Conference Agenda
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16 SES 05.5 A: General Poster Session
General Poster Session | ||
| Presentations | ||
16. ICT in Education and Training
Poster Rethinking Speaking Assessment in Language Education: AI-Based Feedback and Grade 10 Student Well-Being Nazarbayev Intellectual School of Science and Mathematics in Aktau, Kazakhstan Presenting Author:This study addresses the issues of anxiety and communicative insecurity that arise when assessing speaking skills among upper secondary school students. In practice, speaking assessment is often focused on error identification and score allocation, which can hinder students’ ability to express their ideas freely. In response to this challenge, the study aims to reconceptualize speaking assessment through the use of artificial intelligence–based formative feedback as a safe, supportive, and development-oriented practice that prioritizes students’ emotional well-being. The research adopts the view of assessment not as a punitive or selective mechanism, but as a pedagogical tool that supports student development and fosters a sense of confidence and psychological safety. This research is positioned as a pedagogical approach aimed at systematically improving the speaking skills of Grade 10 students preparing for external summative assessment examinations. Students with well-developed speaking skills are better able to structure their thoughts, demonstrate stronger argumentation, and this process directly contributes to the improvement of writing quality. The findings indicate that increased clarity of speech, readiness to initiate responses, and the ability to develop ideas were reflected in students’ written work through greater depth of content and stronger logical coherence. From this perspective, AI-based formative feedback is viewed not only as a tool for enhancing speaking skills, but also as a comprehensive mechanism that strengthens the interaction between different language skills. The study was conducted at the Nazarbayev Intellectual School in Aktau and was grounded in the principles of smart education. The teaching and assessment processes were designed in a learner-centred format through the purposeful and pedagogically informed use of digital technologies. The research was carried out within a comparative action research framework, which strengthened the teacher’s role as a practitioner-researcher and enabled systematic analysis and iterative improvement of classroom practice. The intervention was implemented progressively over three academic terms. During the first term, an adaptation phase to AI-based formative feedback was observed, with initial improvements recorded in students’ participation in speaking activities and response length. In the second term, students’ communicative confidence increased noticeably, and their readiness to initiate and develop spoken responses became more consistent. By the third term, significant progress was evident in the quality and depth of speaking, with students’ responses becoming more structured, well-argued, and reflective in nature. This progression demonstrates that AI-supported formative feedback produces not short-term effects, but sustained and cumulative impact over time. Throughout the study, students’ speaking skills, emotional states, and communicative engagement were examined comparatively before and after the introduction of AI-based formative feedback. Data were collected from multiple sources, including questionnaires, systematic classroom observations, qualitative analysis of oral tasks, and students’ reflective writings. Particular attention was paid to students’ subjective feelings about speaking, their levels of self-confidence, and their willingness to participate in speaking activities. The study therefore considers not only linguistic outcomes but also students’ emotional experiences within the learning process, aligning closely with the increasingly prominent focus on well-being in contemporary educational research. The teacher shifts from a traditional evaluator to a facilitator, supporter, and promoter of learner autonomy. AI-supported feedback enables students to analyse errors in a psychologically safe environment, engage in self-regulation, and develop reflective skills. As a result, trust-based relationships between students and teachers are strengthened. The findings are expected to demonstrate that the use of AI-based formative feedback reduces speaking-related anxiety and enhances students’ communicative confidence. This study contributes to the rethinking of assessment culture in language education by promoting a more humanistic and learner-centred approach. Furthermore, it offers both theoretical and practical contributions to the development of assessment models within smart education that prioritise students’ well-being. Methodology, Methods, Research Instruments or Sources Used This study adopts a comparative Action Research methodology. Action Research enables teachers to systematically investigate and refine their pedagogical practices, while the comparative design focuses on identifying changes between pre-intervention and post-intervention stages. The research was conducted at the Nazarbayev Intellectual School in Aktau, with tenth-grade students as the research participants. The study was organized in accordance with smart education principles, emphasizing the pedagogically informed integration of digital technologies. The research consisted of three stages: initial diagnosis, intervention, and final analysis. During the first stage, traditional speaking assessment methods were employed. Students completed oral tasks such as expressing opinions and analyzing problems, and their speaking engagement, communicative confidence, and emotional states were assessed. Data were collected through student questionnaires, teacher observation checklists, and descriptive analysis of oral responses. Questionnaire items focused on students’ speaking anxiety and self-confidence. These findings were recorded as baseline data. In the second stage, AI-supported formative feedback was introduced as the intervention. Students completed short oral tasks and received individualized, non-judgmental, and development-oriented feedback generated through AI tools. Feedback focused on clarity of ideas, use of linguistic resources, and strategies for improvement. During this stage, the teacher assumed a guiding and supportive role rather than a controlling or evaluative one. Emphasis was placed on student reflection and understanding of personal learning progress rather than grading. The intervention