Entangled learning : an exploration of pre-service teachers’ professional learning in an AI-mediated world

Tools

Huang, Alan (2025) Entangled learning : an exploration of pre-service teachers’ professional learning in an AI-mediated world. In: European Conference on Educational Research 2025, 2025-09-09 - 2025-09-12.

[thumbnail of Huang-ECER-2025-an-exploration-of-pre-service-teachers-professional-learning-in-an-AI-mediated-world]
Preview
 Text. Filename: Huang-ECER-2025-an-exploration-of-pre-service-teachers-professional-learning-in-an-AI-mediated-world.pdf
Final Published Version
License: Strathprints license 1.0
Download (173kB)| Preview

Abstract

With the rapid rise of Generative Artificial Intelligence (GenAI), there has been a dynamic shift in education and teacher education research (OECD-Education International, 2023). We now live in an AI-mediated world where teachers and learners in Europe and around the globe have unprecedented access to a plethora of AI-enabled digital tools which offer considerable opportunities for innovation alongside risks (EU AI Act, 2023). GenAI technology, including ChatGPT, builds on three fundamental pillars: big data, deep neural network models and large language models (Nah, et al., 2023). GenAI tools not only allow users to interact with them dialogically, but they can also create multimodal representations in the form of images, videos and audio. In the context of teacher education, however, there has been limited research on how teachers learn in relation to AI (Sperling et al., 2024) or, more specifically, GenAI. Sperling et al.’s (2024) scoping review on AI literacy in teacher education highlights the importance of involving teachers in exploring the practical dimensions of AI literacy and the situated/contextual nature of teachers’ knowledge in this domain. Similarly, Celik et al.’s (2022) systematic review on the promises and challenges of AI for teachers conclude that relatively little research has explored the potential of AI in teacher education. More recently, Moorhouse and Kohnke (2024) investigated thirteen language teacher educators’ perspectives on GenAI through in-depth interviews. Their findings reveal that participants believe that GenAI will have a significant impact on teacher education curricula, instruction, and assessment. The study also highlights teacher educators’ perceived lack of confidence and competence in using GenAI in practice. This paper is concerned with how pre-service language teachers learn to use GenAI from the perspectives of sociomaterialism and sociocultural theory. Rather than viewing learning as knowledge and skills isolated within individuals, this study positions learning as socially distributed (Fenwick, 2015) and mediated by cultural artefacts working as symbolic tools (Wertsch, 1998; Lantolf, 2000). Leonardi (2013) defines ‘the social’ in sociomaterialism as ‘abstract concepts such as norms, policies, communication patterns, etc,’ and ‘the material’ as ‘the arrangement of an artefact's physical and/or digital materials into particular forms that endure across differences in place and time (p. 74).’ Teacher professional learning, therefore, can be understood as the entanglement of all elements and actors within these sociomaterial assemblages (Fenwick, 2015). A key construct that underpins this research is the role of (technological) mediation, i.e. the co-construction of meaning/knowledge through engaging with GenAI. In other words, GenAI is transforming how we learn and communicate by creating new dialogic spaces for human and nonhuman to learn together (Wegerif, 2007; Huang, et al, 2023; Yan, et al., 2024). To this end, this study aims to address the following research question: how can working with GenAI help pre-service teachers create space for professional learning in an AI-mediated world?

ORCID iDs

Huang, Alan ORCID logoORCID: https://orcid.org/0000-0002-9535-6426;
CORE (COnnecting REpositories)