Conference Agenda

Teachers’ professional development increasingly takes place within diverse professional networks that go beyond individual schools, organizational charts, and national borders. At the same time, teachers are expected not only to implement educational reforms but also to actively shape pedagogical methods, especially amid the ongoing digital transformation (Roumbanis Viberg et al., 2021; Van Der Heijden et al., 2015). These demands highlight the crucial role of teachers’ agency, which is their ability to initiate, justify, and collaboratively promote changes in educational practices (Emirbayer & Mische, 1998; Schatzki, 2002). Although professional networks are widely seen as key sources for professional growth and innovation, there is less understanding of how teachers perceive these multi-level networks as resources for fostering their transformative professional agency.

This research examines how teachers perceive their professional networks shaping their transformative professional agency in the context of digitalization. It is grounded in the concept of transformative professional agency (Korhonen et al., 2024; Markauskaite & Goodyear, 2017; Reinius et al., 2022; Stetsenko, 2016), which views agency as future-focused, collective, and aimed at changing practices rather than merely adapting to existing ones. The study also draws on sociocultural perspectives (Eteläpelto et al., 2013) on professional learning, knowledge creation, and communities of practice (Wenger, 1999), highlighting that social interactions, tools, and organizational contexts shape professional agency. In this view, professional networks serve as dynamic spaces for collective sense-making, identity work, and knowledge development, which can either facilitate or limit teachers’ ability to innovate and develop their practices.

The study utilizes a qualitative, multi-modal research approach within the Finnish context of the Learning and Teaching in Digital Environments Professional Specialization Program (DigiErko). It involves 12 teachers who have completed the program and are actively involved in digital pedagogical development. Data collection includes semi-structured interviews and ego-centered professional network maps focused on teachers’ digital pedagogical activities. The maps served as elicitation tools during the interviews, helping participants reflect on their professional networks and their roles within them.

The data analysis combines qualitative content analysis of interview data with a systematic visual examination of network maps. By merging interview stories with visual data, the study offers a detailed understanding of how teachers view their professional networks and how these networks influence their sense of transformative professional agency.

The findings indicate that teachers’ professional networks consist of multi-level, overlapping ecosystems rather than isolated or linear structures. Teachers describe these networks as ranging from close, school-based collaborations to broader national, digital, and international professional communities. In these various contexts, professional networks enhance transformative professional agency in multiple interconnected ways. They reduce the barriers to experimentation and pedagogical risk-taking by providing safe spaces for dialogue, shared problem-solving, and mutual learning from experiences. Additionally, networks serve as sources of inspiration and innovation, providing access to new ideas, tools, and practices that can be tailored to local settings. Participation in these networks also helps legitimize teachers’ roles in professional development, boosting their professional identity and credibility when leading pedagogical change in schools. Lastly, networks facilitate collective knowledge creation and dissemination, positioning teachers as brokers, mentors, and facilitators whose influence reaches beyond their classrooms.

The study advances research on teacher agency by framing transformative professional agency as relational and distributed within networked contexts. In practice, the results indicate that professional networks need to be intentionally designed, acknowledged, and supported as essential infrastructure for sustained, teacher-led educational change in increasingly digital learning environments.

Across education systems, schools are increasingly expected to engage in evidence-driven school development by systematically using data to inform teaching, learning, and organisational decision-making (Schildkamp, 2019; Vanari et al., 2020). These expectations place teachers at the centre of improvement processes, positioning them not only as users of classroom data but also as contributors to collective professional reasoning and school development. Despite growing data availability, however, research consistently shows that evidence use in schools remains uneven and fragmented. Classroom-level data are often used intuitively or individually, while school-level data tend to serve accountability or reporting purposes, limiting their instructional relevance (Datnow & Hubbard, 2015; Rääk et al., 2021).

From an interpretive perspective, this challenge is not primarily about access to data, but about how teachers make sense of evidence and how individual interpretations are—or are not—transformed into shared professional knowledge that supports school development. Evidence-driven teaching relies on teachers’ capacity to notice relevant classroom events, interpret student learning, and translate these interpretations into instructional action (Blömeke et al., 2015; Vanlommel et al., 2017). Without structured opportunities for collective interpretation, evidence use risks remaining individualised and episodic, limiting its contribution to sustained school improvement.

This study examines Lesson Study as a structured professional learning routine that can support collective meaning-making around classroom evidence and connect classroom inquiry with school development processes. Lesson Study is internationally recognised as a collaborative, inquiry-oriented approach to teacher learning that integrates joint lesson planning, focused observation, and collective reflection on student learning (Lewis et al., 2006; Fujii, 2014). While Lesson Study is often promoted as an effective professional development model, less is known about how teachers interpret classroom evidence within LS and how these interpretive processes contribute to broader school development, particularly in contexts where collaborative inquiry is not yet routinised.