was implemented consistently across several lessons. In the third stage, the same research instruments were reapplied to identify post-intervention changes. Students’ frequency of participation, length of responses, willingness to initiate speaking, and emotional states were analyzed comparatively. Quantitative data were examined through comparative analysis of questionnaire results, while qualitative data were analyzed thematically based on observation notes and student reflections. Ethical considerations were strictly observed, and students’ responses were treated confidentially. To enhance the credibility of the findings, data were collected from multiple sources and triangulation was applied. Questionnaire and observation data complemented one another, strengthening the validity of the conclusions. A limitation of the study is that it was conducted within a single school context; however, the findings provide practical implications for improving speaking assessment practices in language education. Future research may extend this approach to different grade levels and linguistic contexts, as well as investigate its long-term effects. Overall, the study offers a pathway for rethinking assessment culture from a humanistic and learner-centered perspective in line with contemporary educational demands. Conclusions, Expected Outcomes or Findings This study was conducted as a collaborative action research involving Kazakh and English language teachers and highlights the importance of reconceptualizing speaking assessment in language education as a learner-centered and emotionally supportive practice. The findings indicate that traditional approaches to speaking assessment often generate student anxiety and reduce communicative confidence, whereas the use of AI-supported formative feedback contributes to the creation of a psychologically safe learning environment. The intervention was implemented in both Kazakh and English language classrooms following shared pedagogical principles. Comparative analysis of pre- and post-intervention data revealed positive changes in students’ speaking engagement, willingness to express ideas, and communicative confidence. Collaborative pedagogical reflection further demonstrated that these outcomes were consistently observed across different language contexts. Throughout the study, artificial intelligence functioned as a supportive tool that enhanced formative assessment practices without replacing teachers’ professional judgment. Overall, the findings of this collaborative research support the renewal of assessment culture within smart education frameworks and provide a foundation for developing cross-disciplinary, well-being-oriented assessment models in language education. References 1.Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education. 2.Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research. 3.Sadler, D. R. (1989). Formative assessment and the design of instructional systems. Instructional Science. 4.Horwitz, E. K. (2001). Language anxiety and achievement. Annual Review of Applied Linguistics. 5.MacIntyre, P. D. (2017). An overview of language anxiety research. In New Insights into Language Anxiety. 6.OECD. (2017). Students’ well-being: What it is and how it can be measured. 7.Seligman, M. (2011). Flourish: A visionary new understanding of happiness and well-being. 8.Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation. 9.Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education. 10.Luckin, R. et al. (2016). Intelligence unleashed: An argument for AI in education. 11.Zawacki-Richter, O. et al. (2019). Systematic review of AI in higher education. International Journal of Educational Technology. 12.Benson, P. (2011). Teaching and researching autonomy in language learning. 13.Little, D. (1991). Learner autonomy: Definitions, issues and problems. 16. ICT in Education and Training
Poster Understanding Digital Tool Adoption in Early Childhood Classrooms through the Technology Acceptance Model University of Sheffield, United Kingdom Presenting Author:Digital technologies, platforms, and AI-enabled tools are increasingly embedded in early childhood education (ECE), shaping how teachers plan, deliver, and assess learning. Yet questions remain about how early years educators perceive digital tools, how they evaluate their usefulness, and what factors influence their intention to integrate ICT in daily practice. This poster presents findings from a cross-national survey exploring early childhood teachers’ digital practices in the United Kingdom and China. By comparing two distinct educational and policy contexts, the study provides insights into international variations in digital competence, attitudes toward technology, and the conditions supporting or constraining ICT use in ECE settings. The central research question guiding this study is: How do early childhood teachers in the UK and China perceive, evaluate, and adopt digital technologies in their professional practice, and what factors influence their use of ICT in the classroom? Sub-questions include: (1) How useful and easy to use do teachers perceive digital tools to be? (2) How do national policy priorities and institutional environments shape teachers’ ICT readiness? (3) What cross-national patterns emerge in teachers’ attitudes, confidence, and intentions to integrate digital technologies? The conceptual framework is based on the Technology Acceptance Model (TAM), which remains a widely used model in studies of ICT adoption in education. TAM posits that two constructs, perceived usefulness and perceived ease of use, shape users’ attitudes and subsequent behavioural intention to use technology. This study applies TAM within the early childhood context, addressing an area where digital transformation is accelerating but empirical research on teachers’ ICT adoption remains limited. The model is adapted to account for contextual influences, including resource availability, school leadership expectations, policy environments, and cultural norms surrounding digital childhoods. Methodologically, this poster draws on a cross-national online survey completed by 215 early childhood teachers across the UK and China. The survey included demographic items, Likert-scale questions derived from TAM constructs, and open-ended questions designed to capture teachers’ experiences, concerns, and professional needs in relation to ICT. Quantitative data were analysed through descriptive statistics, reliability tests (Cronbach’s α), independent-samples t-tests, and regression analyses exploring relationships between TAM variables. Qualitative responses from