Inteligencia híbrida y agencia educativa: hacia una ecología de colaboración humano-IA

Resumen

La llegada de la inteligencia artificial generativa ha reabierto una discusión que lleva tiempo viva en la tecnología educativa: ¿acabarán las máquinas sustituyendo al profesorado o se limitarán a apoyarlo? La evidencia no respalda ninguna de esas dos posiciones. En este artículo se plantea la inteligencia híbrida como el encuadre más útil para pensar la inteligencia artificial (IA) en la educación: una cooperación simbiótica entre personas e IA en la que la agencia, la decisión y la responsabilidad se reparten en vez de quedar en manos de una sola parte. Desde una perspectiva teórica y argumentativa, el texto acota el concepto y lo separa de ideas afines como el blended learning o la IA educativa, al tiempo que sigue sus fundamentos filosóficos en la tesis de la mente extendida, la simbiosis hombre-computadora y la teoría del actor-red. A continuación, examina tres marcos conceptuales de referencia (el modelo de seis niveles de automatización de Molenaar, la taxonomía AIED-HCD de Cukurova y el marco de adaptatividad híbrida de Holstein, Aleven y Rummel) y los articula en una arquitectura de cinco niveles para los ecosistemas de aprendizaje híbridos: personas y roles, espacio híbrido, datos, modelos y gobernanza. Además, presenta tres escenarios para incorporar progresivamente la IA en la docencia universitaria, junto con los nueve desafíos persistentes: los “elefantes en la habitación”. La discusión pone de relieve las convergencias entre estos marcos y las competencias docentes que la inteligencia híbrida demanda. El artículo concluye que la inteligencia híbrida solo puede materializar su promesa en la educación si adopta un enfoque centrado en el ser humano: la tecnología debe ajustarse a la educación, y no al contrario.
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Achuthan, K. (2025). Artificial intelligence and learner autonomy: a meta-analysis of self-regulated and self-directed learning. Frontiers in Education, 10, Article 1738751. https://doi.org/10.3389/feduc.2025.1738751

Akata, Z., Balliet, D., Rijke, M. d., Dignum, F., Dignum, V., Eiben, G., Fokkens, A., Grossi, D., Hindriks, K., Hoos, H., Hung, H., Jonker, C., Monz, C., Neerincx, M., Oliehoek, F., Prakken, H., Schlobach, S., Gaag, L. v. d., Harmelen, F. v., Hoof, H. v., Riemsdijk, B. v., Wynsberghe, A. v., Verbrugge, R., Verheij, B., Vossen, P., & Welling, M. (2020). A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence. Computer, 53(8), 18–28. https://doi.org/10.1109/MC.2020.2996587

Alfredo, R., Echeverria, V., Jin, Y., Yan, L., Swiecki, Z., Gašević, D., & Martinez-Maldonado, R. (2024). Human-centred learning analytics and AI in education: A systematic literature review. Computers and Education: Artificial Intelligence, 6, Article 100215. https://doi.org/10.1016/j.caeai.2024.100215

Alier, M., García-Peñalvo, F. J., Casañ, M. J., Pereira, J. A., & Llorens-Largo, F. (2024). Safe AI in Education Manifesto. Version 0.4.0. https://manifesto.safeaieducation.org.

Alier, M., Pereira, J., Garcia-Peñalvo, F. J., Casañ, M. J., & Cabré, J. (2025). LAMB: An Open-Source Software Framework to Create Artificial Intelligence Assistants Deployed and Integrated into Learning Management Systems. Computer Standards & Interfaces, 92, Article 103940. https://doi.org/10.1016/j.csi.2024.103940

Alier-Forment, M., Casañ-Guerrero, M. J., Pereira, J., García-Peñalvo, F. J., & Llorens-Largo, F. (2026). Generative artificial intelligence and educational autonomy: historical metaphors and ethical principles for pedagogical transformation. RIED: revista iberoamericana de educación a distancia, 29(1), 9–28. https://doi.org/10.5944/ried.29.1.45536

Almusaed, A., Almssad, A., Yitmen, I., & Homod, R. Z. (2023). Enhancing Student Engagement: Harnessing “AIED”’s Power in Hybrid Education—A Review Analysis. Education Sciences, 13(7), Article 632. https://doi.org/10.3390/educsci13070632

