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

During our Design-Based Research process, we examined how teachers and students actually use GenAI, what challenges they face and how they envision an ideal GenAI tool. Their main challenges concerned overreliance on AI outputs, limited critical evaluation, and uncertainty about appropriate use of GenAI. Teachers emphasised the importance of and need for context-sensitive, transparent, and customisable GenAI tools that complement rather than replace their professional judgment.

Concretely, we found that teachers use GenAI for three different purposes, but especially for replacement tasks such as lesson planning, content creation or teaching recommendations. Secondary school students use GenAI in assignments that require text-generation to get further explanations in STEM-related lessons, to get additional information on a given topic and to improve their writing skills. These results are in line with the ones obtained by Laak and Aru (2024) regarding GenAI use cases for both teachers (e.g. recommendations on teaching methods) and for students (e.g. GenAI seemed more helpful in some courses rather than in others). Moreover, our study suggested that in practice teachers and students desire different AI use and automation levels based on the nature of the learning and the teaching tasks. Likewise, Brandão et al. (2024) described that different activities can lead to different GenAI use; GenAI can serve for automating trivial tasks and it can be used as a critical partner for cognitively demanding activities. This aligns with Cukurova et al. (2019) and Cukurova (2026), who emphasise that AI in education should augment human capabilities rather than replace them, a finding echoed in our own results.

In our interviews, teachers’ concerns on GenAI were focused on student cognitive offloading for core learning processes and on GenAI overreliance without developing students’ critical thinking. Similarly, prior studies (Klopfer et al., 2024; Zhai, Wibowo and Li, 2024) highlighted that an overreliance on AI can undermine students’ critical thinking. Buckingham-Shum (2024) discussed the benefits of short-term productivity that GenAI offers in minor tasks and how its uncritical use within the learning practices may impact foundational learning skills, e.g. critical thinking. Further research is needed on how to potentially reshape the teaching and assessment processes questioning what new skills and processes are important to boost in the AI age (Rethink, 2024; Miao and Shiohira, 2024; Miao and Holmes, 2023).

At the same time, many teachers expressed concerns about the impact of GenAI on their own role in the teaching process. Such concerns have been acknowledged as well by Qin, Li and Yan (2020) and Renz and Hilbig (2020) who detected several barriers to the adoption of AI, including the lack of teachers’ skills and technological competences, the potential loss of teaching autonomy and teachers’ agentic role within the learning process and data ownership. In line with these fears, in our study more than half of the students reported perceiving GenAI as a “second teacher”, always available to provide instant answers and feedback. Chan and Tsi (2024) and Giannakos et al. (2024) stressed the need to foster human expertise which encompasses students’ emotional and contextual knowledge as well as pedagogical/didactical proficiency. This is essential to provide more holistic learning interventions. Framing the teachers’ and students’ GenAI use and concerns within the Human-AI automation model, we observed that teachers’ current practices corresponded to high levels of automation in our sample, where GenAI often operates as an autonomous agent. In such cases, teachers have limited ability to control, adapt, or critically mediate the GenAI outputs to align with their pedagogical goals or specific classroom contexts. This dynamic positions teachers more as end-users of pre-configured GenAI tools than as decision-makers exercising their pedagogical considerations.

The evidence gathered from the Prototype in Scenario 2, which allowed teachers to configure how students interact with GenAI, helped to address several of the limitations of general-purpose GenAI tools discussed above, such as the lack of educational contextualisation, personalisation, pedagogical foundation and teacher autonomy. First, teachers reported that such a tool would allow them to monitor students’ progress, identify learning difficulties, and adjust feedback accordingly, thus emphasising the value of personalisation to individual learners. Second, several teachers highlighted that the prototype enhanced their sense of autonomy and control, as it enabled them to interpret and act on data within their pedagogical judgment, reinforcing rather than diminishing their professional autonomy. In our next steps, we plan to engage in the co-design of the GenAI tool in connection with teachers’ lesson plans and classroom activities, with the aim of contextualising the tool to specific curriculum and pedagogical needs.

