Teacher agency and distributed cognition in the age of generative artificial intelligence
Keywords:
Teacher agency, distributed cognition, generative artificial intelligence, professional judgment, teacher professional development, cognitive offloadingAbstract
The incorporation of generative artificial intelligence into educational practice enables the automation, enhancement, and expansion of activities traditionally associated with teachers’ professional reasoning. This scenario raises questions about teacher agency when processes such as planning, activity design, assessment, and information analysis are carried out through interaction with generative systems. This critical-argumentative essay examines this relationship through a conceptually oriented documentary review that integrates contributions on teacher agency, distributed cognition, professional knowledge, cognitive offloading, pedagogical judgment, and artificial intelligence literacy. It argues that the involvement of artificial intelligence does not necessarily imply a reduction in teacher agency, but rather a reconfiguration of the cognitive operations through which educational action is constructed. This distribution can enhance professional capabilities when it increases available alternatives, reduces instrumental operations, and supports processes of inquiry, creativity, and reflection. However, it can weaken agency when it replaces understanding of educational purposes, contextualization, professional judgment, or the ability to justify pedagogical decisions. The article proposes conceptualizing teacher agency mediated by artificial intelligence as a situated exercise of understanding, regulation, evaluation, and professional responsibility, articulated through a heuristic cycle of pedagogically regulated distributed cognition. It concludes that the educational challenge lies not in distributing cognition between teachers and generative systems, but in externalizing without supervision those cognitive operations that sustain pedagogical judgment, evidence-based decision-making, and professional responsibility for educational actions carried out in specific contexts.
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Aarset, M. V., & Johannessen, L. K. (2022). On distributed cognition while designing an AI system for adapted learning. Frontiers in Artificial Intelligence, 5, Article 910630. https://doi.org/10.3389/frai.2022.910630
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
Cáceres-Mesa, M. L. (Comp.). (2026). Educación superior en tiempos de inteligencia artificial: Pedagogía, evaluación y bienestar. Sophia Editions.
Celik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, Article 107468. https://doi.org/10.1016/j.chb.2022.107468
Choi, S., Jeong, S., Park, S., Kim, Y., & Han, I. (2026). Analyzing teacher-AI interaction patterns across teacher experience and AI proficiency in student-centered lesson design. Teaching and Teacher Education, 169, Article 105266. https://doi.org/10.1016/j.tate.2025.105266
Ding, A.-C. E., Shi, L., Yang, H., & Choi, I. (2024). Enhancing teacher AI literacy and integration through different types of cases in teacher professional development. Computers and Education Open, 6, Article 100178. https://doi.org/10.1016/j.caeo.2024.100178
Eedelouei, S. (2026). Factors influencing language teachers’ judgements and decision-making about the use of generative AI tools: A systematic review. Teaching and Teacher Education, 171, Article 105333. https://doi.org/10.1016/j.tate.2025.105333
Emirbayer, M., & Mische, A. (1998). What is agency? American Journal of Sociology, 103(4), 962–1023. https://doi.org/10.1086/231294
García-Poyato Falcón, J., Mercado Varela, M. A., Cordero Arroyo, G., & Almaraz Núñez, S. (2026). Formación del juicio profesional docente en una experiencia con inteligencia artificial generativa. Revista Educación Superior y Sociedad (ESS), 37(2), 110–126. https://doi.org/10.54674/ess.v37i2.1132
Ghamoushi, M., Mohammadi Zenouzagh, Z., & Hashamdar, M. (2022). Development and validation of a potential assessment inventory for assessing EFL teachers’ ecological agency. Language Testing in Asia, 12, Article 37. https://doi.org/10.1186/s40468-022-00190-5
Grinschgl, S., & Neubauer, A. C. (2022). Supporting cognition with modern technology: Distributed cognition today and in an AI-enhanced future. Frontiers in Artificial Intelligence, 5, Article 908261. https://doi.org/10.3389/frai.2022.908261
Gurl, T. J., Markinson, M. P., & Artzt, A. F. (2025). Using ChatGPT as a lesson planning assistant with preservice secondary mathematics teachers. Digital Experiences in Mathematics Education, 11, 114–139. https://doi.org/10.1007/s40751-024-00162-9
Hong, H.-Y., Chen, M.-J., Chang, C.-H., Tseng, L.-T., & Chai, C. S. (2025). AI-supported idea-developing discourse to foster professional agency within teacher communities for STEAM lesson design in knowledge building environment. Computers & Education, 229, Article 105241. https://doi.org/10.1016/j.compedu.2025.105241
Hutchins, E. (1995). Cognition in the wild. The MIT Press.
Iqbal, J., Hashmi, Z. F., Asghar, M. Z., & Abid, M. N. (2025). Generative AI tool use enhances academic achievement in sustainable education through shared metacognition and cognitive offloading among preservice teachers. Scientific Reports, 15, Article 16610. https://doi.org/10.1038/s41598-025-01676-x
Krushinskaia, K., Elen, J., & Raes, A. (2026). Pre-service teachers’ agency during their interactions with generative AI while designing for learning: A process view on Intelligent-TPACK. Computers and Education Open, 10, Article 100325. https://doi.org/10.1016/j.caeo.2025.100325
Li, Z., Wang, C., & Bonk, C. J. (2026). Generative AI for teachers’ self-directed professional development: A mixed-methods study. TechTrends, 70, 23–38. https://doi.org/10.1007/s11528-025-01123-8
Mendes, V., Gelber, D., Fraser, P., Rodríguez, S., & Rubio, X. (2025). Estrategia regional docente para América Latina y el Caribe, 2025–2030. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000395165
Miao, F., & Cukurova, M. (2024). AI competency framework for teachers. UNESCO. https://doi.org/10.54675/ZJTE2084
Mouta, A., Torrecilla-Sánchez, E. M., & Pinto-Llorente, A. M. (2025). Comprehensive professional learning for teacher agency in addressing ethical challenges of AIED: Insights from educational design research. Education and Information Technologies, 30, 3343–3387. https://doi.org/10.1007/s10639-024-12946-y
Organización de las Naciones Unidas para la Educación, la Ciencia y la Cultura. (2025). Promoting and protecting teacher agency in the age of artificial intelligence. International Task Force on Teachers for Education 2030. UNESCO. https://www.iicba.unesco.org/es/africa-education-knowledge-platform/promoting-and-protecting-teacher-agency-age-artificial-intelligence
Priestley, M., Biesta, G., & Robinson, S. (2026). Teacher agency: An ecological approach (2nd ed.). Bloomsbury Academic.
Sperling, K., Stenberg, C.-J., McGrath, C., Åkerfeldt, A., Heintz, F., & Stenliden, L. (2024). In search of artificial intelligence (AI) literacy in teacher education: A scoping review. Computers and Education Open, 6, Article 100169. https://doi.org/10.1016/j.caeo.2024.100169
Velander, J. (2026). Beyond operational skills: Teachers’ AI knowledge and interactions with generative AI in lesson planning. Computers and Education Open, 10, Article 100371. https://doi.org/10.1016/j.caeo.2026.100371
Wang, K., Chai, C.-S., Liang, J.-C., & Sang, G. (2024). Exploring teachers’ behavioural intentions to design artificial intelligence-assisted learning in Chinese K-12 education. Technology, Pedagogy and Education, 33(5), 629–645. https://doi.org/10.1080/1475939X.2024.2369241
Wingerter, T. L., Straub, T., & Schweitzer, S. (2025). Mitigating automation bias in generative AI through nudges: A cognitive reflection test study. Procedia Computer Science, 270, 2106–2114. https://doi.org/10.1016/j.procs.2025.09.331
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