The study addresses the following research questions:

1. How do teachers experience Lesson Study as a structured collaborative approach for developing evidence-driven teaching practices?

2. In what ways does Lesson Study support teachers in making meaning of improvements in evidence-driven teaching and their connection to broader school development goals?

Education is regarded as a central instrument for promoting social participation. However, the German education system functions as a mechanism for the reproduction of social inequalities through normalizing processes of selection (El-Mafaalani, 2021; Heinemann & Mecheril, 2023). Schools are not only institutions of knowledge transmission, but also spaces of interaction in which societal power relations and structural conditions become effective and are (re)produced through processes of difference-making (West & Fenstermaker, 1995). Teachers therefore bear responsibility for actively counteracting discrimination based on constructions of difference (Antidiskriminierungsstelle des Bundes, 2018). Reflexive knowledge of societal power relations as well as of processes of privileging and discrimination constitutes a prerequisite (Rendtorff, 2021). In the absence of such knowledge, school-based selection practices may particularly affect students with a migration background or from low-income families (Sirin, 2005; El-Mafaalani, 2012). Teacher-led performance-based differentiation does not occur exclusively along the meritocratic principle, but also on the basis of intersectional categories of difference such as gender, race, class, and disability (Giesinger, 2007). Structural inequalities are further intensified through unequal treatment based on inequalities (El-Mafaalani, 2021). Educational inequalities are also reproduced in a digitalized society, even though reflexive and problematizing knowledge of the underlying structural injustices is increasing (Heinz, 2023). For Germany, results from the International Computer and Information Literacy Study (ICILS) 2018 show that social background influences unequal prerequisites for digital competence. Students from families with low cultural capital exhibit lower levels of computer- and information-related competencies than those from families with high cultural capital (Eickelmann et al., 2019). Van de Werfhorst et al. (2022), also analyzing ICILS 2018 data, add that the divide in students’ digital competencies is additionally shaped by gender and migration background. According to Lüring et al. (2022), school-based learning opportunities are used less extensively by children from families with a migration background and from low-income households, among other factors. These patterns are associated with stereotypical and deficit-oriented attitudes by teachers (ibid.). In a case study conducted in three Californian middle schools, Rafalow (2020) identified differing evaluations of and approaches to students’ digital skills depending on ethnicity and social class. Only teachers at a school with a predominantly white, high-income student population transformed digital competencies into educational capital. Rafalow conceptualizes teachers as gatekeepers of students’ potential in dealing with technologies (ibid.). For Europe, especially Germany, systematic reconstructive investigations of these interrelations are still lacking. The present project addresses this desideratum. Its epistemic interest is directed toward inequality-relevant knowledge held by teachers in the context of digitalized teaching. Specifically, the study investigates how such knowledge is shaped by, embedded in, and expressed through teachers’ professional orientations in digitally mediated classroom practices. Inequality-relevant knowledge comprises both knowledge of inequality and inequality-critical knowledge. Knowledge of inequality refers to socially acquired and embodied knowledge that can be explicated in norms (Güllü, 2025). Inequality-critical knowledge may take the form of acquired knowledge and consolidate into problematizing this embodied societal knowledge. On an implicit level, both forms of knowledge may structure practices of action and speech (ibid.). In the tradition of praxeological sociology of knowledge, this distinction does not hinge on the factual correctness or normative desirability of the knowledge in question. Rather, the focus lies on reconstructing how it is produced in practice (Bohnsack, 2021). The study is guided by the following research questions: (1) Which inequality-relevant knowledge do teachers display when speaking about their digitalized teaching practices? (2) Which categories of difference are rendered relevant by teachers in this context? Taken together, these questions reflect an open and exploratory approach to the forms of knowledge under investigation.The project contributes to basic research in the field of inequality-sensitive professionalization.

Executive functions (EFs) are a set of higher-order cognitive processes, that include working memory, inhibitory control, cognitive flexibility, and sustained attention, that play a crucial role in self-regulation, learning, and adaptive behaviour from early childhood onward (Bascandziev et al., 2016). Research highlights early childhood as a sensitive period for the development of EF, with long-term implications for academic achievement, social competence, and emotional well-being (Blakey & Carroll, 2015; Fitzpatrick et al., 2014; Sasser, Bierman & Heinrichs, 2015). For this reason, early childhood education settings represent a key context for fostering the development of these skills through pedagogical practices.

