Keyword: trust

ABSTRACT: This study investigates teachers’ reasoning around implementing generative AI (GenAI) in education. It aims to increase understanding of the importance of collective negotiation of trust that arises when teachers collectively explore generative AI as a pedagogical tool for teaching and learning. Data collected through a longitudinal focus group study comprising four workshops and two SWOT analyses conducted over nine months were analyzed using inductive thematic analysis. Trust was used as a theoretical lens to interpret the results. The results indicate that teachers negotiate trust in generative AI at three levels. At the instrumental level, negotiations concern the usability of the tool in terms of quality and efficiency, the reliability of different tools, and regulatory limitations on their use. At the pedagogical level, negotiations emerge overtrust in human–AI communication and interaction, focusing on linguistic precision and the importance of human presence in learning, alongside discussions about future teacher proficiency. At the systemic level, negotiations about trust occur in relation to beliefs about a future educational context and the teacher’s role within it, where reasoning about the socializing function of education becomes evident. The study emphasizes the importance of continuous professional dialogue and critical reflection to build collective trust in generative AI as an educational tool and ensure a responsible integration into the teaching practice. The research highlights the need for balanced, well-founded approaches to this integration, involving negotiations of trust across multiple levels and perspectives.