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

GenAI marks a significant advancement regarding the AI affordances in the evolution of teacher-AI teaming levels by expanding the scope of complementarity across all levels of interaction. First of all, GenAI enables diversity and efficiency at a scale and precision previously unattainable. GenAI’s capacity to generate multimodal outputs (e.g. textual, visual, auditory) allows teachers to access and adapt resources to diverse learner profiles, linguistic backgrounds, and learning needs. High performance of the state-of-the-art GenAI models in content generation creates unprecedented opportunities for teachers’ transactional teaming with GenAI, allowing them to complete a wider range of tasks, from contextually appropriate lesson materials to content for students with certain impairments. Even generic GenAI systems (e.g. ChatGPT, Gemini, DeepSeek etc.), which are not designed specifically for education, can frequently support transactional and operational teaming due to their broad linguistic and creative affordances. For instance, teachers today commonly request ChatGPT to generate a quiz, summarise a text, or rephrase feedback comments. Although the interaction remains largely transactional, a request is made, and an output is returned (Handa et al., 2025): they provide significant support on a wide range of content generation tasks which were simply not possible before GenAI. The teacher’s agency in these interactions often lies in evaluating and adapting the GenAI response, but the cognitive exchange largely remains at the level of task automation and efficiency (Zhang et al., 2025).

In situational and operational teaming, GenAI provides richer situational awareness by synthesising diverse streams of classroom data (e.g. text, speech, physiological signals, interaction logs, and visual cues) into interpretable insights that help teachers make informed, timely decisions. Unlike earlier traditional AI and analytics systems that offered static or unidimensional dashboards, GenAI can translate complex multimodal data signals into narrative explanations or alternative scenario projections, allowing teachers to be better informed about their classroom contexts through multiple perspectives. Furthermore, GenAI’s language-based reasoning affordances enable it to articulate situational data processing and operational input to the model easier for co-creating plans, goals, and intentions in naturalistic dialogue rather than coding, or limited teacher authoring tools, which would be the case for traditional AI approaches.

More profoundly, the affordances of GenAI open unprecedented pathways towards praxical and synergistic teaming. Unlike traditional AI systems, which operate within fixed boundaries of prediction or classification, GenAI has the potential to engage in co-creative processes with teachers such as generating pedagogical hypotheses, critiquing lesson structures, suggesting conceptual analogies, and surfacing potential epistemic tensions in instructional design. Such a dialogic engagement, which is at the core of praxical and synergistic teaming levels, can be achieved with natural language using GenAI. GenAI also has capacity for adaptive, open-ended reasoning which can allow teachers to externalise, examine, and refine their pedagogical thinking in iterative cycles of reflection and critique. This recursive exchange has the potential to nurture professional growth, enabling teachers to question habitual practices and to explore alternative approaches that neither human expertise nor algorithmic optimisation alone could have revealed. Yet, we are only at the early stages of integrating GenAI meaningfully into teacher-facing AI tools, and the extent to which these systems can genuinely share the responsibility of meaning-making with teachers remains uncertain. It is not yet clear whether GenAI tools can co-create pedagogical meaning in a way that authentically challenges teachers’ reasoning, provokes reflection, and contributes to deeper professional learning. While their dialogic affordances hold promise for more reciprocal exchanges, current implementations rarely demonstrate the capacity to push back against inappropriate or suboptimal pedagogical decisions, to question teachers’ assumptions, or to propose alternative perspectives grounded in educational theory and evidence. Achieving such reflective tension, where AI systems not only assist but also constructively challenge teachers, still require advances in both the cognitive modelling of teaching expertise and the design of teacher-AI interaction interfaces, but GenAI systems provide unique opportunities to be explored in the upcoming years.

Conclusion

The future of GenAI in education will be determined not by how efficiently it helps automate tasks, but by how effectively it empowers teachers to exercise their professional judgment and expand their competence. The conceptual model proposed here (i.e. replacement, complementarity, augmentation) and the five levels of the teacher-AI teaming framework (transactional, situational, operational, praxical, and synergistic) provide a basis for policy and research to map existing teacher-facing AI systems and develop design principles to identify when GenAI systems replace, complement or augment teachers’ competences. The five levels of teacher-AI teaming framework proposed here can be used to evaluate AI tools against explicit teacher-agency support criteria, and track dynamic changes from transactional to synergistic teaming interactions.

It is also important to acknowledge that since synergy requires mutual interactions of two-way information flow between AI and humans, one could also speak of the internalisation of human values into AI systems as part of this process. As teachers work closely with AI, ideally, they would influence the design and tuning of these systems (e.g. through feedback, pushback on recommendations and reasons provided for such pushback, usage choices, participatory design, etc.). Over time, AI used in classrooms should learn the priorities of good teaching for a given teacher dependent upon their pedagogical philosophy, needs and requirements (e.g. promoting inquiry, not giving away answers too easily, respecting diverse solutions, prioritising certain instructional choices over others etc.) because teachers enforce these in their interactions with the AI. In a sense, the AI system learns some of the teacher’s pedagogical wisdom through these synergistic interactions. Thus, augmentation would also require appropriate spaces for the reflection of humans and space for model updates for AI. That is, teachers reflecting on how AI is affecting their practice and their students’ learning, AI using logged interaction data to update its model parameters and weighs to learn from its interactions with teachers. This two-way internalisation is at the heart of human-AI co-evolution in education for synergistic augmentation; teachers shape AI just as AI shapes teaching practices synergistically.

The synergistic augmentation paradigm represents the deepest form of teacher-AI interaction, where AI is no longer a distinct and complementary “add-on” to their competence, but part of the fabric of teaching and learning. This holds the promise of truly hybrid intelligence pedagogies that leverage AI’s capabilities while being guided by human wisdom in synergistic interactions that lead to an augmented competence that is greater than the maximum of an individual human or AI alone. It demands high levels of teacher agency to negotiate the human-AI relationship. In addition, teachers must remain self-aware, continually asserting human values and expertise in the loop even as they embrace new AI-empowered methods. Achieving success at this augmentation paradigm is challenging in education, but it aligns with the vision of education where AI serves as a competence augmentation tool for teachers, ultimately enriching the teaching and learning experiences.