Artificial intelligence (AI) is rapidly becoming a defining capability of the twenty-first century, increasingly shaping contemporary teacher education through ongoing digitalisation. Since the Covid-19 pandemic, teachers’ engagement with digital technologies has intensified, and professional proficiency in deploying digital tools has expanded considerably. At the same time, persistent teacher shortages place additional pressure on teacher education programmes to prepare future teachers under conditions of accelerated change. Together, these developments raise critical questions about the nature of teaching and what it means to learn to teach in the AI era (OECD, 2026).

Digital technologies do not only reshape instructional practices; they also influence how teachers understand their role, expertise, and the relational and affective dimensions of their work (Sutherland et al., 2010; Markauskaite et al., 2022; Strom, 2015). Existing research on teaching in digitally mediated contexts has largely focused on defining digital competencies, examining the adoption of specific tools, or evaluating learning outcomes (Tan et al., 2025). Far less attention has been paid to how pre-service teachers actually experience learning to teach within these changing conditions.

Pre-service teacher education constitutes a significant site of inquiry because it is not merely preparatory but constitutive. It is within initial preparation programmes that future teachers first encounter the discourses, technologies, policies, and relational dynamics that shape their emerging understandings of teaching  (Strom, 2015; Strom & Viesca, 2022; Sidebottom, 2021; Sutherland et al., 2010). These programmes are also spaces in which candidate teachers are expected to learn how to navigate tensions between standardised expectations of teaching and the unpredictable, relational realities of contemporary classrooms (Russell & Martin, 2016). As AI and digital infrastructures become more prominent in higher education, these tensions are intensified, making pre-service teacher education a critical site for examining how future teachers learn to navigate complexity and uncertainty in professional practice (Markauskaite et al., 2022).

Despite growing interest in AI and education, there remains limited empirical research that examines how learning to teach is experienced from the perspective of pre-service teachers themselves. Moreover, teacher education scholarship remains marked by geographical and epistemic unevenness (Ovens et al., 2016; Zembylas, 2024). Much of the existing literature is concentrated in English-speaking and Western contexts, raising concerns about cultural ethnocentricity.

The present study addresses this gap through an ethnographic exploration of pre-service foreign language teacher education in two under-examined contexts: Lithuania and Kenya. Rather than treating these contexts as directly comparable national systems, the study approaches them as distinct educational environments shaped by different historical trajectories, institutional arrangements, and digital conditions (Government of Kenya, 2007; Ministry of Education and Science of the Republic of Lithuania, 2013), treating Lithuania and Kenya as epistemically equal sites of knowledge production rather than positioning one as central and the other as peripheral, which aligns with calls for decolonising and geographically just approaches to educational research (Kruger & Zembylas, 2026).

Foreign language teacher education provides a particularly revealing site of inquiry: language teaching is inherently relational, and AI mediation directly challenges this by reconfiguring communication, authorship, and human interaction in language learning (Yang & Markauskaite, 2025), making foreign language teacher preparation an especially critical context for this study.

The present study seeks to understand how learning to teach is experienced across different educational contexts in teacher education within higher education.
The research is guided by the following questions:

1. How do pre-service foreign language teachers experience and make sense of learning to teach within higher education environments?

2. How do pedagogical practices, institutional arrangements, and digital technologies, including AI, shape these experiences?

3. What tensions and uncertainties do pre-service teachers encounter when learning to teach in higher education environments, and how do they respond to them?

The study adopts an ethnographic research design informed by Agar’s (1980, 2006) abductive and iterative approach to ethnography as epistemology (Skukauskaite, 2023). Fieldwork was conducted in foreign language teacher education programmes at one university in Lithuania and one university in Kenya. Data collection comprised: - weekly participant observation of lectures; - semi-structured interviews with pre-service teachers and teacher educators; - analysis of programme documents and institutional materials. Observations focused on pedagogical interactions, teaching and learning practices, task design, and the situated use of digital technologies, including AI. Interviews explored participants’ experiences of learning to teach, their engagement with digital tools, perceptions of preparedness for practice, ethical considerations, and emerging professional understandings. In total, 36 participants took part in the study, including seven teacher educators (three in Lithuania and four in Kenya) and 29 pre-service foreign language teachers (12 in Lithuania and 17 in Kenya). Purposive sampling was used in Lithuania to recruit participants directly involved in foreign language teacher education. In Kenya, initial purposive sampling was organised in collaboration with two lecturers prior to fieldwork and subsequently evolved into snowball sampling. This approach reflects the researcher’s position as a non-local researcher and enabled access and trust to develop gradually through institutional contacts and participant referrals. Data analysis followed an abductive and iterative logic (Agar, 1980, 2006; Timmermans & Tavory, 2012). Interview transcripts were coded verbatim in MAXQDA using in vivo coding (Charmaz, 2006), generating 452 codes in the Lithuanian dataset and 641 in the Kenyan dataset. Related codes were grouped into broader analytic themes through ongoing movement between empirical material, fieldnotes, and theoretical frameworks, in line with reflexive thematic analysis (Braun & Clarke, 2019) and an ethnographic approach to meaning-making (Skukauskaite, 2023). In addition to thematic analysis, the study employs vignettes as an analytic device. Informed by Agar’s (1980, 2006) concept of rich points and Geert’s (1973) understanding of ethnographic rendering, vignettes capture the moments encountered during fieldwork that disrupted initial interpretations and opened new analytical directions. All necessary ethical approvals had been obtained prior to data collection. In Lithuania, ethical approval was granted by the relevant faculty-level research ethics committee at a public university. In Kenya, institutional permission was secured through the Deputy Vice-Chancellor’s office at a public university, and a national research licence was obtained from the National Commission for Science, Technology and Innovation (NACOSTI).

Findings reveal that learning to teach in the AI era is experienced as emotionally demanding and non-linear, shaped less by competency acquisition and more by navigating uncertainty, relational complexity, and institutional conditions. Three interrelated dimensions constitute this process, including institutional organisation, self-direction, and relational learning, with the relational proving most foundational in both settings. RQ1: Pre-service teachers experience learning to teach as simultaneously formative and uncertain. In Lithuania, participants navigate simultaneous employment, valuing psychology and didactics courses as meaningful while experiencing a persistent gap between university preparation and classroom realities regarding classroom management, parental pressure, and AI use. In Kenya, learning unfolds through lecturer-led pedagogy, peer presentations, and teaching practice, building professional confidence and identity. RQ2: Institutional arrangements shape experiences in contextually distinct ways. In Lithuania, AI mediation has prompted lecturers to replace take-home writing with in-class tasks to ensure authenticity, while hybrid delivery enables access but reduces relational presence. In Kenya, class sizes of up to 300 students limit assessment to written examinations, and no formal AI policy leaves individual lecturers to govern AI use independently. RQ3: Uncertainty emerges most acutely during teaching practice. In Kenya, participants who entered with reluctance left with a deepened sense of vocation – teaching practice as transformative experience. In Lithuania, one participant disclosed after the interview their intention to leave, revealing commitment and doubt as coexisting realities. Across both contexts, AI introduces tensions around cognitive offloading, dependency, and authenticity, and navigating these critically becomes a form of professional formation. These findings contribute empirically grounded evidence that becoming a teacher in the AI era is shaped not by technical competency acquisition, but by navigating uncertainty, relational complexity, and institutional conditions, where AI mediates but does not define the experience of becoming a teacher (Yang & Markauskaite, 2025; OECD, 2026).

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