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

Introduction

The growing presence of generative artificial intelligence (GenAI) technologies in educational contexts presents a paradox of empowerment and concern. On one hand, AI promises to relieve teachers of burdensome administrative tasks (Roy et al., 2024), provide personalised learning insights (VanLehn, 2011), and complement instructional capabilities including for lesson planning, for classroom implementation and for assessment. For example, AI could help teachers plan their lessons by supporting their review of the knowledge to be taught, by providing ideas on alternative pedagogical strategies or by defining students’ needs and familiarising them with such needs (van den Berg and du Plessis, 2023). AI could also support classroom instruction through immediate feedback to students (Di Mitri, Schneider and Drachsler, 2021) or to teachers about their practice (Demszky et al., 2025) or about their interventions (Aslan et al., 2019); and AI can be used for modelling students’ mastery (Minn, 2022), to generate assessment items (Chan et al., 2025), supported essay scoring (Seßler et al., 2025) or feedback on certain aspects of their teaching (Zhang et al., 2025).

On the other hand, there are significant concerns regarding potential threats, including the erosion of teacher autonomy (Miao and Cukurova, 2024), de-professionalisation of teaching (Berendt, Littlejohn and Blakemore, 2020), and ethical pitfalls if AI is misapplied (Holmes et al., 2021). Many education experts are also concerned that over-reliance on AI to perform teachers’ tasks, such as marking, feedback generation, and lesson planning could risk skill atrophy for teachers (Felix and Webb, 2024). Therefore, the notion of teacher agency (i.e. teachers’ ability to exercise professional judgment, innovate in practice, and maintain control over pedagogical decisions) has come to the forefront of debates on AI in education.

This chapter emphasises the need and urgency for moving beyond polarised narratives, rejecting both the dystopian fear of AI teachers replacing humans and the utopian hype that AI alone will solve all educational problems. Instead, it calls for evidence-informed strategies that harness AI’s potential while safeguarding teachers’ agency, rights, and professional integrity. As professionals increasingly offload tasks, that were traditionally viewed as uniquely human, to AI, we must engage in profound and forward-looking reflection about the essential roles and identities of professionals. What are the enduring and uniquely human qualities at the heart of education? How can we safeguard, elevate, and flourish them as technology transforms lives and the broader teaching and learning ecosystem? What is the role of a teacher in education where technological advancements allow the generation and sharing of information effectively and efficiently?

To frame the inquiry into these difficult questions, the chapter outlines a continuum of AI integration in teaching, distinguishing between replacement, complementarity, and augmentation of teacher competence with AI in educational practice. Replacement through automation refers to tasks being offloaded to AI systems and carried out by them with no further teacher intervention. Complementarity requires a careful consideration of what teacher competence means for a given task and a specific AI tool’s design, development, and deployment features that can complement the specific aspects of the teacher’s competence. Augmentation suggests a deeper assimilation of AI models into teachers’ cognitive and pedagogical processes to be able to improve teachers’ competence through iterative interactions so that AI-augmented teachers can achieve the task better than AI or teacher alone (i.e. competence augmentation increases the likelihood of teacher-AI teams to perform better than the best of teacher or AI alone). Understanding these conceptual models is crucial for identifying when AI use supports teacher agency, when it might erode it, and when teachers’ agentic interactions can lead to competence augmentation for teachers, and thus to more effective teaching.