open-ended questions were coded thematically to illustrate the nuanced ways teachers understand and rationalise digital practice. The poster will present key findings illustrating cross-national similarities and differences. Early results indicate that perceived usefulness is the strongest predictor of teachers’ intention to use digital tools in both countries, whereas perceived ease of use varies more significantly, particularly among teachers working in resource-limited or high-demand institutional settings. UK teachers frequently expressed concerns related to online safety, data protection, and appropriate screen-time, reflecting national regulatory priorities. Chinese teachers emphasised curriculum alignment, administrative expectations, and institutional mandates as key drivers of ICT use. These findings suggest that teachers’ ICT adoption is shaped not only by their personal attitudes but also by broader socio-policy structures. The international dimension of the study provides valuable insights into how different systems conceptualise the role of digital tools in early childhood learning, and how teachers’ digital competence can be supported across diverse contexts. By highlighting patterns across two influential early years systems, the poster contributes to European and global discussions on teacher digital readiness, ICT integration, professional development, and the challenges of ensuring equitable and meaningful digital learning opportunities for young children. Methodology, Methods, Research Instruments or Sources Used For the purpose of this poster, the focus is on presenting the survey design, research instruments, and analytic procedures used to investigate teachers’ adoption of digital technologies. The survey was designed using constructs from the Technology Acceptance Model (TAM), which provides a widely used framework for understanding ICT adoption in educational settings. Four scales were used: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Toward Use (ATU) and Behavioural Intention to Use (BI). These constructs were adapted to the early childhood education context, where digital practices must be balanced with play-based pedagogy, children’s developmental needs, institutional expectations, and policy guidelines on online safety and digital competence. The survey consisted of three parts: (1) demographic information (years of experience, qualification level, access to digital resources); (2) a series of five-point Likert-scale questions measuring TAM constructs (e.g., “Digital tools improve my teaching practice,” “Using digital tools in ECE is easy for me”); and (3) open-ended questions that invited teachers to describe opportunities, challenges, and training needs related to ICT integration. Survey items were tested for internal consistency using Cronbach’s α, with all TAM scales showing acceptable to high reliability. A total of 215 early childhood teachers completed the survey, with responses collected from both the UK and China. Independent-samples t-tests were used to examine cross-national differences. Pearson correlations and multiple regression models assessed relationships between PU, PEOU, ATU, and BI. Short-text responses were analysed thematically to contextualise quantitative patterns, particularly around teachers’ concerns, motivations, and examples of digital practice. Overall, this poster presents key descriptive and inferential findings from the survey. This approach enables a clear and visually engaging presentation of how early childhood educators evaluate and use digital tools within different cultural and policy environments. Conclusions, Expected Outcomes or Findings The findings from the cross-national survey indicate distinct patterns in how early childhood teachers in the UK and China perceive and use digital technologies in their professional practice. Overall, teachers in both countries expressed generally positive attitudes toward the educational potential of digital tools, particularly for supporting communication with families, documenting children’s learning, and enriching classroom activities. However, notable differences emerged in the factors shaping their confidence and readiness to integrate ICT. Teachers in the UK tend to emphasise concerns related to online safety, professional training, and appropriate screen time for young children. These concerns were closely linked to their intention to use digital tools, with many indicating that they felt confident using ICT only when clear safety guidelines and secure platforms were available. Several respondents also highlighted disparities in access to digital resources across early years settings, influencing their perceptions of ease of use and feasibility. In contrast, teachers in China more frequently described digital technologies as an expected and routine part of early years practice. Their intention to use ICT was strongly associated with institutional expectations and administrative requirements, including digital reporting systems and curriculum-related digital tools. Perceived usefulness was particularly high among Chinese teachers, many of whom viewed ICT as integral to modernising early childhood education and meeting school-level performance expectations. Across both countries, teachers identified a need for targeted professional development that focuses not only on basic technical skills but also on pedagogical strategies for integrating ICT effectively with young children. Differences in training availability and institutional support appear to play a key role in shaping teachers’ digital confidence. The study is expected to provide practical insights into how teachers in different cultural and policy environments navigate ICT integration, and how training, resource allocation, and institutional guidance can better support meaningful digital practice in early childhood education. References Blackwell, C.K., Lauricella, A.R. and Wartella, E. (2014) “Factors influencing digital technology use in early childhood education,” Computers & Education, 77, pp. 82–90. Chen, C., Lee, S. and Stevenson, H.W. (1995) “Response Style and Cross-Cultural Comparisons of Rating Scales Among East Asian and North American Students,” Psychological Science, 6(3), pp. 170–175. Available at: https://doi.org/10.1111/j.1467-9280.1995.tb00327.x. Davis, F.D. (1989) “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Quarterly, 13(3), pp. 319–340. Available at: https://doi.org/10.2307/249008. Edwards, S. (2016) “New concepts of play and the problem of technology, digital media and popular-culture integration with play-based learning in early childhood education,” Technology, Pedagogy and Education, 25(4), pp. 513–532. Available at: https://doi.org/10.1080/1475939X.2015.1108929. Ertmer, P.A. and Ottenbreit-Leftwich, A.T. (2010) “Teacher Technology Change: How Knowledge, Confidence, Beliefs, and Culture Intersect,” Journal of Research on Technology in Education, 42(3), pp. 255–284. Available at: https://doi.org/10.1080/15391523.2010.10782551. Harzing, A.