptativo, formación docente en IA, políticas educativas y marcos convergentes entre IA y pensamiento computacional. Asimismo, se identifica una alta presencia de revisiones bibliométricas, lo que muestra un campo en expansión y cartografía científica. Se concluye que el pensamiento computacional avanza hacia una consolidación pedagógica con respaldo empírico, mientras la inteligencia artificial impulsa una reconfiguración estructural del currículo. No obstante, persisten desigualdades de acceso, vacíos de formación docente y riesgos de implementación instrumental. Por ello, la innovación curricular exige articulación entre fundamentos teóricos, desarrollo profesional docente y políticas institucionales coherentes. Esto demanda enfoques críticos, contextualizados, sostenibles y graduales.
Detalles del artículo
Sección
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
Cómo citar
Referencias
Abdul, A. S., Wong, S. L., Khambari, M. N., Abd Rahim, N. A., Khalid, F., & Moses, P. (2025). A bibliometric analysis of computational thinking skills: definition, components and assessment tools. Research & Practice in Technology Enhanced Learning, 20. https://doi.org/10.58459/rptel.2025.20035
Akrami, N. E., Hanine, M., Flores, E. S., Aray, D. G., & Ashraf, I. (2023). Unleashing the Potential of Blockchain and Machine Learning: Insights and Emerging Trends From Bibliometric Analysis. Ieee Access, 11, 78879–78903. https://doi.org/10.1109/access.2023.3298371
Ali, M., Ma, M., Muneeb, M., & Wong, G. K. (2025). Mapping Contemporary AI-Education Intersections and Developing an Integrated Convergence Framework: A Bibliometric-Driven and Inductive Content Analysis. In Metrics (Vol. 2, No. 4, p. 23). MDPI. https://doi.org/10.3390/metrics2040023
Angeli, C., & Giannakos, M. N. (2020). Computational Thinking Education: Issues and Challenges. Computers in Human Behavior, 105, 106185. https://doi.org/10.1016/j.chb.2019.106185
Angraini, L. M., Kania, N., & Gürbüz, F. (2024). Students’ Proficiency in Computational Thinking Through Constructivist Learning Theory. International Journal of Mathematics and Mathematics Education, 45–59. https://doi.org/10.56855/ijmme.v2i1.963
Angulo, G. E., & Salazar, E. (2025). Artificial Intelligence in Education and its Influence on the Development of Thinking Skills a Bibliometric Analysis. Available at SSRN 5290467. https://dx.doi.org/10.2139/ssrn.5290467
Appiah, E. D., & Antwi, S. (2025). Research Trends on Computational Thinking in Pre-Service Teachers' Education: A Bibliometric Analysis. TechTrends, 1-12. https://doi.org/10.1007/s11528-025-01111-y
Do Breviário, Á. G. (2025). Artificial intelligence and big data in science education: computational modeling and data analysis to promote scientific thinking in students. INTERSEÇÕES, 106. https://www.researchgate.net/profile/Alaze-Breviario-2/publication/394396307_Intersecoes_-_Volume_2/links/689560e1d3c4ac316e2eee88/Intersecoes-Volume-2.pdf#page=106
Eliska, Suparta, I. N., Ardana, I. M., & Mahayukti, G. A. (2025). Mapping the Integration of Computational Thinking in Mathematics Education: A Scopus-Based Bibliometric Analysis (2015–2025). Media Pendidikan Matematika, 13(2), 746–758. https://doi.org/10.33394/mpm.v13i2.17471
González-, F., López, C., Vásquez, A., & Castro, C. (2024). Inequalities in Computational Thinking Among Incoming Students in an STEM Chilean University. Ieee Transactions on Education, 67(2), 180–189. https://doi.org/10.1109/te.2023.3334193
Hangün, M. E., & Türel, Y. K. (2025). The effects of robot programming on mathematical achievement, mathematics anxiety, and programming self‐efficacy. Computer Applications in Engineering Education, 33(3). https://doi.org/10.1002/cae.70030
Hira, F. A., Rasid, S. Z. A., Khalid, H., Moshiul, A. M., Daud, S. M., Abas, H., Sam, S. M., & Yusof, M. F. (2020). Mapping research trends of blockchain technology in healthcare. IEEE Access, 8, 174244–174254. https://doi.org/10.1109/access.2020.3025011
Husaeni, A., Fitria, D., Abdullah, A. G., Septem Riza, L., Suherman, A., Husaeni, A., & Novia, D. (2025). Trends and impacts of artificial intelligence application in the development of computational thinking skills. Informatics in Education, 24(2), 261-298. https://www.ceeol.com/search/article-detail?id=1372821
Juškevičienė, A., Dagienė, V., & Dolgopolovas, V. (2020). Integrated activities in STEM environment: Methodology and implementation practice. Computer Applications in Engineering Education, 29(1), 209–228. https://doi.org/10.1002/cae.22324
Juškevičienė, A., Stupurienė, G., & Jevsikova, T. (2020). Computational thinking development through physical computing activities in STEAM education. Computer Applications in Engineering Education, 29(1), 175–190. https://doi.org/10.1002/cae.22365
Karatas, F., Eriçok, B., & Tanrikulu, L. (2025). Reshaping curriculum adaptation in the age of artificial intelligence: Mapping teachers’ AI-driven curriculum adaptation patterns. British Educational Research Journal, 51(1), 154–180.