Within this broader research framework, the present paper constitutes a focused theoretical and pedagogical deepening of a wider project aimed at promoting EF development in early childhood. This contribution is designed as an in-depth reflection addressed to the professional training of early childhood educators, with the goal of supporting their ability to intentionally foster executive functions in everyday educational practices and play-based contexts.

Drawing on a socio-constructivist and cultural-historical perspective, this paper conceptualizes EF not as isolated individual abilities, but as skills that develop through social interaction, shared activity, and emotionally meaningful experiences (Bronfenbrenner, Morris, 2006). Within this framework, Vygotsky’s concept of perezhivanie is used as a theoretical lens to understand how children actively experience, interpret, and are transformed by their interactions with the environment. EF development is therefore shaped by the dynamic relationship between the child’s individual characteristics and the educational context in which experiences occur (Vygotskij, 2022).

Play, especially symbolic and imaginative, is identified as a privileged context for the activation and development of EF in early childhood. Imaginative play allows children to move between reality and fiction, attribute new meanings to objects, and construct shared narratives, fostering the development of increasingly complex forms of thinking (Bembich, Sorzio, 2020). From this perspective, the role of the educator is central. Educators act as cultural mediators and facilitators of play, intentionally designing learning environments that promote EF development while respecting children’s agency and interests (Veresov, et al., 2021). Through scaffolding strategies, educators support children in maintaining the coherence of play narratives, remembering rules, managing turn-taking, and regulating emotions during moments of frustration or conflict. Rather than directing play in a rigid manner, educators guide and enrich it by introducing meaningful challenges, posing reflective questions, and adding narrative or problem-solving elements that stimulate executive processes.

Intentional pedagogical planning is particularly important in play-based activities that integrate storytelling, role-play, and symbolic use of materials. By selecting stories, educators create familiar and engaging contexts in which children can explore new roles and perspectives. The introduction of problem situations within play narratives encourages children to collaboratively generate solutions, adapt strategies, and negotiate meanings, thereby strengthening EF in a social and emotionally supportive setting (Veresov, et al., 2021). Adult support does not limit children’s autonomy; rather, it enhances the quality of play by sustaining engagement, deepening conceptual understanding, and supporting self-regulation. These practices are particularly beneficial for children from disadvantaged socio-economic backgrounds, for whom early support of EF can serve as a protective factor against later academic difficulties (Meixner, Laubrock, 2024).

In conclusion, this paper offers a targeted contribution within a broader research and educational project by addressing the role of educator training in supporting EF development in early childhood settings. Educators play a vital role in creating intentional, play-based learning environments that support children’s cognitive and emotional development (Veresov, et al., 2021). By valuing play as a central pedagogical tool and by adopting reflective and responsive teaching strategies, early childhood educators can meaningfully contribute to the development of EF, promoting long-term educational success and overall well-being.

This study looks at how secondary school learners can improve their argumentative skills by analyzing information from different subjects. In today’s education, argumentation is seen as an important skill needed for academic success, involvement, and critical thinking. However, research and classroom experiences show that many learners have difficulty creating evidence-based arguments, especially when dealing with complex texts that require identifying main ideas, evaluating evidence, and using logical reasoning. This study tackles this issue through an interdisciplinary Lesson Study carried out by teachers of English, Kazakh language, and World History in a Grade 10 (learners aged 15 to 16) in Kazakhstan.

The main research question is: How can structured analytical tools help secondary school learners build evidence-based arguments through text analysis in various subjects? The major aim of the research was to see if using structured tables as analytical helpers could improve learners’ ability to extract key information from texts, organize ideas logically, and create clear, well-supported arguments. Another goal was to look into how these tools work for learners with different academic skills and how support can gradually be lessened to encourage independent thinking.

The study is based on sociocultural and cognitive theories. From a sociocultural viewpoint, it uses Vygotsky’s idea of the Zone of Proximal Development, which focuses on how scaffolding and guided support assist learners in tackling complex tasks they can't yet do on their own. Structured tables served as tools to help learners shift from supported to independent argument creation. From a cognitive standpoint, the study incorporates cognitive load theory, which stresses the need to minimize unnecessary cognitive load when students handle complex tasks like text analysis and argumentation. By breaking down the argumentative process into manageable parts (key ideas, evidence, counterarguments, conclusions), the structured tables helped learners organize information better and concentrate their cognitive efforts on reasoning instead of managing the task.