-W. (2006) “Response Styles in Cross-national Survey Research: A 26-country Study,” International Journal of Cross Cultural Management, 6(2), pp. 243–266. Available at: https://doi.org/10.1177/1470595806066332. Lindahl, M.G. and Folkesson, A.-M. (2012) “ICT in preschool: friend or foe? The significance of norms in a changing practice,” International Journal of Early Years Education, 20(4), pp. 422–436. Available at: https://doi.org/10.1080/09669760.2012.743876. Liu, X. and Pange, J. (2015) “Early Childhood Teachers’ Access to and Use of ICT in Teaching: The Case of Mainland China,” in. Global Learn, Association for the Advancement of Computing in Education (AACE), pp. 590–596. Available at: https://www.learntechlib.org/primary/p/150908/ (Accessed: July 9, 2025). Osman, R.B., Choo, P.S. and Rahmat, M.K. (2013) “Understanding student teachers’ behavioural intention to use technology: Technology Acceptance Model (TAM) validation and testing,” International Journal of Instruction, 6(1). Available at: https://dergipark.org.tr/en/pub/eiji/issue/5138/70018 (Accessed: July 14, 2025). Teo, T. (2009) “Modelling technology acceptance in education: A study of pre-service teachers,” Computers & education, 52(2), pp. 302–312. Tondeur, J. et al. (2017) “Understanding the relationship between teachers’ pedagogical beliefs and technology use in education: a systematic review of qualitative evidence,” Educational Technology Research and Development, 65(3), pp. 555–575. Available at: https://doi.org/10.1007/s11423-016-9481-2. Venkatesh, V. and Davis, F.D. (2000) “A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies,” Management Science, 46(2), pp. 186–204. Available at: https://doi.org/10.1287/mnsc.46.2.186.11926. Yang, T. and Hong, X. (2022) “Early childhood teachers’ professional learning about ICT implementation in kindergarten curriculum: A qualitative exploratory study in China,” Frontiers in Psychology, 13. Available at: https://doi.org/10.3389/fpsyg.2022.1008372. 16. ICT in Education and Training
Poster Digital Exclusion Among Children and Young People a Scoping Review Østfold University College, Norway Presenting Author:Digital environments have become a central part of children’s and young people’s everyday lives, shaping how they learn and participate in society (Livingstone & Third, 2017). In education, digital tools and platforms are increasingly integrated into teaching, learning, and assessment practices (OECD, 2021). Although digitalization has transformed many aspects of everyday life, access to and use of technology remains unequal. This is not just a technical issue, but a societal one, with implications for social participation, education and future economic opportunities (Helsper, 2021). While unequal access and participation can limit children and young people’s social and educational opportunities, research also shows that digital participation can involve risks such as cyberbullying, exposure to harmful content and risks for children’s wellbeing associated with extensive screen use (OECD, 2025). These nuances indicate that digital exclusion can have different consequences for children and young people. It may reduce some risks while also restricting certain forms of participation. This scoping review focuses on digital exclusion, understood as the social consequences and lived experiences that arise from unequal participation in digital and social contexts. In the literature, terms such as digital divide, digital inequality, digital equity, digital inclusion and digital exclusion are often used with overlapping meanings and without clear differentiation (Gottschalk & Weise, 2023). Clarifying these terms helps to distinguish between approaches that focus on access, skills and outcomes, and those that consider the lived consequences of unequal digital participation. Since digital engagement takes place within broader social, economic and institutional contexts (Selwyn, 2004), Helsper (2021) situates digital exclusion in relation to social inclusion emphasizing how limited digital participation both reflects and reinforces existing social disadvantages related to education, income, health and community engagement. In this sense, digital exclusion represents one dimension of wider social exclusion processes, where lack of digital engagement contributes to increasing forms of marginalization. Building on this understanding, this review specifically examines children and young people’s experiences of digital exclusion. The objective of this scoping review is to systematically map the extent, nature, and scope of existing research on how children and young people perceive digital exclusion. While much of the existing research addresses the structural dimensions of digital exclusion, fewer studies have examined children and young people’s subjective experiences of digital exclusion (Helsper, 2021). This scoping review addresses this gap by synthesizing existing research on how children and young people themselves perceive and experience digital exclusion across educational and everyday contexts. Thus, the research question is as follows: What is known from existing research about how children and young people experience digital exclusion in educational and everyday digital settings? The scoping review is conducted in accordance with the JBI guidelines for scoping reviews and aligned with the PRISMA Extension for Scoping Reviews. A total of 16 studies were included. Most used qualitative methods to explore digital exclusion in education and everyday life. Three main perspectives emerged: multi-level digital divides, child-centered experiences, and structural inequalities linked to socioeconomic, geographic, and gender factors. Common barriers included limited device and internet access, low digital skills, affordability issues, and restrictive household or school policies. Disadvantaged groups, such as rural youth, girls in restrictive environments, and children with functional variations, were most affected and often required additional support and accessible tools. This review shows that children’s digital exclusion is multi-layered, shaped by access, skills, outcomes and social context. Exclusion is not only about resources but also about