Knowledge (Co-)Construction Among Artificial Intelligence, Novice Teachers, and Experienced Teachers in an Online Professional Learning Community

Abstract

Background: There are various challenges to teachers' use of generative artificial intelligence (GenAI) for professional learning. Although GenAI is expected to play a transformative role in teachers' learning, its impact on them remains subtle. Objectives: Guided by community of practice, this paper examines the integration of GenAI into an online professional learning community (OPLC) to facilitate knowledge co-construction among GenAI, novice teachers and experienced teachers. Methods: We used a mixed-methods approach that included topic modelling and sentiment analysis on the quantitative side and content analysis for the qualitative data. Results: We identified the top three latent themes in the OPLC's discourse—(1) generating instructional material, (2) assessment, and (3) pedagogy—and six distinct teacher-GenAI interaction profiles. For novice teachers, these included ‘engaged AI explorers’, ‘selective satisfiers’ and ‘silent strategists’; and among experienced teachers, we discerned ‘careful critics’, ‘reflective realists’ and ‘cautious contemplators’. Novice teachers exhibited technological adaptivity, while experienced ones engaged reflectively with content and focused more on students, and GenAI proved effective at providing instructional materials. Conclusions: The findings demonstrate how GenAI can contribute to knowledge co-construction, as a facilitator of rather than a replacement for human interaction.