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

Introduction

Advancements in Artificial Intelligence (AI) are impacting everyday life in a variety of domains, including education, healthcare and industry (Holmes and Tuomi, 2022; Varghese and Chapiro, 2024). In the educational sector, the current and rapid (r)evolution of generative AI (GenAI) promises more accurate and personalised learning support, automation of some teaching routines and augmentation of teachers’ pedagogical actions (Holmes et al., 2022; Zhai et al., 2021). At the same time, the use of AI tools involves potential risks to human-led teaching, such as lack of human interaction between students and teachers, threats to teacher autonomy or data privacy and ethical issues (Baidoo-Anu and Owusu Ansah, 2023; Mørch and Andersen, 2023).

Against this backdrop, it is important to distinguish between general-purpose AI tools and those AI tools specifically designed for education (European Commission, 2025). Educational AI tools, such as intelligent tutoring systems or adaptive learning technologies, are developed with the explicit aim of supporting teaching and learning, offering functions like personalised tutoring, automated feedback, and curriculum-aligned content generation. On the other hand, general-purpose AI, often using GenAI-powered tools as chatbots and conversational agents, addresses a wide range of tasks across domains. This distinction underscores that while the responsible integration of AI into education should be approached with care, particular attention is needed when adopting general-purpose tools, as they are not designed with pedagogical goals in mind (Molenaar et al., 2025).

General-purpose AI, such as ChatGPT or Claude, has increasingly found its way into educational settings. Yet, as general-purpose tools they are not designed for educational use. Significant concerns with these tools are their lack of:

1. connection with the educational context and curriculum 2. grounding in pedagogical and learning theories 3. personalisation to learners’ needs 4. support of teacher autonomy.

Connecting the characteristics of the educational context (e.g. delivery setting, learning objectives and activities, teaching and assessment methods) with the technology is critical for ensuring that teaching genuinely supports learning. These characteristics are reflected in both instructional and learning design processes. Instructional design provides systematic guidance for translating curriculum and subject knowledge into coherent learning pathways and aligning objectives, methods, and assessments (Reiser, 2001). Learning design, in turn, emphasises the creation of meaningful learning experiences grounded in pedagogical theory, accounting for prior student knowledge, motivation, collaboration, and the multiple ways learners interact with content, teachers, and peers (Laurillard, 2013). However, general-purpose AI is not sensitive to differences in course structures, teaching methods, evaluation procedures, and content curricula, risking that the AI output can be irrelevant or confusing. For example, a history teacher may struggle to ensure that GenAI-generated explanations follow the national curriculum’s emphasis on critical source analysis, while a mathematics teacher may find that GenAI responses skip over foundational concepts needed by students who lack prerequisite knowledge (Mørch and Andersen, 2023).

Effective learning also relies on pedagogical principles that guide how knowledge is delivered, practiced, and assessed. General-purpose AI tools lack such grounding and typically cannot be configured to match with the pedagogical intentions of a course (e.g. scaffolding knowledge step by step, fostering inquiry-based learning). As a result, they produce content that may seem accurate or plausible but does not necessarily promote deep understanding or meaningful learning. While GenAI tools excel at providing immediate, direct answers to user prompts, the learning process itself often benefits from strategically delayed and less direct feedback, since meaningful learning is rooted in inquiry, not just in receiving the correct answers. Moreover, general purpose AI offers limited personalisation and adaptation to learners’ individual needs. While GenAI may appear adaptive, it typically lacks genuine learner modelling, which takes account of prior knowledge or enables appropriate responses to affective, motivational, or cognitive differences among students. Finally, general-purpose AI challenges teacher autonomy over the educational triangle that connects the teacher, the learning environment, and the student. That is, by mediating interactions within this triadic relationship, AI risks shifting the balance of agency away from teachers and learners toward algorithmic systems whose operations and decision-making processes are largely opaque. This can lead to a reconfiguration of pedagogical control, where instructional decisions, feedback, and even the pacing of learning lacks grounding in human interpretation, empathy, and contextual understanding. Such a shift raises critical questions about autonomy, accountability, and the preservation of human-centred education in technologically mediated learning environments.

Building on the abovementioned context, we raise a critical question for research and practice: How can general-purpose AI tools be effectively transitioned into educational AI that connects to the educational context in classrooms, aligns with pedagogical theories, supports personalisation to learners’ needs and supports teachers’ autonomy? Addressing this question is key to ensuring that AI not only generates content but also meaningfully enhances teaching and learning. Prior research has stressed the importance of Human-Centred Design (HCD) in the co-creation of technological solutions to achieve a careful consideration of instructional and learning design aspects, learning theories and teacher perspectives. These approaches actively position the educational stakeholders (e.g. teachers, curriculum designers) as co-design partners to achieve a synergy between their needs, the technological innovations and the pedagogical context (Buckingham Shum, Ferguson and Martinez-Maldonado, 2019; Dimitriadis, Martínez- Maldonado and Wiley, 2020). Nevertheless, existing literature reviews on Human-Centred Design in AI note that its adoption in actual cases is still scarce and mainly regards stakeholder involvement in initial brainstorming on the users’ needs rather than their actual participation in the design and development of tools (Topali et al., 2025; Zawacki-Richter et al., 2019).