opportunities, support, and recognition. The pandemic illustrated how new vulnerabilities emerge as contexts shift. While research focuses mostly on predefined “at-risk” groups, exclusion can also affect children outside these categories, highlighting the need for broader, more integrated approaches. Methodology, Methods, Research Instruments or Sources Used This scoping review is conducted in accordance with the JBI guidelines for scoping reviews (Peters et al., 2020) and the PRISMA ScR (Tricco et al., 2018). The scoping review was guided by a predefined protocol, developed to ensure a clear definition of the study’s objective and methods, and to support transparent and unbiased reporting (Peters et al., 2020). Eligibility criteria To be eligible for inclusion, studies must focus on children and young people aged 10-18 years. It is essential that the study centers on the perspectives, experiences and perceptions of the children and young people themselves. Studies that focus solely on adults, such as parents, teachers, or professionals, were excluded unless they also incorporated perspectives from children and young people. Conceptually, the focus is on digital exclusion, broadly understood as a range of structural and experiential limitations affecting children and young people’s access to and participation in digital environments. This includes barriers related to access, such as the lack of devices, connectivity, or digital infrastructure, as well as challenges related to use, including limited digital skills, insufficient support, or lack of opportunities for meaningful engagement. Search strategies A comprehensive search was conducted by a university librarian specialized in searches for review studies across four databases: Education Source, ERIC, PsycINFO and Scopus. The four databases are chosen because they together both cover education, psychology-related fields, sociology, and interdisciplinary interpretations such as combinations of e.g., social sciences and technology. No language or year limitations were applied in the search strategy. Study selection Identified publications were organized and uploaded into EndNote, and duplicates were removed. All records were imported into Rayyan (https://www.rayyan.ai/; Ouzzani et al., 2016) a web-based platform that supports blinded screening and collaboration among reviewers. Four reviewers independently screened the titles and abstracts. Conflicts were resolved through discussion through consensus meetings. Data extraction and synthesis of results Data extraction was conducted by all the authors using a structured charting form developed according to the JBI guidelines for scoping reviews (Pollock et al., 2023). For each included study, key information such as author, year, country, population, concept, context, study design, and main findings were extracted. The extraction process was iterative, allowing refinement of categories as familiarity with the data increased. The data were summarized using a qualitative synthesis approach. A critical appraisal was not conducted, as the purpose was to map the literature rather than to assess its quality. Conclusions, Expected Outcomes or Findings The reviewed studies reveal a diverse set of approaches to understand digital exclusion, ranging from structural models of the digital divide (Jovita et al., 2021; Mkhize & Davids, 2021) to relational and perspectives focusing on everyday digital practices and interactions (Caton et al., 2023; Jones et al., 2022). Across these perspectives, digital exclusion is conceptualized as a multi-layered phenomenon that operates at all three levels of the digital divide: access, skills and outcomes (Van Deursen & Helsper, 2015). It is notable that fifteen of the sixteen reviewed studies focused on children and young people living in circumstances associated with disadvantages, including those living in poverty, being refugees, or those with having functional variations. While this focus is both necessary and valuable, it also means that we know less about how digital exclusion may be experienced among children who are not typically considered vulnerable. Feelings of exclusion can arise simply through everyday social interactions, such as peer rejection, and are not limited to situations of broader structural disadvantage (Killen et al., 2013), raising questions about how digital exclusion is defined and recognized in research, and whether certain forms of exclusion remain hidden when studies seem to primarily include predefined groups at risk. The reviewed studies imply that no single theoretical framework adequately captures the complexity of children and young people ’s digital exclusion. Structural, relational, capability-based and rights-informed perspectives each explain different aspects of how inequalities and exclusion are produced, negotiated and experienced. Integrated models, such as the one advanced by Helsper (2021), offer a direction by linking structural domains with personal resources and lived experiences within a clear analytical framework. Future research should further develop such integrative frameworks and prioritize children and young people`s own perspectives, including how they navigate digital and offline exclusion and inclusion across the life course. References Gottschalk, F., & Weise, C. (2023). Digital equity and inclusion in education: An overview of practice and policy in OECD countries (OECD Education Working Papers No. 299; OECD Education Working Papers, Vol. 299). https://doi.org/10.1787/7cb15030-en Helsper, E. J. (2021). The Digital Disconnect: The Social Causes and Consequences of Digital Inequalities. SAGE Publications Ltd. https://doi.org/10.4135/9781526492982 Jones, N., Devonald, M., Dutton, R., Baird, S., Yadete, W., & Gezahegne, K. (2022). Disrupted education trajectories: Exploring the effects of Covid-19 on adolescent learning and priorities for “building back better” education systems in Ethiopia. Development Policy Review, 40. (rayyan-275360136). https://doi.org/10.1111/dpr.12607 Livingstone, S., & Third, A. (2017). Children and young people’s rights in the digital age: An emerging agenda. New Media & Society, 19(5), 657–670. https://doi.org/10.1177/1461444816686318 OECD. (2021). OECD Digital Education Outlook 2021: Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots. OECD Publishing. https://doi.org/10.1787/589b283f-en OECD. (2025). How’s Life for Children in the Digital Age? OECD Publishing. https://doi.org/10.1787/0854b900-en Peters, M. D. J., Marnie, C., Tricco, A. C., Pollock, D., Munn, Z., Alexander, L., McInerney, P., Godfrey, C. M., & Khalil, H. (2020). Updated methodological guidance for the conduct of scoping reviews. JBI Evidence Synthesis, 18(10). https://journals.lww.com/jbisrir/fulltext/2020/10000/updated_methodological_guidance_for_the_conduct_of.4.aspx Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., … Straus, S. E. (2018). PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Annals of Internal Medicine, 169(7), 467–473. https://doi.org/10.7326/M18-0850 Van Deursen, A. J. A. M., & Helsper, E. J. (2015). The Third-Level Digital Divide: Who Benefits Most from Being Online? In Communication and Information Technologies Annual (pp. 29–52). Emerald Group Publishing Limited. https://doi.org/10.1108/S2050-206020150000010002 16. ICT in Education and Training
