Conference Agenda

Digital storytelling has gained increasing attention in European educational research as an instructional approach that integrates digital technologies with narrative-based learning. In secondary education, where learners are expected to develop advanced language skills alongside digital competence, digital storytelling represents a pedagogical intersection between ICT-enhanced learning and communicative pedagogy. This study investigates the implementation of digital storytelling in high school classrooms and examines its impact on students’ language development, engagement, and digital competence.

The study is guided by the following research question:
How does the use of digital storytelling influence high school students’ language learning, learner engagement, and digital competence development?

The research is underpinned by a multidisciplinary theoretical framework, drawing on sociocultural learning theory (Vygotsky, 1978), which conceptualizes learning as a socially mediated process, and multiliteracies theory (Cope & Kalantzis, 2015), which emphasizes multimodal meaning-making in contemporary digital environments. These perspectives are complemented by European policy frameworks, such as the Digital Competence Framework for Educators (DigCompEdu), which foreground the development of digital, communicative, and critical competences as core educational goals.

Studies have shown that digital storytelling can enhance language skills, including speaking and listening, and promote motivation and active participation in secondary education settings. For example, research in Algerian secondary schools found that digital storytelling is perceived as an effective strategy for enhancing language skills and student engagement (Hadj Boulenouar, 2024). Moreover, a systematic review published in Sustainability reports that digital storytelling contributes to improved speaking proficiency across educational levels, indicating its broad pedagogical value.

This proposal argues that digital storytelling, when intentionally embedded in high school curricula, can address multiple educational goals: improving language outcomes, supporting learner engagement, and fostering digital literacies. The study also considers the challenges teachers face in implementation, including technology access and instructional design, drawing on empirical findings from secondary schooling research.

This presentation focuses on the role of Artificial Intelligence (AI) in the context of teacher education. AI is simultaneously transforming and challenging traditional educational practices. Various AI technologies offer both new opportunities and complex challenges for teachers, teacher educators, and learners. Generative AI tools such as ChatGPT and Copilot, with their natural language interfaces and broad content capabilities, can be applied to a wide range of pedagogical purposes. According to Kasneci et al. (2023) and Resnick (2023), these tools can support drill‑and‑practice activities as well as more creative and collaborative approaches.

Alongside these positive aspects, there are also concerns regarding the use of generative AI in education. First, the ease of use and natural language interaction may encourage students to outsource their thinking to AI (Kasneci et al., 2023; Fan et al., 2025). Teachers design learning activities to stimulate students’ cognitive processes and support meaningful learning. However, with AI tools, these tasks can easily be copied into the system to produce ready-made assignments without cognitive effort, creating an illusion of learning. Yet learning requires sustained engagement with new information, time, and active processing of content (Van Merrienboer & Sweller, 2005).

Another challenge, common to many educational technologies, concerns the teacher’s role and teacher agency. By agency we refer to professional agency, i.e., teachers’ ability to make pedagogical choices and decisions regarding learning activities within their classrooms and courses (Eteläpelto et al., 2013). Particularly with adaptive systems or Intelligent Tutoring Systems, materials, learning paths, and activities are typically pre-designed, reducing the teacher’s role in decision‑making. As AI systems become more sophisticated, tensions have emerged around potential threats to teachers’ autonomy (Selwyn, 2019). Some scholars even suggest that AI could replace teachers entirely (Romero & Ventura, 2020), while others argue that AI may substitute specific functions but cannot replicate the inherently human aspects of teaching (Järvelä et al., 2023).

This presentation provides teacher educators’ perspectives on these challenges and possibilities related to AI within teacher education. This mixed‑methods study includes 156 teacher educators from four Finnish universities. The dataset consists of quantitative data collected through an online questionnaire containing Likert‑scale statements, as well as qualitative data collected through open‑ended questions. The quantitative section of the questionnaire was built upon the Technology Acceptance Model (TAM) by Davis et al. (1989), focusing on attitudes towards AI, perceived usefulness, and perceived ease of use. Additionally, an AI anxiety dimension was included. The qualitative section consisted of three questions: 1) As a teacher educator, what thoughts and emotions do artificial intelligence evoke in you? 2) In your view, how should teacher education respond to the increasing use of artificial intelligence? 3) What support or resources would you need to feel more confident and in control when using artificial intelligence in teaching?

The data analysis was conducted in two phases. First, K‑means clustering was used to group respondents based on the TAM factors and AI anxiety. Second, qualitative data were analysed using inductive approach to capture teacher educators’ own descriptions of how they perceive the role of AI in teacher education.

The results reveal three distinct groups of teacher educators with differing perspectives on AI in education, ranging from strongly positive to more cautious. AI anxiety in particular appears to differentiate the groups. The qualitative findings further elaborate these perspectives using educators’ own expressions regarding the opportunities, risks, and future role of AI in teaching and learning.

Overall, this presentation offers a much‑needed perspective on teacher educators’ views of AI, its potential, challenges, and implications for teacher education, an area that has so far received limited research attention.

