A Segue From Search to Dialogue: Leveraging GenAI for Pre-Service Teacher Training

Introduction

The rise of Generative AI (GenAI) marks a milestone in the evolution of Large Language Models (LLM) based on Artificial Intelligence with its already “long history and philosophy” (Bozkurt et al., 2023, p. 54). This development challenges and disrupts the processes of professionalisation in Higher Education profoundly. Hence, while general public discourse regarding the impact of GenAI has ranged from anxious to enthusiastic (Tlili et al., 2023), the practical and theoretical implications need more clarification. This chapter focuses on exploring and explaining these ideas in relation to the future of teaching and learning for pre-service teacher (PST) students.

What distinguishes GenAI from other digital technologies is that it “adopts supervised and reinforcement learning techniques”, enabling interaction with ‘intelligence’ and information (Bozkurt et al., 2023, p. 54). By this, it sheds new light on conceptions of knowledge and skills and thus leads to questions of how to conceptualise these terms. This affordance is distinctive because it signals a diminishing of the dominance of the 'search paradigm' of inquiry (Mason, 2023). As such, this technology is poised to advance our understanding of how knowledge and skills can be produced in dialogue and how such knowledge and skills might be co-produced. Focusing on higher education pre-service teacher training opens pathways for simulated training and reflection on engaging in conversations (Mason et al., 2020).

Teachers' interaction skills primarily address the interaction with students. However, among the major challenges of (novice) professionals are interaction with parents, colleagues, principals, and others, which usually are not the subject of preservice teacher education. In addition, these interactions become more complicated when the interaction partners' backgrounds differ from one's own. These different backgrounds may include culture, gender, majority/minority relations, or class. Choosing a field of interaction where students have little experience and perceive it as a challenging part of becoming a teacher (Preidel et al., 2009) while the relevance of the interaction between teachers and parents is pronounced from several perspectives (Lin et al., 2019; Rattenborg et al., 2019; Kraft & Rogers, 2015), this field of study gives insights into the leverage of using GenAI for training. Simulation training connects theory and practice, leading student teachers to discover its usefulness for breaking down tasks, addressing confusions and ambiguities in theories and teaching pedagogies, and understanding subtle nuances in the learning process.

The current literature often shows a positive impact of GenAI in public debates (Tlili et al., 2023) but also marks that “the negative narratives imply that there are critical issues to resolve before fully adopting these technologies” (Bozkurt et al., 2023, p. 59). This is especially the case for the co-construction of students' learning processes, allowing students to define (and also be responsible for) their learning pathways (Vespone, 2023). Here, GenAI marks out new ground but also invites new “craftsmanship” to design sufficient prompts that fit the learning goals and, in the same step, address the AI bot adequately (Bozkurt & Sharma, 2023).

Building upon the literature that digital technology can serve as a tool for collaboration, GenAI has evolved into a catalyst for promoting intercultural learning within teacher training environments, affirming the significance of intercultural learning for future education – a necessary doctrine that exposes students to diverse cultures, thereby enhancing their intercultural competence and cultural intelligence (Hackett et al., 2023; Bolaji & Pollock, 2022). As a result, GenAI has become an enabler in the act of teaching for equipping, strategising, and stimulating intercultural learning among teacher-students in co-constructive environments.