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

Complementarity refers to AI systems functioning as supportive tools that amplify a teacher’s capabilities, while the teacher remains actively involved. In the complementarity paradigm, AI and teacher work in tandem, each contributing what they do best without necessarily interacting with each other for augmentation of each other’s competence. The underlying philosophy of complementarity is that leveraging AI’s strengths (e.g. data processing, pattern recognition, speed, scale, no exhaustion, efficiency and time-saving opportunities etc.) to complement human strengths (e.g. relational interpretation, empathy, moral discernment, contextual judgment etc.). In this paradigm, as AI primarily processes data to present insights or operationalise instructional intentions defined by teachers. It is less about AI learning from humans and more about teachers internalising computational representations and reshaping their own mental models and professional reasoning through complementary interactions with AI. Contrary to the replacement paradigm, AI systems don’t perform teachers’ tasks entirely.

If implemented appropriately, complementarity conceptualisation can further reinforce the teacher’s agency and has the potential to improve human competence at a given task. For complementarity to be operationalised, we must first articulate a human competence model to be able to specifically define what aspects of a teacher competence can be complemented with AI. Then, we can examine how the affordances of a given AI modelling technique or the design of a specific AI agent can interlock with each layer of that competence continuum. Holstein, Aleven and Rummel (2020) provide a useful framework for thinking about what specific aspects of teacher competence can be complemented with the help of AI, identifying four dimensions of complementarity: complementary goal setting (e.g. when teachers set, monitor, and evaluate learning goals with AI support); complementary perceptual input (e.g. when teachers’ perception about student learning is expanded by AI); complementary actions (e.g. when teachers’ actions are scaled by AI); complementary decisions (e.g. when teacher decision-making is assisted by data-informed AI recommendations). For instance, an AI model that has the capabilities of processing sensory information from students’ interactions can complement teachers’ situation specific skills of monitoring student interactions. On the other hand, an AI model that has the capability of tracking students’ online interactions in an intelligent tutoring system can complement teachers’ knowledge of their students’ current level of mastery on a topic. Such a teacher-AI complementarity would have the purpose of supporting rather than supplanting teachers’ integrative professional judgment. However, this framework does not provide any insights into how exactly specific aspects of teacher competence can be complemented.

Building on this notion of complementarity, it becomes essential to conceptualise how teacher-AI interactions may vary in depth and complexity. Here we propose that for any given educational task, and depending on both the specific aspects of a teacher’s competence, and the specific AI affordances, teacher-AI teaming can occur at five distinct levels: transactional, situational, operational, praxical, and synergistic teaming.

Transactional teaming refers to interactions between teachers and AI that consist of discrete transactions, defined by a request-response mechanism (i.e. the teacher inputs a command, and the AI outputs a result). Teachers and AI systems’ actions are perceived by one another, with each action dynamically informing and triggering a corresponding response from the other agent. At this level, AI agents can automatically perform actions based on teachers’ input, often completing routine or repetitive tasks on their behalf. The core dynamic of transactional teaming is “request to execution” for task automation and efficiency; therefore, the primary goal is to enhance teaching productivity by streamlining these processes. The Srivastava et al. (2021)’s Smart Learning Assistance (SLA) system can be described as an exemplar of transactional teaming in teacher-AI interaction. In this system, the teacher issues a command (e.g. inputting spoken words, sign language gestures, or Braille text), and the GenAI tool automatically converts to or from speech, sign language, or Braille, thereby returning a translated result that supports communication with the teacher or peers. In doing so, the SLA tool takes over routine translation tasks, allowing the teacher to delegate discrete, repetitive conversion work, precisely the “request → execution”, dynamic that defines transactional teaming.

Situational teaming refers to a form of interaction in which teachers operate based on a shared awareness of the teaching and learning context, constructed through the combined perceptions of both human and artificial agents. At this level, AI systems collect data from classroom interactions and/or learning activities in digital learning platforms or real-world classrooms through sensors, process it using underlying models, and provide educationally meaningful information to support teachers in making informed decisions and taking appropriate actions. The Hybrid Human- Agent Tutoring (HAT) platform developed by Sawaya et al. (2025) can be used as an example of situational teaming. The system collects data on tutors’ discourse practices, analyses it with AI models, and provides human teachers and coaches with GenAI created feedback to guide their instructional coaching sessions. This creates a shared awareness of the tutoring context, where AI highlights patterns in tutors’ interactions and coaches use these insights to make informed pedagogical decisions. The core dynamic mirrors situational teaming in that teachers and AI agents co-construct context awareness that informs human action, rather than automating tasks.

Operational teaming involves the cooperation of planning and execution of teaching-related tasks between the teacher and the AI system. At this level, teachers provide information about the current and desired states of the teaching and learning context, articulated through intentions, instructional goals, tasks, and actionable plans. The AI system supports teachers by incorporating these goals in its decision-making to autonomously perform or assist with the goals set by teachers. This teaming enables efficient task execution aligned with the teacher’s needs. However, it also requires teachers to have a comprehensive understanding of the instructional goals and the pedagogical interventions to achieve them. The Pair-Up system developed by Yang et al. (2023) can be used as an example of operational teaming. In this system, teachers articulate high-level instructional goals (which can also be done with natural language using GenAI), such as when to transition students between individual and collaborative learning activities. The AI system integrates these goals into its decision-making by monitoring student progress in an intelligent tutoring system in real time, then recommending or enacting transitions for classroom practice that align with the teacher’s pedagogical intentions. This cooperative planning and execution allow teachers and AI to jointly manage complex classroom orchestration tasks, with AI autonomously assisting in carrying out the instructional plans while ensuring that task execution remains consistent with the teacher’s overarching objectives.

Praxical teaming refers to a form of interaction in which the teacher and the AI system exchange information about actions and procedures, grounded in prior experience, usage patterns, or training. This level of teaming emphasises the development of shared “understanding” and practices over time, enabling the AI to learn from the teacher’s instructional habits and preferences, while the teacher adapts to the AI’s capabilities and pedagogical suggestions.

For example, when offering recommendations to improve teaching, an LLM-based conversational support system can learn from teachers’ feedback (both explicit and implicit) on those suggestions and adjust their underlying models accordingly. This requires teachers to possess the competence to critically evaluate the AI’s suggestions, rather than accepting them uncritically and AI to have the capability to learn from teacher corrections to adjust its internal model accordingly. The machine-learning-based feedback suggestion system developed by Bernius, Krusche, and Bruegge (2021) illustrates an example of praxical teaming. The system analyses students’ python coding script submissions in large courses and proposes feedback suggestions that instructors can review, adapt, or reject. Over time, the AI learns from teachers’ corrections and adjustments, refining its ability to generate more contextually appropriate feedback aligned with instructors’ pedagogical preferences. This dynamic exchange of information, AI adapting to teachers’ evaluative patterns while teachers critically assess and refine AI-generated suggestions, embodies the essence of praxical teaming, where shared practices and mutual adaptivity develop iteratively.