Poster What Students Ask Matters: Static and Longitudinal Questioning Patterns in Student–LLM Dialogue and Cognitive Gains 1: The Chinese University of Hong Kong, Shenzhen, China; 2: East China Normal University Presenting Author:This symposium focuses on how higher education students learn through dialogue with large language models (LLMs), with particular attention to student questioning as the interaction mechanism that steers inquiry, shapes epistemic engagement, and ultimately influences higher-order learning outcomes. As LLMs become widely available across institutions and languages, universities face a shared challenge: moving from adoption and policy debates to evidence-based design of learning activities that reliably support critical and creative thinking. 1. Research questions RQ1: What kinds of questioning practices do students enact in student–LLM dialogue across disciplines and task types? 2. Objective 3. Conceptual framework 4. Methodological approach 5. Intended purpose of the discussion Methodology, Methods, Research Instruments or Sources Used This study uses a sequential explanatory mixed-methods design to examine how students’ questioning develops during student–LLM philosophical dialogue and how different questioning patterns relate to perceived gains in critical thinking (CT) and creative thinking (CrT). The context is a required general education course in which first-year postgraduate students completed an end-of-semester dialogue task with an institutional ChatGPT model embedded in the university learning platform. The final analytic sample included 106 students, selected via questionnaire screening to ensure comparable baseline levels of prior GenAI/LLM experience, AI-related knowledge, familiarity with AI tools, and AI-use frequency. Data sources and instruments include: (a) student–LLM dialogue transcripts (chat logs) submitted as part of the course task; (b) a post-task online questionnaire measuring perceived CT and CrT gains using two 5-item, 6-point Likert scales adapted from established instruments; and (c) rubric-based performance scoring of submitted dialogue products using an analytic critical thinking rubric and a creative thinking rubric aligned with TTCT criteria. Analytic procedures proceeded in four phases. First, dialogue transcripts were imported into qualitative analysis software and all student questions were coded using a deductive framework aligned with the revised Bloom’s taxonomy (factual, explanatory, application, analytical, evaluative, exploratory). Coding reliability was established on a randomly selected subset using two independent coders, followed by discussion-based reconciliation. Second, question-type proportions per student were computed and used as continuous indicators for latent profile analysis to identify distinct static questioning profiles. Third, question-level data were organized by conversation rounds and modeled using group-based trajectory modeling to identify longitudinal questioning trajectories. Fourth, students were assigned to profiles and trajectory groups based on posterior probabilities, and subgroup differences in perceived CT and CrT gains were tested using appropriate non-parametric and parametric comparisons depending on distributional assumptions. Conclusions, Expected Outcomes or Findings Across 106 postgraduate students’ student–LLM philosophical dialogues, the study identified clear heterogeneity in questioning and its links to perceived cognitive gains. Latent profile analysis revealed three static questioning profiles: Fact-focused, Explanation-focused, and Evaluation-focused Questioners. Students in the Fact-focused profile reported significantly lower perceived gains in critical thinking (CT) and creative thinking (CrT) than the other two profiles, while Explanation-focused and Evaluation-focused students reported similarly high gains. Group-based trajectory modeling further identified two developmental patterns across conversation rounds, with a key divergence around Rounds 5–7: one group plateaued at application and analytical questioning, whereas the other progressed toward evaluative and exploratory questioning. Students who plateaued reported significantly lower perceived CT gains than those who advanced to higher-order questioning. Overall, the findings indicate that benefits from student–LLM dialogue depend on both what students ask and how their questioning develops over time, highlighting mid-dialogue stagnation as a practical target for instructional scaffolding. References Assaly, I. R., & Smadi, O. M. (2015). Using bloom’s taxonomy to evaluate the cognitive levels of master class textbook’s questions. English Language Teaching, 8(5), 100–110. https://doi.org/10.5539/elt.v8n5p100 Baek, C., Tate, T., & Warschauer, M. (2024). “ChatGPT seems too good to be true”: College students’ use and perceptions of generative AI. Computers and Education: Artificial Intelligence, 7, 100294. https://doi.org/10.1016/j.caeai.2024.100294 Belkina, M., Daniel, S., Nikolic, S., Haque, R., Lyden, S., Neal, P., Grundy, S., & Hassan, G. M. (2025). Implementing generative AI (GenAI) in higher education: A systematic review of case studies. Computers and Education: Artificial Intelligence, 8, 100407. https://doi.org/10.1016/j.caeai.2025.100407 Bloom, B. S., Engelhart, M. D., Furst, E., Hill, W. H., & Krathwohl, D. R. (1956). Handbook I: Cognitive domain. David McKay. Chin, C. (2002). Open investigations in science: Posing problems and asking investigative questions. https://repository.nie.edu.sg/handle/10497/301 Chin, C., & Osborne, J. (2008). Students’ questions: A potential resource for teaching and learning science. Studies in Science Education, 44(1), 1–39. https://doi.org/10.1080/03057260701828101 Du, X., Du, M., Zhou, Z., & Bai, Y. (2025). Facilitator or hindrance? The impact of AI on university students’ higher-order thinking skills in complex problem solving. International Journal of Educational Technology in Higher Education, 22(1), 39. https://doi.org/10.1186/s41239-025-00534-0 Essel, H. B., Vlachopoulos, D., Essuman, A. B., & Amankwa, J. O. (2024). ChatGPT effects on cognitive skills of undergraduate students: Receiving instant responses from AI-based conversational large language models (LLMs). Computers and Education: Artificial Intelligence, 6, 100198. https://doi.org/10.1016/j.caeai.2023.100198 Facione, P. A. (1990). Critical thinking: A statement of expert consensus for purposes of educational assessment and instruction (No. ED 315 423; American Philosophical Association Delphi Research Report). https://stearnscenter.gmu.edu/wp-content/uploads/12-The-Delphi-Report-on-Critical-Thinking.pdf Gonsalves, C. (2024). Generative AI’s impact on critical thinking: Revisiting bloom’s taxonomy. Journal of Marketing Education, 02734753241305980. https://doi.org/10.1177/02734753241305980 16. ICT in Education and Training
