OECD Digital Education Outlook 2026: Exploring Effective Uses of Generative AI in Education

In this chapter, teacher agency refers to teachers’ active capacity and incentives to make choices and exert influence in their professional practice. It encompasses the autonomy to make instructional decisions, the ability to adapt and innovate pedagogy, the power to shape the educational environment in accordance with their professional values and their students’ needs as well as the willingness and incentives to do so. In essence, teachers have agency when they “act rather than are acted upon” in the educational process. Their agency is rooted in professional competence and confidence, and is often enabled or constrained by the tools and technology they use in their practice as well as the broader institutional and policy context in which they are situated. Teacher agency is not an all-or-nothing attribute; it exists in degrees and forms. Educational sociologists have described multiple forms of teacher autonomy, for example, autonomy over curriculum content, over pedagogy, over student assessment, and over professional development pathways (Frostenson, 2015). A supportive school culture and policy framework can expand these autonomies, whereas top-down mandates, high-stakes accountability regimes, or technology systems making autonomous decisions on their behalf can compress them (as can be the case with AI-based educational technologies).

Teacher agency is a key concept in education since teachers’ sense of agency is linked to their motivation, job satisfaction, and willingness to embrace new pedagogies or tools. When teachers feel empowered to make decisions, they are more likely to take initiative in improving their teaching and respond creatively to challenges in the classroom. Teacher agency enables teachers to adapt and update curricula, incorporate emerging real-world problems, and contextualise learning experiences to social, emotional, and relational needs of their students in ways that AI algorithms cannot. Moreover, teacher agency is tightly connected to teacher identity, that is, the sense of oneself as a professional with a meaningful mission. The introduction of GenAI into the classroom can perturb that identity, since some teachers may fear being displaced or judged by GenAI systems, while others might see GenAI as an opportunity to enhance their effectiveness.

From a learner’s perspective, teacher agency has the potential to translate to richer educational interactions. A teacher with high agency will actively interpret AI-generated insights or recommendations and adapt them to the context of their students. For example, if a GenAI tool generates feedback on students’ essays or produces a set of suggested prompts to foster metacognitive reflection, a teacher exercising professional agency will treat these as provisional resources (e.g. reviewing their pedagogical relevance, rephrasing or extending them to align with students’ learning goals), and using them as a springboard for discussion or further inquiry. By contrast, a low- agency scenario might involve a teacher simply pasting the AI-generated feedback into the learning platform without review or contextualisation, or alternatively, disregarding the system’s suggestions entirely due to mistrust or lack of confidence. Both extremes are suboptimal, and the goal is a balanced partnership where the teacher remains the orchestrator of instruction, using AI tools as informative assistants. Although most researchers and practitioners would agree with the proposed need for balance, there is little understanding regarding where this balance stands for a given teacher task, how it can be conceptualised, and how it can be operationalised. This chapter is an attempt to fill in this gap. In the following sections, the chapter dives deeper into how a theoretical conceptualisation of AI’s role vis- à-vis teacher agency, introducing the conceptualisations of replacement, complementarity, and augmentation, and then proposes a five-level teacher-AI teaming framework with clear definitions and examples of each level. Although, a broad set of socio-technical, institutional, and cultural factors profoundly shape how teachers perceive, enact, and sustain agency in their interactions with AI, the five-level framework proposed here mainly examines teacher agency through the lens of AI system affordances and interface-level design considerations.

Three conceptualisations of AI in education and implications on teacher agency

This section presents three conceptual modes of integration as a framework to differentiate AI’s roles and their implications for teacher agency. These modes can be considered as points along a dynamic spectrum from AI operating independently of teachers to AI becoming deeply embedded in teachers’ cognitive routines to augment their competence. By delineating these, the chapter aims to clarify which approaches threaten teacher agency and which can potentially bolster it. Although the framework is applicable to all forms of AI, generative AI provides distinctive affordances that can shape and support varying levels of teacher agency. These will be discussed with examples where appropriate.

