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?

This exploratory study will collect data from second year pre-service language teachers on an undergraduate programme at a Scottish university. The qualitative research adopts an interpretivist paradigm, which aims to understand and investigate the richness and depth of issues in a specific context (Cohen, et al. 2018). Approximately 10 participants who have chosen the module Digital Technologies in Language Education will be recruited. To minimise the possibility of coercion, participant recruitment will begin after they have completed the module and received their grades in mid-April 2025. As this is the fifth iteration of the module, examples of artefacts submitted by former students will be shared with participants to contextualise the task. In this optional module, students learn to create a digital story using Digital Multimodal Composing (DMC) - an overarching term that refers to the use of digital tools across multiple modalities to construct meaning (e.g. Hafner and Ho, 2020; Kessler, 2024). The task allows pre-service teachers to express meanings that go beyond the use of language in a traditional written essay. Throughout the module, I provide students with various forms of formative feedback. Students will GenAI use in the DMC process, thus enabling materiality to contain agency through its participation in the teacher education assemblages. Data will be collected through semi-structured interviews conducted in person or via Zoom based on participants’ preferences. Each interview will last 30 to 40 minutes and explore the following topics: - Pre-service teachers’ experience with GenAI, - Their existing beliefs and attitudes toward the technology, - Their understanding of the relationship between human and nonhuman actors in a complex interdependent professional learning environment. The data will be analysed using inductive thematic analysis (Braun and Clarke, 2006; Saldaña, 2011) where I seek to draw out key ideas by identifying recurring themes. After an initial reading was made to become familiarised with the data, the data will be grouped together to form hierarchies of codes and themes. Themes are then categorised into similar clusters and to develop theoretical constructs. The analysis will focus on the interrelationship (entwinement) of the social and material, aiming to understand how it is formed and the ways in which teacher professional learning relates to this emerging technology.

The findings will provide insights into the perceptions of pre-service teachers and contribute to a better understanding of the way in which they engage with the development of GenAI in their professional learning. Adopting a more critical stance towards this new technology, the study aims to capture its situated nature and how it is enacted in practice. Expected outcomes include - A better understanding of the relationship between GenAI and teacher professional learning in pre-service teacher education, with implications for teacher education more broadly, which will maximise the potential of GenAI as a learning tool; - Practical approaches to integrate GenAI in initial teacher education programmes such as: 1) encouraging pre-service teachers to reflect on meaning-making practices as they engage with GenAI and other multimodal digital tools, 2) shifting the focus from teacher cognition to the role of ‘the social’ and ‘the material’ in order to develop pre-service teachers’ adaptive and critical digital competence. The study will also have implications for the redesign of teacher education curricula and other teacher professional development programmes in an AI-mediated world at the local, national and international levels. By exploring the sociomaterial and sociocultural perspectives of GenAI use in teacher education, the research will inform policies and practices that address both the opportunities and challenges posed by AI in education.

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