Poster Artificial Intelligence Governance and Policy Challenges in European Higher Education Babes-Bolyai University of Cluj-Napoca, Romania Presenting Author:The rapid expansion of Artificial Intelligence (AI) is generating significant governance and policy challenges for higher education institutions across Europe. Universities are no longer only sites of technological adoption but key actors in shaping ethical standards, regulatory frameworks, and institutional strategies related to AI. This proposal examines how AI is influencing governance structures, policy development, and decision-making processes in European higher education, with particular attention to accountability, academic integrity, data governance, and institutional autonomy. The main research question guiding this contribution is: How are European higher education institutions and policy frameworks responding to the governance challenges posed by Artificial Intelligence? This question is complemented by subsidiary inquiries into (a) the alignment between European-level AI regulations and institutional policies, (b) the role of university leadership and governance bodies in regulating AI use, and (c) the tensions between innovation, regulation, and academic values. The primary objective of the presentation is to analyse AI as a governance issue rather than solely a pedagogical or technological innovation. It aims to explore how AI reshapes institutional responsibilities, redistributes decision-making power, and challenges existing policy instruments within higher education systems. A particular focus is placed on how universities interpret and operationalise European policy initiatives related to AI, such as ethical guidelines, data protection regulations, and emerging AI governance frameworks. The conceptual framework draws on higher education governance theory, public policy analysis, and sociotechnical perspectives. AI is conceptualised as a policy object that is co-constructed through regulatory discourse, institutional strategy, and everyday organisational practices. This approach allows for an examination of governance at multiple levels: supranational (European Union), national higher education systems, and institutional governance structures. Methodologically, the study adopts a comparative European perspective, integrating empirical data from multiple countries and institutional contexts. This cross-national approach highlights how different governance traditions and regulatory environments shape institutional responses to AI. By situating institutional practices within broader European and international policy debates, the proposal aligns with EERA’s emphasis on comparative and policy-oriented educational research. The intended purpose of the discussion is to contribute to scholarly and policy debates on AI governance in higher education. The presentation seeks to inform policymakers, institutional leaders, and researchers about emerging governance models and to support more coherent, transparent, and value-driven AI policies within European higher education. Methodology, Methods, Research Instruments or Sources Used The study employs a mixed-methods research design with a strong emphasis on policy and governance analysis. Data collection consists of three interconnected components. First, a qualitative document analysis is conducted on European-level policy documents, including AI ethical guidelines, higher education policy reports, and regulatory frameworks related to data protection and digitalisation. In parallel, institutional policy documents such as AI strategies, codes of academic integrity, and governance guidelines from selected European universities are analysed to identify dominant governance approaches and regulatory discourses. Second, semi-structured interviews are carried out with key governance actors, including university leaders, policy officers, quality assurance managers, and educational policymakers. These interviews explore institutional decision-making processes, perceived governance challenges, and interpretations of European AI policies. The interviews provide insight into how governance responsibilities are negotiated within and across institutional levels. Third, a targeted survey of academic staff and administrators complements the qualitative data by capturing perceptions of policy clarity, institutional support, and governance effectiveness regarding AI use in teaching, assessment, and research. Comparative analysis is used to identify similarities and differences across national and institutional contexts. Data are analysed using thematic and policy discourse analysis techniques, allowing for triangulation across sources. Ethical approval procedures and data protection requirements are strictly followed in accordance with European research ethics standards. Conclusions, Expected Outcomes or Findings The expected findings indicate that AI governance in European higher education is characterised by fragmentation and policy uncertainty. While European-level initiatives provide broad ethical and regulatory orientations, their translation into institutional policies remains uneven and, in many cases, reactive rather than strategic. The study is expected to identify tensions between institutional autonomy and regulatory compliance, particularly regarding academic integrity, data governance, and accountability. Many institutions appear to rely on informal practices and ad hoc guidelines rather than coherent governance frameworks, placing additional responsibility on individual academics and students.Cross-national differences are anticipated, reflecting diverse governance traditions, levels of policy maturity, and