Baker, R. S., & Hawn, A. (2022). Algorithmic Bias in Education. International Journal of Artificial Intelligence in Education, 32(4), 1052–1092. https://doi.org/10.1007/s40593-021-00285-9

Banihashem, S. K., Bond, M., Bergdahl, N., Khosravi, H., & Noroozi, O. (2025). A systematic mapping review at the intersection of artificial intelligence and self-regulated learning. International Journal of Educational Technology in Higher Education, 22(1), Article 50. https://doi.org/10.1186/s41239-025-00548-8

Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö., & Mariman, R. (2025). Generative AI without guardrails can harm learning: Evidence from high school mathematics. Proceedings of the National Academy of Sciences, 122(26), Article e2422633122. https://doi.org/10.1073/pnas.2422633122

Beckman, K., Apps, T., Howard, S. K., Rogerson, C., Rogerson, A., & Tondeur, J. (2025). The GenAI divide among university students: A call for action. The Internet and Higher Education, 67, Article 101036. https://doi.org/10.1016/j.iheduc.2025.101036

Bulathwela, S., Pérez-Ortiz, M., Holloway, C., Cukurova, M., & Shawe-Taylor, J. (2024). Artificial Intelligence Alone Will Not Democratise Education: On Educational Inequality, Techno-Solutionism and Inclusive Tools. Sustainability, 16(2), Article 781. https://doi.org/10.3390/su16020781

Carter, L., Liu, D., & Cantrell, C. (2020). Exploring the Intersection of the Digital Divide and Artificial Intelligence: A Hermeneutic Literature Review. AIS Transactions on Human-Computer Interaction, 12(4), 253–275. https://doi.org/10.17705/1thci.00138

Čep, A., Bernik, A., & Tomičić, I. (2026). Adaptive Learning Systems in Higher Education: Challenges, Trends, and Outcomes. In K. Arai (Ed.), Proceedings of the Future Technologies Conference (FTC) 2025, Volume 4 (pp. 1–17). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-07992-3_1

Clark, A., & Chalmers, D. (1998). The Extended Mind. Analysis, 58(1), 7–19.

Corbett, A. T., Koedinger, K. R., & Anderson, J. R. (1997). Intelligent Tutoring Systems. In M. Helander, T. K. Landauer, & P. Prabhu (Eds.), Handbook of Human-Computer Interaction (2nd ed., pp. 849–874). Elsevier Science. http://act-r.psy.cmu.edu/wordpress/wp-content/uploads/2012/12/173Chapter_37_Intelligent_Tutoring_Systems.pdf

Creely, E., & Blannin, J. (2025). Creative partnerships with generative AI. Possibilities for education and beyond. Thinking Skills and Creativity, 56, Article 101727. https://doi.org/10.1016/j.tsc.2024.101727

Cukurova, M. (2025). The interplay of learning, analytics and artificial intelligence in education: A vision for hybrid intelligence. British Journal of Educational Technology, 56(2), 469–488. https://doi.org/10.1111/bjet.13514

Dellermann, D., Ebel, P., Söllner, M., & Leimeister, J. M. (2019). Hybrid Intelligence. Business & Information Systems Engineering, 61(5), 637–643. https://doi.org/10.1007/s12599-019-00595-2

Drăgoi, M.-V., Puiu, R.-A., Petrea, G., Cristoiu, C. A., & Dumitrescu, C.-I. (2026). Willingness to Allow Educational Data Use for Learning Analytics in Higher Education: Trust and Governance Predictors: An Exploratory Study. Education Sciences, 16(4), Article 637. https://doi.org/10.3390/educsci16040637

El Hosayny, O., Razkane, H., & Yeou, M. (2026). Metacognitive regulation and ChatGPT-assisted writing: introducing an interactive model for cultivating strategic engagement. Discover Education, 5(1), Article 246. https://doi.org/10.1007/s44217-026-01282-7

Estrada-Molina, O., Ruiz Zapatero, J. L., Rodrigo Lacueva, P., Zambrano-Acosta, J. M., & Chica Chica, L. F. (2025). Artificial intelligence in primary education: systematic review (2000-2024). Education in the Knowledge Society, 26, Article e32300. https://doi.org/10.14201/eks.32300

European Parliament, & The Council of the European Union. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act) (Text with EEA relevance). (Official Journal of the European Union). European Union Retrieved from https://eur-lex.europa.eu/eli/reg/2024/1689/oj.

Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2025). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, 56(2), 489–530. https://doi.org/10.1111/bjet.13544

García-Peñalvo, F. J. (2018). Ecosistemas tecnológicos universitarios. In J. Gómez (Ed.), UNIVERSITIC 2017. Análisis de las TIC en las Universidades Españolas (pp. 164–170). Crue Universidades Españolas.

García-Peñalvo, F. J. (2023). The perception of Artificial Intelligence in educational contexts after the launch of ChatGPT: Disruption or Panic? Education in the Knowledge Society, 24, Article e31279. https://doi.org/10.14201/eks.31279

García-Peñalvo, F. J. (2024a). Generative Artificial Intelligence and Education: An Analysis from Multiple Perspectives. Education in the Knowledge Society, 25, Article e31942. https://doi.org/10.14201/eks.31942

García-Peñalvo, F. J. (2024b). Mito de la inteligencia. Más allá de una educación de silicio. In C. Suárez-Guerrero, J. E. Raffaghelli, & P. Rivera-Vargas (Eds.), Mitos EdTech. Desmontando el solucionismo tecnológico en educación (pp. 79–87). Editorial UOC.

García-Peñalvo, F. J. (2025). Three Scenarios for AI in Education: From Responsible Assistance to Co-Creation. Education in the Knowledge Society, 26, Article e32932. https://doi.org/10.14201/eks.32932

García-Peñalvo, F. J., Alier, M., Pereira, J. A., & Casañ, M. J. (2024). Safe, Transparent, and Ethical Artificial Intelligence: Keys to Quality Sustainable Education (SDG4). IJERI – International Journal of Educational Research and Innovation(22), 1–21. https://doi.org/10.46661/ijeri.11036

García-Peñalvo, F. J., Alier-Forment, M., Vázquez-Ingelmo, A., García-Holgado, A., Casañ-Guerrero, M. J., & Pereira, J. (2026). Generative artificial intelligence-assisted assessment in Software Engineering assignments: A proof of concept. RIED-Revista Iberoamericana de Educación a Distancia, 29(2), 343–368. https://doi.org/10.5944/ried.47173

García-Peñalvo, F. J., & Vázquez-Ingelmo, A. (2023). What do we mean by GenAI? A systematic mapping of the evolution, trends, and techniques involved in Generative AI. International Journal of Interactive Multimedia and Artificial Intelligence, 8(4), 7–16. https://doi.org/10.9781/ijimai.2023.07.006

Graham, C. R. (2006). Blended learning systems: Definition, current trends, and future directions. In C. J. Bonk & C. R. Graham (Eds.), The handbook of blended learning: Global perspectives, local designs (pp. 3–21). JosseyBass/Pfeiffer.

Haraway, D. (1991). Simians, cyborgs, and women: The reinvention of nature. Routledge.

Holstein, K., Aleven, V., & Rummel, N. (2020). A Conceptual Framework for Human–AI Hybrid Adaptivity in Education. In I. I. Bittencourt, M. Cukurova, K. Muldner, R. Luckin, & E. Millán (Eds.), Artificial Intelligence in Education. 21st International Conference, AIED 2020, Ifrane, Morocco, July 6–10, 2020, Proceedings, Part I (pp. 240–254). Springer International Publishing.