Khalili, S., & Breyer, C. (2022). Review on 100% renewable energy system analyses—A bibliometric perspective. IEEE Access, 10, 125792–125834. https://doi.org/10.1109/access.2022.3221155
Kraishan, O. M. (2023). Features of applying artificial intelligence in the eighth grade science curriculum in the Sultanate of Oman. Journal of Higher Education Theory and Practice, 23(9), 143–163. https://doi.org/10.33423/jhetp.v23i9.6140
Lee, J., & Cho, J. (2024). Artificial intelligence curriculum development for intelligent system experts in university. International Journal on Advanced Science, Engineering and Information Technology, 14(2), 409–419. https://doi.org/10.18517/ijaseit.14.2.18860
Liu, S., Peng, C., & Srivastava, G. (2023). What influences computational thinking? A theoretical and empirical study based on the influence of learning engagement on computational thinking in higher education. Computer Applications in Engineering Education, 31(6), 1690–1704. https://doi.org/10.1002/cae.22669
Liu. (2024). Assessing implicit computational thinking in game‐based learning: A logical puzzle game study. British Journal of Educational Technology, 55(5), 2357–2382. https://doi.org/10.1111/bjet.13443
Liu, J., Ma, J., & Li, S. (2025). Research on school-based AI curriculum design and practice for cultivating computational thinking in high school students. Education and Information Technologies, 30(6), 7949-7993. https://doi.org/10.1007/s10639-024-13115-x
Mee, R., Che, F., Pek, L. S., Abd Rauf, M. F., Mingmei, Y., & Derahvasht, A. (2025). Building digital thinkers: A bibliometric analysis of computational thinking in children’s education for a sustainable future. Contemporary Educational Technology, 17(3), ep581. https://doi.org/10.30935/cedtech/16309
Mohamed, M. S., & Nik, N. (2025). Artificial Intelligence in Curriculum Development: A Global Systematic Review of Trends, Challenges, and Strategic Directions. Journal Of Curriculum Studies Research, 7(2), 466-497. https://doi.org/10.46303/jcsr.2025.30
Nattawuttisit, S., & Maneerat, P. (2024). AI-driven adaptive curriculum development: Enhancing student learning outcomes aligned with the Thai qualifications framework in higher education. Journal of Theoretical and Applied Information Technology, 102(17), 6512–6520.
Piedad, E. J. (2024). Strengthening artificial intelligence-on-edge education in the Philippines: A teacher-centric curriculum development strategies. 2024 IEEE 13th International Conference on Engineering Education (ICEED), 1–6. https://doi.org/10.1109/ICEED62316.2024.10923809
Rijo, S., Segredo, E., & León, C. (2022). Computational thinking and user interfaces: A systematic review. IEEE Transactions on Education, 65(4), 647–656. https://doi.org/10.1109/te.2022.3159765
Sim, L., Katman, H. Y. B., Baharuddin, I. N. Z., Gobinath, R., Ibrahim, M. R., & Alnadish, A. M. (2024). Global research trends in soft soil management for infrastructure development: Opportunities and challenges. IEEE Access, 12, 73731–73751. https://doi.org/10.1109/access.2024.3403720
Susanto, E., Sugiyanti, W., Arifin, M., Fathonah, S., & Nurkolis, N. (2025). Enacting Coding and Artificial Intelligence Education Policy in Elementary Schools. Journal of Innovation and Research in Primary Education, 5(1), 1616–1628. https://doi.org/10.56916/jirpe.v5i1.3135
Tedre, M., Toivonen, T., Kahila, J., Vartiainen, H., Valtonen, T., Jormanainen, I., & Pears, A. (2021). Teaching machine learning in K–12 classroom: Pedagogical and technological trajectories for artificial intelligence education. IEEE Access, 9, 110558–110572. https://doi.org/10.1109/access.2021.3097962
Yadav, S., & Chakraborty, P. (2023). Introducing schoolchildren to computational thinking using smartphone apps: A way to encourage enrollment in engineering education. Computer Applications in Engineering Education, 31(4), 831–849. https://doi.org/10.1002/cae.22609
Yolcu, V., & Demirer, V. (2023). The effects of educational robotics in programming education on students’ programming success, computational thinking, and transfer of learning. Computer Applications in Engineering Education, 31(6), 1633–1647. https://doi.org/10.1002/cae.22664
Zafrullah, Z., Gasuko, G. V., Oktarina, A. D., & Arriza, L. (2025). Tracing 21st-Century Trends of Computational Thinking in Educational Research in Indonesia. Journal of Technological Pedagogy and Educational Development, 2(1), 1-9. https://doi.org/10.59247/jtped.v2i1.4