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

Given the limited application of the full cycle of Human-Centred Design in general-purpose GenAI, we deem that GenAI tools designed without teachers’ pedagogical considerations may inadvertently hinder their autonomy, since teachers have to deal with AI-generated outputs that do not align with their pedagogical intentions. Indeed, prior research indicated that while ChatGPT and other GenAI tools are nowadays used by most students in higher education (Xiaoyu, Zainuddin and Hai Leng, 2025), they limit teacher control during classroom practices (Chapwanya, 2025). For instance, if a teacher proposes that their students use ChatGPT to help create an essay, the teacher may not be able to monitor what the students are asking the chatbot, what feedback is given to them and/or when such prompts or answers need to be corrected, thus leaving aside teacher knowledge/expertise from the learning situation.

Although initially introduced to discuss how educational AI affects teacher control, the Human-AI automation model (Molenaar, 2022) can also be applied to understand different levels of automation in learning situations using GenAI systems. This model articulates the transition of control between teacher and intelligent technology through six levels of automation during the teaching and learning practice (Figure 8.1), i.e. from “teacher has full control” (left), to “technology has full control” (right). Projecting the model into GenAI usage, in the second level (Teacher Assistance), we have cases where teachers have full control over the learning situation (e.g. teaching methods, feedback on course assignments) and GenAI tools propose additional information, explanations, examples, or text snippets for the teacher to implement in their lessons, improving teachers’ existing instructional design. In the third level of the model (Partial Automation), teachers give part control of specific tasks to GenAI. For instance, GenAI highlights common student errors and provides an overview to students highlighting which errors they have made. The teacher can then use this list in a class discussion of errors and elaborate on how to resolve them. In the next levels of the model, advancing towards the right side, the AI takes almost full control largely acting independently. For instance, GenAI tools such as platforms like Synthesis Tutor where GenAI generates content, feedback, and assessments dynamically for each student, without expecting any teacher intervention.

An apparent limitation of this model is that it does not integrate the entire instructional cycle in which teachers plan, execute, reflect on, and revise their lessons. The model focuses mainly on actions performed during teaching, while it is equally important to consider teacher autonomy and control during course design and refinement. In the planning phase, critical instructional and learning design decisions are taken about sequencing, scaffolding, and alignment with learning objectives, pedagogical tasks and teaching methods. In the refinement process, teachers continually improve their lessons.

Let us consider the case of a GenAI-feedback tool: during the instructional cycle, the teacher specifies the assessment criteria, the type of feedback that should be emphasised (e.g. formative comments on argumentation rather than grammar), and the depth or timing of responses that are appropriate for their students. Based on this instructional plan, the functioning of the GenAI can be refined. Now the general-purpose AI tool is embedded in an instructional cycle, which predetermines how the GenAI tool operates. This allows for a division of labour in which the teacher retains complete control over the instructional plan and functioning of the GenAI, while the GenAI supports the teacher with high automation during the enactment of the lesson, generating feedback for student submissions in real time without the teacher’s direct intervention. In this scenario, teacher autonomy is exercised during the orchestration cycle of the instruction (in this context, orchestration refers to the general management of the learning activities). This means that the teachers can adjust the system requirements of the GenAI tool given their educational context to ensure that they continue to determine the pedagogical framing of the feedback, while automation during enactment reduces teacher workload and ensures consistency.

Drawing upon these reflections, the Human-AI Automation model presented above can be extended to understand teachers’ autonomy within GenAI-powered systems with respect to design and orchestration decisions. Teacher autonomy is not only exercised through control of AI during the course enactment, but also during the course design and instructional cycle. Accordingly, we conceptualise two different levels of autonomy where teachers act as: 1) co-design partners during the AI tool design (e.g. voicing their needs regarding the interface, tool features, and tool infrastructure) and 2) co-orchestration partners in instruction cycle where they plan, execute and reflect on each lesson and apply the tool to their own teaching, deciding for example how the educational tasks will be divided between the teacher and the AI.