Our study indicated that teacher autonomy is not a static condition but can be fostered via participation in both tool design and classroom co-orchestration with GenAI. We would like to acknowledge that an important dimension of teacher autonomy lies also in teachers’ decision-making actions, e.g. to reject or override GenAI suggestions, such as discarding outputs that do not align with their pedagogical context, However, our analysis in this chapter centres on the structural forms of autonomy embedded in the design and orchestration of GenAI systems. When teachers are actively involved in the tool design phase, they help embed their pedagogical values, contextual knowledge, and ethical considerations into the system’s parameters, ensuring that AI aligns with curricular intentions and classroom realities. When teachers are acting as co-orchestration partners during the instructional cycle, the foundation for a division of labour (with GenAI) over time is established: teachers determine the pedagogical tasks and when and how automation can happen.

Consequently, these two levels create a matrix (see Figure 8.7). A GenAI system may have low AI automation in enactment but still limit teacher autonomy if its design is predefined. Another GenAI system might support high automation during enactment, but still allow high teacher autonomy since its elements and aspects are co-designed. Within this dynamic, the role and autonomy of teachers can evolve in multiple directions. In some scenarios, GenAI systems risk replacing teachers by taking over core instructional functions such as content delivery, assessment, and feedback without permitting them to control or monitor the GenAI output, thereby marginalising human judgment and pedagogical expertise. In other scenarios, GenAI may complement teachers by handling some instructional tasks, enabling teachers to focus on higher-order teaching activities such as critical discussion and socio-emotional support. In some scenarios, GenAI could augment teachers, enhancing their capabilities and insights by performing tasks and generating learning insights that were previously difficult or impossible to achieve, such as real-time identification of the misconceptions of multiple individual students. This spectrum from replacement to augmentation highlights the ethical and pedagogical imperative to design and orchestrate GenAI systems that reinforce the central role of teachers in shaping meaningful learning experiences (Ley et al., 2025; Molenaar, 2024; Cukurova, 2026).

Conclusions

The acceptance and adoption of GenAI-powered systems in formal educational activities have to balance automation with teacher autonomy. Current GenAI tools rarely account for the autonomy teachers require in earlier stages of course design. Without meaningful involvement of teachers in the design of these systems, GenAI risks reinforcing predefined models of teaching and learning that may conflict with instructional and learning design principles.

This chapter discussed the importance of maintaining teacher autonomy during the design and use of GenAI in educational settings. The chapter showed how Human-Centred Design and Design-Based Research approaches can help to transition from general-purpose GenAI to educational GenAI, highlighting the need to involve stakeholders as co-design and co-orchestration partners when developing and using such systems. Human-Centred Design and Design-Based Research offer promising approaches to address this challenge. Human-Centred Design ensures that teachers and students are engaged as active participants in the design of educational technologies, while Design- Based Research involves iterative cycles of development and testing in authentic contexts.

We expanded the Human-AI automation model proposed by Molenaar (2022) to frame teacher autonomy in designing and using GenAI systems in education. Accordingly, we distinguish between two key forms of teacher autonomy in the context of AI-supported education. The first involves teachers acting as co-design partners in the creation of the AI tools themselves. In this role, they contribute their insights and express their needs regarding elements such as the user interface, functional features, and underlying infrastructure, ensuring that the tools are purposefully aligned with educational goals. The second form positions teachers as co-orchestration partners throughout the instructional cycle. Here, teachers plan, enact, and reflect on each lesson while meaningfully integrating the AI tool into their teaching practice. This level of autonomy allows them to determine how instructional tasks are distributed between themselves and the AI, shaping how the technology supports learning in their own classrooms. Our study shows, using tangible evidence, how teachers and students can be involved in the design of educational GenAI systems and what kinds of systems meet their expectations. We hope it can inspire policy-makers and EdTech developers and companies.