Curriculum innovation trends based on Computational Thinking and Artificial Intelligence: a documentary analysis.

Main Article Content

Doris Cáceres Suárez

Abstract

This article analyzes curricular innovation trends based on computational thinking and artificial intelligence at different educational levels, with the aim of identifying their conceptual, methodological, and structural features between 2020 and 2025. Methodologically, a documentary research study with an interpretive scope was conducted, guided by the PRISMA protocol. The search was carried out in Scopus and Google Scholar using Boolean equations related to curricular innovation, computational thinking, artificial intelligence, and education. From an initial 94 records, after cleaning, screening, and full-text reading, 35 scientific studies were selected. The results reveal two main analytical areas: computational thinking as a component of curricular innovation and artificial intelligence as a driving force for adaptive curricular transformation. In the first case, studies on conceptual delimitation, skills assessment, teacher training, and didactic integration through robotics, programming, gamification, and STEAM approaches predominate. The second section highlights research on personalized learning, data analytics, adaptive curriculum design, AI teacher training, educational policies, and convergent frameworks between AI and computational thinking. It also identifies a high presence of bibliometric reviews, indicating an expanding field and ongoing scientific mapping. The conclusion is that computational thinking is moving toward pedagogical consolidation with empirical support, while artificial intelligence is driving a structural reconfiguration of the curriculum. However, inequalities in access, gaps in teacher training, and risks of instrumental implementation persist. Therefore, curricular innovation requires articulation between theoretical foundations, teacher professional development, and coherent institutional policies. This demands critical, contextualized, sustainable, and gradual approaches. 

Downloads

Download data is not yet available.

Article Details

Section

Artículo de investigación

How to Cite

Cáceres Suárez, D. (2026). Curriculum innovation trends based on Computational Thinking and Artificial Intelligence: a documentary analysis. Mundo Sostenible, 2(1), 39-65. https://doi.org/10.66707/7vvy1y43

References

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