We discuss the use of Human-Centred Design to enhance teacher autonomy, i.e. the degree of professional freedom granted to teachers to make decisions about learning, assessment and the tools available to mediate these processes (Molenaar, 2021). Within this frame we explore how to enhance teacher autonomy via Human-Centred Design with teachers participating as:

1. co-design partners during the design of an AI tool (e.g. interface, tool features, infrastructure) expected to be integrated for a particular educational purpose

2. co-orchestration partners where the teachers plan, execute and reflect on each lesson and apply the tool to their own teaching by deciding, for example, how the educational tasks can be divided between the teacher and the AI.

This chapter introduces a teacher-centred approach to the transition from generative AI as a general-purpose tool to educational AI, and stresses the importance of maintaining connection to the educational context, pedagogical objectives and autonomy of teachers during this transition. The chapter draws upon the Human-AI Automation Model proposed by Molenaar (Molenaar, 2021) to examine how teachers can be meaningfully positioned as co-actors in the design and integration of GenAI technologies. Building upon this theoretical foundation and the limited evidence on Human-Centred Design in AI, we offer empirical insights into the development lifecycle of educational GenAI systems, with teachers as co-designers, following Human-Centred Design principles (i.e. gathering initial teachers’ needs, eliciting co-design requirements and resulting in co-development). We conceptualised and designed a prototype to support teacher instructional/learning design (e.g. feedback design) and classroom enactment (e.g. capturing student-generated GenAI analytics), according to teacher needs and the current limitations of GenAI tools. Our takeaways aim to highlight the key role of educators as co-design partners to ensure that GenAI tools support pedagogical goals, classroom needs and teacher autonomy during the learning process.

Related work

Within the landscape of Technology-Enhanced Learning, researchers stress the need to adopt Human-Centred Design approaches in the design of technological innovations, so that the end-products meet the user needs (e.g. teachers, students) (Giacomin, 2014). Human-Centred Design places people’s needs, values, and rights at the core of digital design (Shneiderman, 2020). As far as educational-oriented AI is concerned, Human-Centred Design views AI-driven tools as a means of empowering learners and educators, supporting efficiency, active learning, critical thinking, and creativity (Topali et al., 2025). The approach also offers frameworks, such as Value Sensitive Design, that explicitly integrate human values into technology design (Friedman et al., 2013). Yet, there are limited examples of adopting Human-Centred Design in the design of AI and GenAI solutions (Topali et al., 2025). Examples include studies by Holstein, McLaren, and Aleven (2019; 2019), who positioned teachers as co-designers of a wearable AI tool to augment students’ monitoring in K-12 classrooms. Likewise, Lister et al. (2021) followed a participatory approach to design a virtual agent with visual-disabled students assisting them within the context of distance learning.

When it comes to general-purpose GenAI, there are a few studies that implemented Human-Centred Design at the initial design stage by understanding stakeholders’ perspectives in K-12 education. For instance, Han et al. (2024) interviewed primary school teachers and students, and found that GenAI could be beneficial in personalising the learning experiences and providing instant feedback to them. The authors indicated concerns over data authorship, lack of critical thinking in the case of hallucinations and students’ and teachers’ autonomy. Likewise, Hays, Jurkowski, and Sims (2023), Kaplan-Rakowski et al. (2023), Laak and Aru (2024) and Monteiro et al. (2024) shed light on teachers’ viewpoints on the use of ChatGPT for educational purposes. In most studies, teachers reported that ChatGPT might be beneficial for students, but they regretted their lack of control on the answers given, and had concerns about the privacy of the data and who can analyse it and about the lack of contextualisation. For instance, Prestridge, Fry, and Kim (2024) interviewed ten secondary school teachers to understand the potential added value of GenAI in their courses and they stressed the importance of considering the different course contexts to use GenAI meaningfully. These studies also discussed the need for teachers' professional development, both to provide guidance on how to use GenAI for education and as a means to address feelings of fear and replacement by AI.

The above studies provide important insights about teacher and student needs regarding the use of GenAI for teaching and learning, such as the importance of teacher control over the GenAI generated learning content. However, most studies simply focus on teachers’ general perceptions about the use of only ChatGPT via surveys with Likert scale items, or short-answer formats (e.g. Hays, Jurkowski and Sims, 2023; Monteiro et al., 2024). They do not extract design guidelines for the development of educational-oriented GenAI tools and do not position the stakeholders as co-partners of such development and integration processes. One exception is the study by Han et al. (2024), which limits its focus to a GenAI writing tool for primary education. Table 8.1 summarises the current state of the literature in this regard. In all studies, to the best of our knowledge, there is a lack of evidence that Human- Centred Design processes informed the design of a GenAI system based on participants’ requirements and actively involved them as co-designers.

Kaplan-Rakowski et al. (2023)

Interviews/ Han et al. (2024)

Monteiro et al. (2024)

Laak and Aru (2024)

Prestridge et al. (2024)