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

Augmentation refers to the process by which AI tools, and the new practices they enable, become woven into the internal repertoire of teachers in ways that these human-AI interactions also lead to an increase in teacher competence. This very much corresponds to the fifth level of the teacher-AI teaming framework.

Synergistic teaming refers to a form of interaction in which the teacher and the AI system mutually enhance each other through critical evaluations, challenging to each others’ suggestions and propositions with logic and evidence, and engage in solving complex problems together to move towards a shared understanding and mutual development. Effective synergistic teaming involves mutual interaction, where the AI agents and teachers evaluate each other’s claims with epistemic awareness and remind one another of aspects that may have been overlooked. When this interaction is well-aligned, a form of creative resonance emerges, enabling the teacher and AI to deepen their understanding of the task and generate outcomes neither could achieve independently. Therefore, this type of teaming is conceptualised as synergistic, that is, the emergent competence is likely to exceed the maximum of individual AI or human competence at a given task (Cukurova, 2024). The main limitations of praxical teaming in comparison to synergistic teaming lie in its reliance on the existing knowledge, expertise, and competence of the teacher, without necessarily pushing them beyond their current practices. As such, praxical teaming tends to converge at the maximum of the teacher’s present competence, whereas synergistic teaming requires a more ambitious, mutual development between AI and teachers, a state that remains far more difficult to achieve in practice. Thus, while praxical teaming often stabilises at the ceiling of teachers’ or AI’s current max performance with the potential benefits of efficiency and time-savings, moving toward synergistic teaming, where AI and teachers mutually extend and transform one another’s capacities, is more difficult to realise.

The main condition for augmentation in this sense is synergistic human-AI teaming, where human-AI combination yields emergent competence exceeding the maximum of individual AI or human competence alone at a given task.

However, this idea, informed by theories of distributed and extended cognition, faces significant empirical hurdles. A recent comprehensive meta-analysis of 106 experimental studies covering all sectors, with ~370 effect sizes reported, reveals that human-AI combinations underperform the better performer, human or AI alone, in 58% of the cases reviewed (Vaccaro, Almaatouq and Malone, 2024), demonstrating that synergy is context-dependent and should not be assumed. Critically, synergy emerges only under specific conditions. For instance, task type matters. Synergy is more likely to occur in relatively simple content creation tasks (e.g. open-ended science education questions) but fails in complex decision-making tasks (e.g. classification of a student’s emotional state given their relationship with other students). Second, when humans outperform AI alone, synergy is more likely to arise; yet when AI outperforms humans, human-AI teaming tends to degrade the performance. This indicates that synergy depends on human competencies to meta-cognitively assess when to trust AI input and when to ignore it which emphasises the importance of improving teachers and students’ AI competencies to be able to protect their own agency and have a critical and informed trust in AI (see, for instance, UNESCO AI competency framework for teachers (Miao and Cukurova, 2024). Third, the instructional design in which AI is interacted with is very important. The evidence from the meta-review indicates that only 3 out of 106 experiments explicitly tested predetermined subtask delegation between humans and AI to structure their interactions. These yielded non-significant synergy gains, underscoring that effective augmentation requires systematically scaffolded human-AI interactions embedded within a well-designed instructional framework.

These findings affirm that synergistic augmentation is difficult to achieve across many domains including healthcare, law, and education. They suggest that future AI implementations in education aiming to realise augmentation through synergistic interactions should prioritise task-specific AI design, development, and deployment, rather than relying on generic tools such as ChatGPT, which are not inherently educational, or human development, technologies. While such general-purpose systems can be valuable for exploratory or creative use, their outputs are not pedagogically grounded nor optimised for instructional alignment. Achieving meaningful synergy therefore requires the deliberate scaffolding of teacher-AI interactions within context-specific educational frameworks and the provision of AI competency training for teachers to help them unlock emergent forms of teacher-AI teaming. It is equally important to establish robust evaluation metrics that capture what specific aspects of teacher and AI competence are being augmented at a given task. Without these intentional design and assessment mechanisms, teacher-AI teaming is unlikely to achieve synergy and risk producing diminishing returns compared to standalone human or AI performance. The five-level framework proposed here examines the teacher agency through the lens of AI system affordances and interface-level design considerations. The system affordances discussed above therefore are necessary, yet not sufficient conditions for teacher-AI synergistic interactions. Box 7.1 presents examples for the five different AI-teacher teaming presented above.

In order to illustrate the spectrum of teacher-AI teaming in more concreate terms, here the levels are described through the case of a multimodal AI system designed to diagnose students’ collaborative group challenges and group interactions in higher education contexts (Suraworachet, Zhou and Cukurova, 2025). The purpose of this tool is to support teachers’ situation-specific skills of monitoring students’ group interactions, enabling more accurate diagnosis and timely interventions in students’ group work activities.

1. Transactional teaming: At its most basic, the system functions as a machine-learning classification tool that automatically categorises students into high, medium, and low levels of collaborative problem-solving competence using multimodal sensor data (Spikol et al., 2018). Here, the AI executes a discrete classification task in response to teacher data input of students’ interaction logs, audio and videos, streamlining routine diagnostic work but not actively shaping pedagogical practice.

2. Situational teaming: At this level, the system integrates GenAI models to detect challenge dimensions from student dialogue (Suraworachet, Seon and Cukurova, 2024) and computer vision techniques to analyse non-verbal group interactions (Suraworachet, Zhou and Cukurova, 2025) and visualise insights from these AI models on a teacher-facing dashboard. These insights create a shared awareness of classroom dynamics, aiming to complement teachers’ perception of group processes and helping them recognise moments that may warrant intervention. These visualisations for instance can indicate students’ active listening times in group activities, types of group interactions they are engaged in, and diagnosed challenges (i.e. cognitive, emotional, metacognitive or technical) as well as to what extent these challenges were resolved by the group.