Holstein, K., McLaren, B. M., & Aleven, V. (2018). Student Learning Benefits of a Mixed-Reality Teacher Awareness Tool in AI-Enhanced Classrooms. In C. Penstein Rosé, R. Martínez-Maldonado, H. U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren, & B. du Boulay (Eds.), Artificial Intelligence in Education. 19th International Conference, AIED 2018, London, UK, June 27–30, 2018, Proceedings, Part I (pp. 154–168). Springer International Publishing. https://doi.org/10.1007/978-3-319-93843-1_12

Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-Designing a Real-Time Classroom Orchestration Tool to Support Teacher–AI Complementarity. Journal of Learning Analytics, 6(2), 27–52. https://doi.org/10.18608/jla.2019.62.3

Hooshyar, D., Šír, G., Yang, Y., Kikas, E., Hämäläinen, R., Kärkkäinen, T., Gašević, D., & Azevedo, R. (2025). Towards responsible AI for education: Hybrid human-AI to confront the elephant in the room. Computers and Education: Artificial Intelligence, 9, Article 100524. https://doi.org/10.1016/j.caeai.2025.100524

Hooshyar, D., Yang, Y., Pedaste, M., & Huang, Y. M. (2020). Clustering Algorithms in an Educational Context: An Automatic Comparative Approach. IEEE Access, 8, 146994–147014. https://doi.org/10.1109/ACCESS.2020.3014948

Jang, H., Kim, S., & So, H.-J. (2026). Technology-based scaffolding for ill-structured problem-solving: a systematic review and emerging directions with AI. Learning: Research and Practice, 12(2), 187–210. https://doi.org/10.1080/23735082.2025.2591403

Jovanović, M., & Campbell, M. (2022). Generative Artificial Intelligence: Trends and Prospects. Computer, 55(10), 107–112. https://doi.org/10.1109/MC.2022.3192720

Kaebnick, G. E., Magnus, D. C., Kao, A., Hosseini, M., Resnik, D., Dubljević, V., Rentmeester, C., Gordijn, B., & Cherry, M. J. (2023). Editors’ Statement on the Responsible Use of Generative AI Technologies in Scholarly Journal Publishing. Hastings Center Report, 53(5), 3–6. https://doi.org/10.1002/hast.1507

Knoop-van Campen, C., & Molenaar, I. (2020). How Teachers integrate Dashboards into their Feedback Practices. Frontline Learning Research, 8(4), 37–51. https://doi.org/10.14786/flr.v8i4.641

Knox, J., Williamson, B., & Bayne, S. (2020). Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies. Learning, Media and Technology, 45(1), 31–45. https://doi.org/10.1080/17439884.2019.1623251

Kudina, O., Ballsun-Stanton, B., & Alfano, M. (2025). The use of large language models as scaffolds for proleptic reasoning. Asian Journal of Philosophy, 4(1), Article 24. https://doi.org/10.1007/s44204-025-00247-1

Laak, K.-J., & Aru, J. (2025). AI and personalized learning: Bridging the gap with modern educational goals. Technology & Society, 28(4), 133–150. https://doi.org/10.30191/ETS.202510_28(4).RP08

Latour, B. (2005). Reassembling the Social: An Introduction to Actor-Network-Theory. Oxford University Press. https://doi.org/10.1093/oso/9780199256044.001.0001

Lee, C., Luo, L., Kuhlmann, S. L., Plumley, R. D., Panter, A. T., Bernacki, M. L., Greene, J. A., & Gates, K. M. (2025). Interpretable Predictive Analytics for Online Learning: A Markov-Based Machine Learning Approach. Journal of Learning Analytics, 12(2), 259–278. https://doi.org/10.18608/jla.2025.8375

Leydesdorff, L. (2012). The Triple Helix, Quadruple Helix, …, and an N-Tuple of Helices: Explanatory Models for Analyzing the Knowledge-Based Economy? Journal of the Knowledge Economy, 3(1), 25–35. https://doi.org/10.1007/s13132-011-0049-4

Licklider, J. C. R. (1960). Man-Computer Symbiosis. IRE Transactions on Human Factors in Electronics, HFE-1(1), 4–11. https://doi.org/10.1109/THFE2.1960.4503259

Marina, J. A. (2020). Proyecto Centauro. Ediciones KHAF.

