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

The design requirements derived from the interviews led to the development of a low-fidelity prototype under three different scenarios (see Table 8.4 and below). The GenAI could be finetuned by developers to better support classroom use while maintaining teacher autonomy. This prototype was then used in other co-design sessions with additional teachers to confirm the elicited requirements and to modify it according to their preferences and needs.

Prior to these sessions, the prototypes were fed with fictional data supporting the different design requirements described before (e.g. GenAI analytics about students who asked for more exercises, students who copy-pasted the given answers to course assignments). During the sessions, teachers interacted with the prototype simulating three scenarios at different teaching moments: pre-course design configuring and contextualising the GenAI chatbot; course monitoring and reflection on the GenAI analytics captured from the interactions of the students with the chatbot; and after-course reflection and assessment of students’ submissions and of the employed teaching methods.

Afterwards, teachers were asked to complete several surveys with both close and open-answer questions to assess the integration of the prototype, its usefulness, its usability and potential adoption in their regular practice. Further information about the low-fidelity prototype and one of the co-design sessions can be found in Ortega-Arranz et al. (2025).

Each scenario (see Table 8.4) aimed to address 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. This was also about filling gaps: a lot of AI tools mainly focus on course enactment and offer limited educational tools supporting teachers in course design and after-course assessment or automatically generating learning tasks and feedback interventions that are context aware (Delgado Kloos et al., 2025; Topali et al., 2025).

The prototype enables teachers to configure the behaviour of the system when the students

Figure 8.4 left interact with the chatbot. This includes contextualising the answers with documentation, 1 and configuring the answers according to different teaching methodologies such as the

Course Students Figure 8.4 right desired feedback depth (e.g. providing hints enactment versus direct answers).

First insights on the low-fidelity prototype

The teachers actively contributed to shaping the prototype according to real-case scenarios and their own teaching contexts. They offered valuable feedback on what felt promising as well as areas that need further refinement. Overall, their impressions were positive, and the collaborative process highlighted both the opportunities and challenges in co-designing GenAI for educational contexts.

A key outcome of the GenAI co-design approach was that teachers wanted to remain actively present as co-orchestration partners with the GenAI tool throughout the instruction process. This co-orchestration unfolds through a deliberate division of labour over time. During the lesson planning and preparation phase, teachers set the pedagogical parameters within which the GenAI operates, defining learning objectives, instructional strategies and assessment criteria. This ensures that the system’s functions remain grounded in the teacher’s pedagogical judgment and contextual understanding of the learners. During the lesson enactment phase, the GenAI can execute predefined tasks, such as monitoring student progress or providing adaptive feedback, allowing teachers to redirect their attention toward more advanced pedagogical responsibilities, including facilitation of critical discussion, individualised mentoring, and emotional support.

A. “It would help me to see what exactly the students do and where they might still need support

B. “You could see if students can master the material with the right

C. ¨I think that students are more focused when working, then I can also give more targeted feedback¨

A. “By getting insights about how the students use the tool, you can give better feedback and directions to the individual students.”

B. “It provides tools to better coach individual students, or to address possible misconceptions and ambiguities in the learning material in a more targeted way?”

A. “It allows me to monitor many more individual students and therefore also guide them better. That tool makes me a more competent teacher”

B. “I can control better a learning situation that includes GenAI”

C. “Because it helps with awareness and goal-oriented use of GenAI, within a controlled environment. “

A. “Although it doesn't become easier due to the new options, it does become better”

B. “It gives a clear and insightful overview of the prompts and how GenAI is

used, but at the same time it results in increased workload”

C. “It gives you a lot of insight, but at the same time it does not make your job faster”

Regarding concrete findings, while we explored the three scenarios, in this section we will only focus on Scenario 1 on helping teachers monitor the student-GenAI interactions. Here are the identified findings:

• The prototype could help teachers to understand their students’ moments of progress and adjust the feedback accordingly. Seven teachers mentioned that such a tool would support their effectiveness by gaining insights on student progress, the methods they are applying and the moments they are struggling. It would help them know when and what type of support to provide (Table 8.5 [Insights]). Two teachers stressed the added value of personalising the support to individual student needs (Table 8.5 [Personalisation]).

• The prototype could enhance teachers’ feeling of autonomy and control over AI. Five teachers noted that the envisaged tool could foster their autonomy and increase their level of control within learning situations involving GenAI tools (Table 8.5 [Autonomy]). During the co-design session they proposed further ideas for GenAI analytics to support their level of pedagogical oversight of their students, such as “an overview of cohorts of students that are and are not efficient in working with AI”, “group report on students who work together using GenAI”, “aspects that are changing notably about the student progress while they are using GenAI for a period. If you could get that in a file you could also share and re-use”, “a report on GenAI prompt use or progress throughout the week”.

• Providing more insights to teachers could add more complexity. Four teachers discussed the added value of the tool in terms of awareness of student progress compared to the extra complexity or workload that it adds (Table 8.5 [Complexity]). Several teachers stated that the tool might add cognitive load or time to their tasks, yet it would also offer insights to understand their students and help them stay in control over AI.