How do teachers use GenAI in education: Early benefits and concerns

A review of the literature reveals that while AI applications in education are burgeoning, our understanding of how exactly teachers are integrating GenAI into their practices at a scale remains nascent and continually evolving. Additionally, the ways in which teachers engage with GenAI vary significantly across countries and jurisdictions, making it challenging to comprehensively capture the full range of practices in this chapter. However, for instance, based on the open call for evidence of the use of GenAI in education by the UK Department for Education, which received (non-representative, but elaborate) responses from 567 participants, the majority of whom were teachers, the public generally believed GenAI to offer various opportunities (Department for Education, 2023). These include freeing up teachers' time, improving teaching and educational materials, providing additional support to students (particularly those with special educational needs and disabilities (SEND) and those for whom English is an additional language (EAL)), and enhancing subject-specific applications (e.g. STEM). Overall, these perceived benefits argued to outweigh concerns about GenAI in education (e.g. students’ overreliance on GenAI, academic misconduct, fear of GenAI replacing face-to-face teaching, and the ”digital divide”). Most use cases observed in self-declared surveys and interviews of teachers also indicate that teachers use GenAI to develop lesson materials, ensuring alignment with curricular objectives while saving time on content preparation (Department for Education, 2024). In assessment, teachers tend to use it to support their marking and provide personalised formative feedback for students. Beyond the classroom, teachers appear to use GenAI in drafting statutory policies, streamlining administrative tasks, and aiming to reduce bureaucratic burdens (Department for Education, 2024).

While self-reported survey data offer some insights into teachers' usage of GenAI, this method faces inherent limitations, notably biases arising from external pressures or social desirability that may prevent teachers from accurately reporting their AI practices. Recent research from Anthropic.ai (Handa et al., 2025) analyses over four million conversations with their GenAI system and shows high reliance on AI for some professions. Among certain professional groups, including language and literature teachers, AI conversations correspond to the performance of more than 75% of their professional tasks (following the task mapping for occupations by the O*Net database of the US Department of Labor). While understanding the extent of GenAI usage among teachers is crucial, examining precisely how teachers employ GenAI, whether primarily for augmentation or automation, is equally significant. Although it is a task-dependent discussion, evidence suggests a tendency towards automating routine tasks such as content generation (Handa et al., 2025), with AI directly executing responsibilities requiring minimal teacher involvement. Although such conclusions should be interpreted cautiously, as log data alone cannot reveal how teachers ultimately engage with, or act upon AI-generated outputs (e.g. some may discard them entirely and continue their work independently), automated applications of AI in education raise critical questions regarding the broader implications for teacher agency.

Beyond the recent use cases of GenAI, the past few decades have witnessed an explosion of interest in applying AI to education, from intelligent tutoring system and automated grading tools to AI-driven decision-making tools and adaptive learning platforms. There is substantial amount of evidence that shows the positive impact of using these AI applications to support students’ academic performance (Li, Gobert and Dickler, 2019; VanLehn et al., 2020), their affective engagement (Bosch et al., 2016; Calvo and D’Mello, 2010), and metacognitive development (Azevedo, Cromley and Seibert, 2004; Laru and Järvelä, 2015) in controlled experimental evaluations. Although these small-scale empirical studies provide valuable insights, their outcomes typically reflect carefully designed academic AI tools evaluated under controlled conditions in which teacher implementation is guided by researchers in high fidelity.

Alongside these promises, early scholarly and practitioner commentary raised flags about possible pitfalls. These concerns have been amplified by the rapid rise of GenAI, which brought AI’s capabilities, along with its risks, into mainstream awareness. Therefore, most educational stakeholders recently began grappling with scenarios that once seemed futuristic, or of interest to a small group of scholars.

One core concern in these discussions is the potential erosion of teacher agency. As GenAI systems begin to handle not just typical monotonous administrative tasks of teachers but also complex pedagogical and instructional decisions, such as selecting content, assessing student work, or providing feedback, teachers might find their professional judgment marginalised by algorithmic outputs. Recent empirical research is starting to document these dynamics. For instance, a recent study by Guan, Zhang, and Gu (2025) on pre-service teachers indicated that exposure to GenAI in education prompted reflections on their evolving role and anxiety about role change, highlighting the need to prepare teachers for new hybrid roles working alongside AI. Complementary evidence from interview-based research with 57 teachers across eight schools in Sweden and Australia further reveals that, rather than freeing teachers from work, GenAI often generates new forms of invisible labour while challenging their agency on the pedagogical appropriateness and social sensitivity of educational content (Selwyn, Ljungqvist and Sonesson, 2025). These findings suggest that while GenAI tools have significant potential to support teachers and teaching practices, they can, in practice, redistribute and obscure teacher labour, reinforcing rather than reducing workload anxieties and concerns over teachers’ professional autonomy.