Replacement

Replacement refers to AI systems executing tasks that a teacher would typically do with an AI-driven process with minimal or no teacher intervention. Classic examples include automated grading of exams or essays, asking an AI model to create lesson plans, questions, materials, algorithmic scheduling of student practice, such as homework, or AI tutors delivering content directly to learners, automating the pedagogical practice of teachers. The main appeal of automation is efficiency and scalability. Indeed, certain labour-intensive tasks in teaching (e.g. grading multiple-choice quizzes, drafting lesson plans, generating practice problems) can be reliably automated, potentially freeing teachers’ time for other or more complex work (e.g. Leiker, Finnigan and Cukurova, 2023; Pea et al., 2022; Roy et al., 2024). Influenced by the political, managerial, or leadership level ecosystemic issues of teaching practice, currently the evidence about how teachers actually use this saved time is scarce.

The potential of productivity gains in education is indeed important. Recent research by the Education Endowment Foundation (EEF), independently evaluated by the National Foundation for Educational Research (NFER), examined the use of GenAI among 259 teachers across 68 secondary schools in England. The randomised controlled trial revealed that teachers who used GenAI, supplemented by practical guidance, reduced their lesson and resource planning time by an average of 31 percent, reducing their weekly average planning time from 81.5 to 56.2 minutes, without compromising the quality of their lesson plans and resources (Roy et al., 2024). As highlighted by the Teacher Task Force and UNESCO Global Report on Teachers (2024), education systems face compounding teacher and resource crises, especially in low- and middle-income countries. An estimated 44 million additional primary and secondary teachers are said to be needed by 2030, including 15 million in sub-Saharan Africa. This worldwide shortage of teachers is aggravated by rising attrition, as many teachers leave the profession early. Rural and remote areas have been hit hardest, where underqualified teachers often fill the gaps and multi-grade classrooms are common; 90% of secondary schools in sub- Saharan Africa face serious teaching shortages. Consequently, learning gaps are widening. Students also contend with severe shortages of education materials and quality content. In some classrooms, a single textbook must be shared by a dozen or more pupils. Much of the digital education content that could help is not in the learner’s language. For instance, even though they are not representative of countries’ learning resources, 92% of open education resources are in English, marginalising non-English-speaking learners (UNESCO, 2023). The replacement paradigm in AI offers opportunities to counteract these global shortages with significant productivity gains. GenAI-powered tools can supplement overburdened teaching workforces and provide instructional support to students in underserved areas. Translation and content generation driven by GenAI can expand the availability of high-quality teaching resources in local languages and for students with Special Educational Needs and Disabilities (SEND).

Nevertheless, the full automation of teacher tasks also raises concerns about the loss of teacher agency and may come at certain costs. The purpose of highlighting these challenges is not to oppose productivity gains in education, but to invite policy dialogue and careful consideration of how some of these challenges can be mitigated. Two of those concerns are the dehumanisation of education and teacher cognitive atrophy.

Dehumanisation refers to the erosion of the human elements that are fundamental to teaching and learning. Education is an interactive process of human development. When AI is used as a substitute for teachers or peers, there is a danger that learning becomes overly mechanistic, losing the empathy and social dynamics that characterise many effective pedagogies and educational practices. Dehumanisation can manifest in teaching, assessment and feedback for example. Some may be tempted to have “teacherless schools” and provide instruction by having students working through AI-personalised curricula. However, human teachers contribute numerous intangible qualities (e.g. moral judgment, inspiration, role-modelling, the ability to build trust with other humans and mentor them) that no AI currently can replicate. Even if students progressed academically, they would miss out on collaborative learning, dialogue, and the social construction of knowledge with other humans. Educational practice often thrives on inquiry- based instruction, collaborative lab-work, group discussions, and debate, which are facets that require human presence and guidance. Similarly, feedback and evaluation from impersonal algorithms might make students feel less seen or valued as individuals. Finally, if teachers were asked to work under algorithmic scripts or performance dashboards that dictated their every move, their professional identity would likely erode and resemble more that of assembly line workers than educators. Protecting teacher agency is thus directly tied to keeping education human- centred.

Similarly, the integration of generative AI into educational environments in ways that do not allow teacher agency also raises significant questions about its impact on teachers various higher-order thinking skills, particularly through its influence on critical and reflective thinking practices. A recent experiment focusing on the cognitive cost of using a Large Language Model in the educational context of writing an essay shows that students writing without an LLM support exhibited the strongest, widest-ranging brain activation, those using a search engine showing intermediate engagement, and those using an LLM a limited cognitive engagement (Kosmyna and Hauptmann, 2025). While the evidence on the potential negative impact on GenAI users’ cognitive capabilities when they use GenAI is just emerging, this may also be a risk for teachers when they use AI in the replacement paradigm.