This paper investigates the dynamic co-transformation of pedagogical practices between teacher educators (TEs) and pre-service teachers (PSTs) during AI-enhanced material development in initial teacher education (ITE). Across European and international contexts, the rapid emergence of generative artificial intelligence (AI) has intensified debates about teacher agency, pedagogical responsibility, and ethical practice. While much existing research focuses on teachers’ or students’ individual adoption of AI tools, less attention has been paid to how pedagogical practices are mutually reshaped through interaction among teacher educators, pre-service teachers, peers, and AI technologies themselves. This paper addresses this gap by conceptualising AI integration as a reciprocal, relational, and non-linear process of co-transformation.

The study is guided by the following research question:

How do the pedagogical practices of teacher educators and pre-service EFL teachers dynamically co-transform during AI-enhanced material development?

Conceptually, the study is framed by UNESCO’s AI Competency Framework for Teachers, which provides a structured lens to examine pedagogical change across five competency blocks: human-centred mindset, ethics of AI, AI foundations and applications, AI pedagogy, and AI for professional learning. To interpret the nature and direction of change within these blocks, the framework is complemented by Transformative Learning Theory, which foregrounds disorienting dilemmas, critical reflection, and perspective transformation. This combined framework enables the analysis to move beyond linear or skill-based accounts of AI integration and instead capture uneven, reciprocal, and sometimes contradictory trajectories of pedagogical change.

Methodologically, the paper reports findings from a qualitative descriptive case study conducted within a 15-week undergraduate English language teacher education course. Participants included one experienced teacher educator and eight third-year pre-service EFL teachers engaged in a project-based sequence involving AI-enhanced material development and online microteaching. Data sources comprised PST reflective journals, AI-enhanced teaching artifacts, focus group interviews, and retrospective, reflective, and prospective journals written by the teacher educator. Data were analysed using deductive thematic analysis, guided by the competency descriptors of UNESCO’s framework and informed by principles of transformative learning.

The findings demonstrate a reciprocal and multi-directional co-transformation between TEs and PSTs. Strong transformation was observed in the human-centred mindset and AI pedagogy blocks, where participants increasingly emphasised teacher agency, pedagogical filtering of AI outputs, and hybrid human-AI creativity. In contrast, the ethics of AI block remained largely unaddressed, revealing a shared silence in which ethical concerns were reduced primarily to plagiarism avoidance. The study also documents non-linear pathways, including resistance and intentional withdrawal from AI use, which are theorised as alternative forms of transformation rather than failure.

By foregrounding co-transformation, this paper contributes to European and international discussions on ICT and AI in education by showing that effective AI integration in teacher education depends on structured pedagogical scaffolding, critical reflection, and explicit engagement with ethical dimensions. The study offers implications for teacher education curricula, professional development, and institutional policy in AI-rich educational environments.

The established thesis in digital technology asserts that digital assessments revolutionise the evaluation of learning by overcoming the inherent limitations of human data collection. Digital assessment is presented as the silver bullet to solve the problem of unreliable assessment (Kilani et al., 2024; McBride et al., 2024; Mukherjee et al., 2020). The UK government shares this techno-solutionist approach and has invested significantly in developing and piloting emerging educational technology (Dyson, 2025). Aligned with this approach, the UK government launched a newly digitised version of an existing baseline assessment for 4-year-olds in September 2025. In the new test, children are given their own touchscreen device on which to answer questions and complete tasks. The Reception Baseline Assessment (RBA) (Standards and Testing Agency, 2025) is a statutory test of maths and language skills, introduced in 2021, which all state educated 4-year-olds in England must take within the first 6 weeks of school. The results of the test are used solely for accountability purposes, giving a baseline from which to measure progress through primary school.

Our research sought to ask, ‘How do children experience the RBA?’ To do this, we took a longitudinal case study approach, observing children completing the baseline over a two-year period from 2024 to 2025. We analysed the video data using multimodal discourse analysis (MDA) to determine the child’s experience.

Our research suggests that the introduction of children’s own touchscreen devices has led to unintended consequences. We found that all children observed displayed indications of stress and anxiety. This was evident in their embodied responses to the test. We also found that disadvantage was amplified due to the algorithmic routing through the test, which privileged those who had higher levels of digital literacy and were confident native speakers of English. Finally, we found that the test depended on high levels of digital literacy as children were required to select the appropriate digital action without any prompting from the teacher. This has resulted in test which is an unreliable assessment of children’s knowledge and skills and which negatively impacts on child wellbeing.

We argue that the English Department for Education’s lack of appropriate ethical regulations and democratic governance of edtech is situated within a societal ‘technocrisis of childhood socialisation’ (Cuffe et al 2025). Without effective development, regulation and research, new edtech has as much potential to harm as it does to improve educational experiences. This is a cautionary tale for all geopolitical contexts, as governments must develop effective ways to regulate and evaluate emerging edtech in order to prevent harm.