Molenaar, I. (2022). Towards hybrid human-AI learning technologies. European Journal of Education, 57(4), 632–645. https://doi.org/10.1111/ejed.12527

Molina-Carmona, R., Compañ-Rosique, P., Satorre-Cuerda, R., Villagrá-Arnedo, C. J., Gallego-Durán, F. J., & Llorens-Largo, F. (2017). Technological Ecosystem Maps for IT Governance: Application to a Higher Education Institution. In F. J. Garcia-Peñalvo & A. García-Holgado (Eds.), Open Source Solutions for Knowledge Management and Technological Ecosystems (pp. 50–80). IGI Global. https://doi.org/10.4018/978-1-5225-0905-9.ch003

Molina-Carmona, R., & García-Peñalvo, F. J. (2025). Safeguarding Knowledge: Ethical Artificial Intelligence Governance in the University Digital Transformation. In E. Vendrell Vidal, U. R. Cukierman, & M. E. Auer (Eds.), Advanced Technologies and the University of the Future (pp. 201–220). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-71530-3_14

Moshaver, S., &Mehdizadeh, S. (2026). A pedagogical framework for physics simulation enhanced augmented reality in on-site architectural design. Discover Education, 5(1), Article 194. https://doi.org/10.1007/s44217-026-01216-3

Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, Article 100020. https://doi.org/10.1016/j.caeai.2021.100020

Pardos, Z. A., & Bhandari, S. (2024). ChatGPT-generated help produces learning gains equivalent to human tutor-authored help on mathematics skills. PloS one, 19(5), Article e0304013. https://doi.org/10.1371/journal.pone.0304013

Pelánek, R. (2015). Metrics for Evaluation of Student Models. Journal of Educational Data Mining, 7(2), 1–19. https://doi.org/10.5281/zenodo.3554665

Prinsloo, P., Khalil, M., & Slade, S. (2024). Learning analytics as data ecology: a tentative proposal. Journal of Computing in Higher Education, 36(1), 154–182. https://doi.org/10.1007/s12528-023-09355-4

Rahm, L. (2023). Education, automation and AI: a genealogy of alternative futures. Learning, Media and Technology, 48(1), 6–24.https://doi.org/10.1080/17439884.2021.1977948

Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x

Selwyn, N. (2022). The future of AI and education: Some cautionary notes. European Journal of Education, 57(4), 620–631. https://doi.org/10.1111/ejed.12532

Shiri, A. (2024). Artificial intelligence literacy: a proposed faceted taxonomy. Digital Library Perspectives, 40(4), 681–699. https://doi.org/10.1108/DLP-04-2024-0067

Topali, P., Haelermans, C., Molenaar, I., & Segers, E. (2025). Pedagogical considerations in the automation era: A systematic literature review of AIEd in K-12 authentic settings. British Educational Research Journal, 51(6), 2777–2809. https://doi.org/10.1002/berj.4200

Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7

UNESCO. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development (ED-2019/WS/8). (Working papers on education policy). UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000366994.

UNESCO. (2024). AI competency framework for teachers. UNESCO. https://doi.org/10.54675/ZJTE2084

van Leeuwen, A., Knoop-van Campen, C. A. N., Molenaar, I., & Rummel, N. (2021). How Teacher Characteristics Relate to How Teachers Use Dashboards: Results From Two Case Studies in K-12. Journal of Learning Analytics, 8(2), 6–21. https://doi.org/10.18608/jla.2021.7325

Williamson, B., Macgilchrist, F., & Potter, J. (2023). Re-examining AI, automation and datafication in education. Learning, Media and Technology, 48(1), 1–5. https://doi.org/10.1080/17439884.2023.2167830

Yang, J., Jin, H., Tang, R., Han, X., Feng, Q., Jiang, H., Zhong, S., Yin, B., & Hu, X. (2024). Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond. ACM Transactions on Knowledge Discovery from Data, 18(6), Article 160. https://doi.org/10.1145/3649506

Zawacki-Richter, O., Marín, V. I., Bond, M., & 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), Article 39. https://doi.org/10.1186/s41239-019-0171-0

Zhang, Q., & Liang, Y. (2026). Chatbot-assisted self-regulated learning scaffolding in EAP writing: the mediating role of SRL and the moderating role of AI literacy. Education and Information Technologies, In Press. https://doi.org/10.1007/s10639-026-14038-5

Inteligencia híbrida y agencia educativa: hacia una ecología de colaboración humano-IA. (2026). Education in the Knowledge Society (EKS), 27, e33610. https://doi.org/10.14201/eks.33610
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