The development of a GenAI prototype using design-based research and participatory approaches

Reflecting on these different levels of autonomy for teachers and the constraints in the general-purpose AI tools, we aimed to explore how we can support the transition from general AI technologies into education-oriented tools. The study presented in this chapter is part of a project addressing the following research question: To what extent can we enhance teacher autonomy by positioning them as co-design tool partners in the transition from general-purpose AI tools into educational AI? To answer the research question, we followed Human-Centred Design, by giving teachers the possibility to voice their needs when it comes to GenAI use, as well as their pedagogical goals and classroom practices. This emphasis on teacher input supports the development of GenAI as a co-design partner rather than a one-size-fits-all assistant.

This section first presents the process of understanding teachers’ and students’ needs in GenAI usage and then the ideation and low-fidelity development of a GenAI chatbot with extended functionality to foster teacher autonomy for secondary education. To guarantee that the GenAI system satisfies the learning and teaching needs, we followed a Design-based Research (DBR) methodological approach (Amiel, 2008) (Figure 8.2). Accordingly, we further divided the research question into the following sub-questions that were addressed in each design-based research phase (see Figure 8.2):

1. How do teachers and students use GenAI? 2. What limitations and challenges do teachers and students face when using GenAI? 3. How do teachers perceive the requirements for GenAI tools identified from prior interviews? 4. How do teachers perceive the added value and potential adoption of the proposed GenAI tool features in their educational contexts?

Our research design followed a qualitative phenomenological process (Creswell, 2013) to get from: 1) general problem understanding on GenAI usage (questions 1 and 2, Figure 8.2) to 2) requirements’ validations of teachers’ main needs (question 3, Figure 8.2) and 3) low-fidelity prototyping of a GenAI system satisfying such needs (question 4, Figure 8.3).

During Phase 1, we conducted a set of face-to-face, semi-structured, one-to-one interviews with ten secondary school teachers and 12 students. In Phase 2, we conducted a focus group with nine teachers to reflect upon teachers’ requirements (as collected from Phase 1) for using GenAI for secondary education. These data collection approaches were chosen to help us understand in-depth teachers’ and students’ needs when using GenAI. Lastly, in Phase 2 we also conducted three co-design events with eight teachers, each time working with the requirements gathered from the focus group towards the design of a low-fidelity prototype of a GenAI system (see Table 8.4). During Phase 2, teachers worked with three different scenarios for the prototype addressing different teaching moments (i.e. course design, course enactment, after course reflection and assessment) taking into account the complexity and nature of the teaching process.

Prerequisites for participants to participate in this study were the previous use of GenAI tools for formal teaching and learning purposes. Teachers often face difficulties in connecting the course learning design and their teaching needs with the desired data-driven information about the student (Mangaroska and Giannakos, 2019). Thus, we employed a set of techniques to better guide our teachers in their role as co-designers and support them during this process. For example, we conducted interviews and a focus group to understand teachers’ current use of the existing GenAI tools. Additionally, we followed the “superpower” approach proposed by Holstein, ΜcLaren and Aleven (2019; 2019), asking teachers about the “superpowers” that an ideal GenAI tool could support. During the co-design events we used “speed-dating” processes and prototype simulation exercises, to discuss the use of GenAI in relation to the actual learning scenarios of the teachers.

The data sources employed in the interviews were the stakeholders’ recordings about the perceived GenAI challenges, added value and actual use cases, answers to a profiling questionnaire and artifacts (post-it notes with additional ideas). In the focus group, the data sources regarded mainly a profiling questionnaire and the generated artifacts (post-it notes with challenges and superpowers). Content analysis of the collected data was employed utilising inductive coding, i.e. categories derived from participants’ answers. For instance, we extracted the following categories based on participant answers on their current use of GenAI:

1. GenAI for replacing current learning and teaching tasks [Replacement] 2. GenAI for complementing current learning and teaching tasks [Complementarity] 3. GenAI for supporting learning both for teachers and students [Learning].

Information about the stakeholder groups and their characteristics is presented in Annex 8.A.