Artificial Intelligence (AI) has brought in irreversible changes in learning and teaching processes, prompting a re-evaluation of teacher education globally. While this technological advancement is also celebrated for its potential to innovate teacher education, effective integration requires addressing the heterogeneity of the teaching workforce, which shapes how AI is adopted. Such a gap is significant for induction-phase teachers, who occupy a uniquely precarious position: they are expected to act as primary agents in translating AI-related policy ambitions into classroom practice due to their assumed technical familiarity, while simultaneously negotiating the ongoing formation and transforming their professional identity. As Tondeur et al. (2017) contend, perceived complexity and inadequate preparation present persistent hurdles among beginning teachers, and AI integration undoubtedly adds another layer of technological complexity to an already demanding transition.

Adaptive expertise, as the ability to flexibly respond to problems in novel, complex and changing contexts, is key to induction-phase teachers’ development (Rohwedder & Le Fevre, 2025). It helps teachers maintain consistent performance in new situations, such as flexibility, innovation, and creativity, which requires activities beyond the established routines (Cai et al., 2023; Ward et al., 2019). In the context of AI, where the technological landscape changes fundamentally, the complexity of classroom situations is in constant flux. In response, teachers with high adaptiveness will have knowledge that continuously grows and invent new procedures when necessary (Bohle-Carbonell et al., 2014; Hatano and Oura, 2003).

Previous studies using TAE as a framework in AI-integrated contexts have not yet paid due attention to how induction-phase teachers construct adaptive expertise. Furthermore, most AI research tends to depict teacher development as a linear and top-down process, thereby overlooking the heterogeneity of teachers’ backgrounds, experiences, and professional trajectories (Liu & Shang, 2025). This study addresses these gaps by conceptualising TE as a process that requires contextualised and individualised customisation to teachers’ diverse needs. To this end, the research adopts a qualitative case study approach, guided by the following three research questions:

(1) How do induction-phase teachers currently use AI in curriculum and schooling in China?

(2) What new competencies does AI integration into education require of induction-phase teachers?

(3) How can teacher training address the newly developing needs for induction-phase teachers?

Drawing on a multiple case study conducted in China, this chapter investigates induction-phase teachers’ experience of AI integration into teaching, factors shaping their practice, and these teachers’ developmental needs. Primarily using the interview data, triangulated with self-report questionnaires and stimulated recalls, the study finds that, while beginning teachers initially demonstrate a relatively cautious and pragmatic orientation towards AI adoption, their attitudes shift positively with increased exposure. Furthermore, the study identifies teacher autonomy, emotion regulation, pedagogical enactment, and deliberate practice as critical individual competencies contributing to teachers’ adaptive expertise. Additionally, collaborative and relational factors are essential for fostering required expertise. The chapter concludes with implications and recommendations for developing tech-enhanced language pedagogy and AI-focused teacher training in China and beyond.

This chapter primarily draws on online semi-structured interview data. To enhance the trustworthiness of the study, prolonged engagement and reflexive journaling (Dado et al., 2023) were employed throughout the process to triangulate and enrich the reported perspectives. Each semi-structured interview lasted approximately 90 minutes and was conducted in English, given that all participants are professionally certified English teachers. All interviews were audio-recorded and transcribed using automatic transcription software, TurboScribe, after which the transcripts were carefully reviewed and verified by the researcher for accuracy and clarity. Follow-up communication via WeChat was used to confirm factual details and to seek clarification where necessary. In data analysis, the study employs Braun and Clark’s (2006) six-phase thematic analysis. It combines both deductive coding—stemming from the adaptive expertise research of Pelgrim et al. (2022), and inductive coding—allowing the sub-themes to emerge from teachers’ narratives. NVivo 14.0 software was used to assist with the data coding and analysis.

The findings show that beginning teachers’ experience with AI integration into their teaching is a layered and complex process, revealing the interrelated nature of various dimensions of adaptive competences. The study confirms previous findings concerning the complexity of integrating technology in their teaching for induction-phase teachers. This is because induction-phase teachers need to deal with the tension between pedagogical innovation and professional survival needs. This paper makes the following unique contributions. Theoretically, this investigation contributes to the teacher expertise and development literature by identifying the competences required to integrate AI in teaching.This helps better understand the nature of TAE in technology-assisted teaching. From a pedagogical perspective, this study provides evidence for enhancing teacher educators' critical awareness of the professional development needs of induction-phase teachers concerning AI integration into public schooling, such as embedding hands-on, domain-specific AI training programmes, extending beyond STEM to customise language teaching contexts, which did not receive due scholarly or professional attention. It also embodies research-informed practice to help policy and institutional change for AI in TE and beyond. While drawing on data from Chinese schools, its thick description and comparative perspective woven into the study and this report offer wider implications on early career teachers’ adaptive expertise development more broadly.

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