# Towards Synergistic Teacher-AI Interactions with Generative Artificial Intelligence

arXiv: https://arxiv.org/abs/2511.19580 · HTML: https://mlsanity.com/paper/2511.19580/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 48 references that are in the map.
- First posted on arXiv 24 Nov 2025.

## Builds on (most cited first, 12 of 12)

- [The Learnability of In-Context Learning](https://mlsanity.com/paper/2303.07895/) (2023)
- [When combinations of humans and AI are useful: A systematic review and meta-analysis](https://mlsanity.com/paper/2405.06087/) (2024)
- [Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations](https://mlsanity.com/paper/2503.04761/) (2025)
- [A Survey on Post-training of Large Language Models](https://mlsanity.com/paper/2503.06072/) (2025)
- [TeachTune: Reviewing Pedagogical Agents Against Diverse Student Profiles with Simulated Students](https://mlsanity.com/paper/2410.04078/) (2024)
- [A Novel Approach to Scalable and Automatic Topic-Controlled Question Generation in Education](https://mlsanity.com/paper/2501.05220/) (2025)
- [The Interplay of Learning, Analytics, and Artificial Intelligence in Education: A Vision for Hybrid Intelligence](https://mlsanity.com/paper/2403.16081/) (2024)
- [PromptHive: Bringing Subject Matter Experts Back to the Forefront with Collaborative Prompt Engineering for Educational Content Creation](https://mlsanity.com/paper/2410.16547/) (2024)
- [An AI-based Learning Companion Promoting Lifelong Learning Opportunities for All](https://mlsanity.com/paper/2112.01242/) (2021)
- [A Hierarchical Framework for Collaborative Artificial Intelligence](https://mlsanity.com/paper/2212.08659/) (2022)
- [Adult learners recall and recognition performance and affective feedback when learning from an AI-generated synthetic video](https://mlsanity.com/paper/2412.10384/) (2024)
- [Evaluating Trust in AI, Human, and Co-produced Feedback Among Undergraduate Students](https://mlsanity.com/paper/2504.10961/) (2025)

## Abstract (arXiv)

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.