3. Operational teaming: At this level, the system begins to co-operate with teachers in the planning and execution of instructional tasks by operationalising teacher-defined goals into actionable rules and parameters. Teachers specify priorities, such as fostering equitable participation or scaffolding metacognitive processes, and these are translated into threshold values, conditional rules, and automated prompts. For example, a teacher may configure the system so that if a student has not contributed during the problem-scoping phase for more than a specified number of minutes, the dashboard generates a nudge to encourage participation. Similarly, if the models detect that a group is struggling with task-specific metacognitive challenges, the system can intervene using GenAI created outputs by suggesting students revisit the activity description and clarify their immediate objectives. In this way, the dashboard functions as a co-orchestration partner, aligning its feedback and interventions with the teacher’s declared pedagogical intentions while retaining the teacher’s overarching authority and professional judgment.

4. Praxical teaming: At this level, the interaction between teachers and the system evolves into the development of shared practices, where AI system learns and adapt through sustained use. Instead of system generated GenAI feedback being delivered directly to students, the dashboard provides visualisations and tailored recommendations first to the teacher. Teachers then critically review these suggestions, revising or editing them, and may add further recommendations based on their professional judgment. These modifications are logged and processed by the AI system, which incorporates them into its knowledge base to iteratively refine its outputs. Over time, this process enables the AI to better converge with the expectations and pedagogical needs of the teacher. Nevertheless, the developmental trajectory of praxical teaming remains bounded by the teacher’s existing expertise, as the AI primarily adapts to current practices rather than actively pushing teachers beyond their established competence. In contrast, synergistic teaming requires a deeper mutual transformation, where both the teacher and AI extend one another’s capacities to co-create new practices and outcomes that surpass the competence of either agent alone.

5. Synergistic teaming: At this level, teacher-AI teaming evolves into dialogic co-construction of educational practice. An additional AI agent monitors teacher interactions with the diagnostic and feedback system, critically evaluating teacher-generated recommendations against learning sciences principles, historic interaction data, and prior teacher edits. Instead of passively accepting teacher revisions, the system engages the teacher in negotiation, prompting reconsideration of overlooked dimensions or alternative strategies. Through this dialogic exchange with a GenAI agent, both teacher and AI agents evaluate one another’s claims and mutually extend their reasoning. When well aligned, this results in a form of creative resonance in which the teacher’s pedagogical expertise and the AI’s analytic and evaluative capacities intertwine to generate novel solutions, interventions, or instructional designs that neither could achieve alone. This is conceptualised as synergy, an emergent competence that exceeds the maximum of either human or AI performance individually.

Empirical evidence from recent studies underscores the tangible benefits of praxical and synergistic teacher-AI teaming in enhancing both teacher efficacy and student learning outcomes. For instance, in an auto grader research study, a praxical teacher-AI teaming approach was shown to reduce grading time by 44% while improving accuracy by 6% compared to manual grading (Liu and Liang, 2024). Teaching assistants consistently rated the AI-assisted process as faster, easier, and more enjoyable, reporting that automation alleviated routine cognitive burdens and allowed greater focus on higher-order pedagogical reasoning. Similarly, a teacher-AI feedback co-creation study demonstrated that involving subject matter experts in GenAI-supported content authoring can yield comparable instructional quality to human-only materials, while dramatically reducing time and cognitive effort (Reza et al., 2025). Involving ten mathematics experts and 358 learners, Reza et al. (2025) show that an iterative human-AI approach to co-producing feedback reduced perceived workload by 50% and shortened the content development process from several months to a few hours, while maintaining statistically significant learning gains for student. Together, these findings confirm the empirical potential of GenAI to support more advanced teaming approaches to amplify teacher productivity, improve instructional quality, and sustain learning outcomes when human oversight and agency remain central to system design.

However, both studies caution that augmentation is not without limitations. In the Liu et al. (2024) study, AI performance degraded on unrepresented cases, revealing the dependence of system reliability on training data diversity and the need for ongoing human teacher verification. Similarly, participants in the Reza et al. (2025) study reported occasional model unpredictability and difficulties in steering GenAI outputs, emphasising that the quality of augmentation depends on teachers’ prompt literacy and capacity for meta-cognitive regulation. Moreover, lesson-specific variation in learning outcomes indicated that GenAI may underperform in certain pedagogical contexts or with particular learner profiles. Finally, neither of these studies measured improvements in teacher competence before and after their interactions with the tools to evaluate the impact of these interactions on teacher competence.

These findings collectively suggest that augmentation benefits are contingent upon interface design features, structured scaffolding of praxical and synergistic interactions, and the particular competence and motivation of teachers. Whatever the affordances of teacher-AI teaming might be, without sufficient teacher competence and motivation to engage meaningfully with these tools, the likelihood of achieving augmentation remains low. Furthermore, some teachers can indeed achieve competence gains even in their transactional teaming with AI tools, when these are used reflectively and purposefully. This also illustrates that the five levels of teacher-AI teaming are not hierarchically ordered stages of progression but rather context-sensitive modes of interaction. Different educational tasks, disciplinary demands, and institutional and local contexts may indeed benefit from different levels of teaming. Nonetheless, the design of teaming affordances that allow higher degrees of teacher agency (e.g. operational, praxical, and synergistic) represents a deliberate effort to maximise the likelihood that teacher-AI interaction contributes to further enhance teachers’ agency and competence development. These higher-order forms of teaming are not inherently superior, but they are structured to provide richer opportunities for reflection, adaptation, and pedagogical transformation. In this sense, augmentation is not a property of technology alone but an emergent outcome of the dynamic interplay between teacher competence and motivation; the design of human-AI complementarity interface; and the affordances of the AI models.