The research also references literature on higher-order thinking skills and argumentation. According to revised Bloom’s Taxonomy, constructing an argument requires learners to progress from understanding to analysis, evaluation, and synthesis. Previous studies on graphic organizers and structured analytical tools suggest that visual and organizational supports can help this process, especially for learners who find abstract reasoning or written expression challenging. Methodologically, the study used a qualitative Lesson Study design that included three research lessons. Data was gathered through classroom observations, pre-study learner surveys, semi-structured student interviews, analysis of learner work, and focused case studies of three students with different achievement levels. The iterative nature of Lesson Study allowed for ongoing adjustments of teaching strategies based on observed learner needs.

The findings show that structured analytical tables led to noticeable improvement in students’ argumentative skills across subjects. Most learners were better able to identify key ideas, choose relevant evidence, and build more coherent arguments. Notably, this approach reduced the number of learners who needed extra help, while allowing high-achieving learners to become more independent as support was gradually removed. The European and international significance of this study lies in its focus on practical teaching strategies that promote argumentation as a skill used across subjects. In various educational contexts facing issues like information overload, media literacy, and critical thinking, structured support combined with collaborative teacher inquiry provides an effective and flexible solution. The study adds to international conversations on curriculum development, interdisciplinary teaching, and teacher learning, showing how Lesson Study can systematically enhance learners’ argumentative skills across different subjects.

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?

Teachers operate under conditions of increasing professional demands, including intensified workloads and the need to respond to diverse learner needs. In this context, artificial intelligence (AI) is frequently discussed as a potential means of supporting instructional practice. This is congruent with the growing number of teachers who are reporting an increased utilization of AI in the preparation of their lessons (Herm & Hagen, 2025). Nevertheless, the German School Barometer revealed that 62% of respondents expressed feelings of uncertainty or extreme uncertainty regarding the utilization of AI, and 31% reported not using AI at all (Herm & Hagen, 2025). These findings indicate a substantial lack of professional preparedness, underscoring the need to systematically address AI-related competencies in teacher training. Consequently, AI must become an integral component of teacher training, accompanied by empirical research on pre-service teachers’ (PSTs) AI use and the effectiveness of related pedagogical measures.

However, research on the use of AI for subject-specific materials development remains limited, and clear connections between AI literacy and concrete teaching practice are rarely made (Xin, 2024, p. 4). Moreover, studies focusing explicitly on PSTs remain scarce, as research often centers on students or in-service teachers (Huang et al., 2025, p. 2).

Against this backdrop, this dissertation project aims to investigate the usage of AI among PSTs for English as a foreign language in secondary schools. The project's central theme is the design and analysis of a university seminar (comprising 11 sessions of 90 minutes each) in which generative AI is systematically employed for materials development. The present project examines how PSTs utilize AI in this context, the evaluation criteria they apply, and the changes observed in their practices and evaluative perspectives over the course of the seminar. The analysis examines patterns of interaction and adaptation that emerge in dealing with AI and focuses on how the critical justification of AI-based material revisions develops.

The following research questions are investigated:

• RQ1: How can a theory- and practice-based seminar be designed didactically, in which AI-supported materials development serves as a learning opportunity for the development of AI literacy?
• RQ2: In what ways do PSTs use AI before, during, and after the seminar?
  • RQ2a: What patterns of interaction and adaptation emerge?
  • RQ2b: How does the seminar change the critical assessment of the (pedagogical, linguistic, and ethical) quality of AI outputs, and what criteria determine the revisions?

The seminar, therefore, combines existing didactic and materials-related theories with AI-specific content. For instance, the program includes sessions on AI algorithms, ethical considerations, and prompt engineering, and their implications for materials development. The theoretical underpinnings of the seminar are provided by the UNESCO AI Competency Framework for Teachers (2024) because it provides clear guidance for the AI content that is to be implemented in teacher training (Bai, 2025, p. 50). Materials development was chosen as the focal area, as it constitutes a recurring and authentic pedagogical practice in which AI-specific learning objectives and curricular guidelines can be operationalized. According to Edmett et al. (2024, p. 31), it is the most popular use of AI among teachers, with 57% of the surveyed teachers reporting that they use AI for this purpose. However, in the same study, more than half of those teachers disagreed or strongly disagreed with the statement 'I have received enough training to incorporate AI into my teaching' (Edmett et al., 2024, p. 36), which points to a clear misalignment between the frequent use of AI in teaching and teachers’ self-reported training experiences.

The seminar’s positioning at the intersection of didactics and AI education introduces a new dimension of professionalization, grounded in the systematic reflection on pedagogically justified AI use.