Towards Synergistic Teacher-AI Interactions with Generative Artificial Intelligence

TL;DR

The paper addresses the challenge of integrating GenAI into education in a way that preserves and enhances teacher agency. It introduces a five-level teacher-AI teaming framework (transactional to synergistic) grounded in distributed cognition to capture the cognitive and collaborative dynamics between teachers and GenAI. Through a synthesis of AI in Education literature and a systematic review of teacher-facing GenAI tools (2010–2025), it demonstrates that current practice is heavily skewed toward transactional, situational, and operational interactions, with no observed empirical evidence of genuine synergistic teaming. The authors argue that achieving synergy requires intentional design, AI literacy, governance, and co-design processes, and they offer practical guidance for phased implementation and evaluation to move toward co-adaptation and creative outcomes beyond what either agent could achieve alone. Overall, the framework provides a roadmap to harness GenAI for augmenting teacher capabilities and classroom outcomes while mitigating risks of deprofessionalisation.

Abstract

Generative artificial intelligence (GenAI) is increasingly used in education, posing significant challenges for teachers adapting to these changes. GenAI offers unprecedented opportunities for accessibility, scalability and productivity in educational tasks. However, the automation of teaching tasks through GenAI raises concerns about reduced teacher agency, potential cognitive atrophy, and the broader deprofessionalisation of teaching. Drawing findings from prior literature on AI in Education, and refining through a recent systematic literature review, this chapter presents a conceptualisation of five levels of teacher-AI teaming: transactional, situational, operational, praxical and synergistic teaming. The framework aims to capture the nuanced dynamics of teacher-AI interactions, particularly with GenAI, that may lead to the replacement, complementarity, or augmentation of teachers' competences and professional practice. GenAI technological affordances required in supporting teaming, along with empirical studies, are discussed. Drawing on empirical observations, we outline a future vision that moves beyond individual teacher agency toward collaborative decision-making between teachers and AI, in which both agents engage in negotiation, constructive challenge, and co-reasoning that enhance each other's capabilities and enable outcomes neither could realise independently. Further discussion of socio-technical factors beyond teacher-AI teaming is also included to streamline the synergy of teachers and AI in education ethically and practically.

Figures (6)

• Figure 1: The five levels of teacher-AI teaming
• Figure 2: The transactional teaming. A teacher sends a request to an AI agent, and the AI agent then performs the requested action(s).
• Figure 3: The situational teaming. An AI agent gathers data from learning/teaching contexts. It then sends information to teachers. A teacher then combines their own observation with AI information to perform pedagogical decisions/actions(s).
• Figure 4: The operational teaming. A teacher sets a goal(s) for an AI agent in terms of tasks, action plans or parameters, and the AI agent then performs the requested action(s) according to the teacher's instructions.
• Figure 5: The praxical teaming. An AI agent provides feedback/information about learning/teaching contexts to a teacher. The teacher reviews and revises the feedback. The AI agent then incorporates the teacher's inputs to produce new feedback. The teacher and the AI agent can engage in this iterative reviewing and revising process until the teacher is satisfied with the feedback before they take actions.