resource capacities. The findings will highlight the need for multi-level governance approaches that better align European policy objectives with institutional decision-making processes. The conclusions will emphasise that effective AI integration in higher education requires not only technological innovation but also robust governance structures grounded in European educational values. The presentation will contribute policy-relevant insights for developing more transparent, consistent, and sustainable AI governance models within European higher education. References European Commission. (2022). Ethical guidelines on the use of artificial intelligence and data in teaching and learning for educators. Publications Office of the European Union. European Commission. (2021). Coordinated plan on artificial intelligence 2021 review. Publications Office of the European Union. Kulikowski, K., Przytuła, S., & Sułkowski, Ł. (2023). Artificial intelligence and human decision-making in higher education. Higher Education, 86(2), 251–268. Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1), 39. European Commission. (2024). Artificial Intelligence Act: Implications for education and training systems. Publications Office of the European Union. OECD. (2023). Artificial intelligence in education: Challenges and opportunities for policy. OECD Publishing. UNESCO. (2023). Guidance for generative AI in education and research. UNESCO. Williamson, B., & Eynon, R. (2023). The datafication of education governance: AI, algorithms and the re-making of higher education. Learning, Media and Technology, 48(1), 1–15. 16. ICT in Education and Training
Poster An Empirical Study on Mentor Support in a Creative Learning Environment 1: Nagasaki University / JAPAN; 2: Nagasaki Junshin Catholic University / JAPAN Presenting Author:This study investigates the actual conditions and challenges of mentor support in a creative learning environment established through a collaborative project between Nagasaki University and Nagasaki City. The learning environment was designed as a place where learners could engage with advanced technologies such as virtual reality (VR), 3D printers, and drones in a playful and exploratory manner. The program targeted students from Grade 5 of elementary school to Grade 12 of high school and was operated as an industry–government–academia collaborative initiative. In this learning environment, undergraduate and graduate students served as mentors and provided support to promote learners’ autonomous and creative learning. While creative learning environments such as FabLabs have been widely recognized as effective settings for fostering creativity, critical thinking, collaboration, and other 21st-century skills within the context of STEAM education, the specific roles and support practices of mentors in such environments remain insufficiently examined, particularly in Japan. To address this gap, this study aims to clarify the actual practices of mentor support and to identify challenges faced by mentors when supporting diverse learners in a creative learning environment. After the completion of the four-month program in the 2024 academic year, a questionnaire survey was conducted with mentors who were involved in learner support and program operation. The survey focused on multiple aspects of mentor support, including overall activity support, support for the use of digital devices, interaction and attitudes toward others, and mentors’ own growth and learning. Responses were collected using a four-point Likert scale, and free-response questions were included to capture mentors’ perceptions of difficulties and situations requiring particular support. The results of the survey revealed that mentors perceived their support to be sufficient in areas such as explaining activity rules, providing basic instructions on how to use digital devices, responding to learners’ questions, and supporting hands-on and creative activities. These findings suggest that mentors were able to actively engage with learners and facilitate exploratory learning during the initial and experiential phases of activities. In addition, mentors reported positive engagement in stimulating learners’ intellectual curiosity and supporting trial-and-error processes. However, challenges were identified in areas related to collaborative learning support, activity reflection, goal setting, and inquiry-based learning. The results indicated no significant bias toward positive responses in these areas, suggesting that mentors may have lacked sufficient awareness or pedagogical expertise to support deeper learning processes. Furthermore, although mentors reported that they independently learned how to use digital devices, there was a noticeable bias in the types of devices they felt confident supporting. Devices with which mentors had less experience tended to receive lower levels of perceived support. Free-response data further highlighted issues such as a shortage of mentors during peak participation times, insufficient information sharing among mentors, and gaps in mentors’ knowledge and skills. In particular, mentors noted inconsistencies in explanations of device usage and difficulties in guiding learners from experiential activities, such as VR experiences, toward more creative activities like 3D modeling or game development. Overall, this study clarifies both the strengths and limitations of mentor support in a creative learning environment. The findings suggest the need for improved support systems to facilitate the transition from experiential to creative learning, enhanced information-sharing mechanisms among mentors, and structured opportunities for mentors to develop technical and pedagogical competencies. Methodology, Methods, Research Instruments or Sources Used This study was conducted in a creative learning environment established on the campus of Nagasaki University through a collaborative project with the Nagasaki City Information Policy Promotion Department. The program was operated from July to October 2024 as an industry–government–academia collaboration. Participants were students from Grade 5 of elementary school to Grade 12 of high school, primarily recruited from schools in Nagasaki City. A total of 141 learners participated in the program, including 85 elementary school students, 47 junior high school students, and 9 high school students. The learning environment was equipped with a variety of digital devices, including VR headsets, 3D printers, drones, humanoid robots with emotion recognition functions, and programmable robotic vehicles. In addition, laptop computers and tablet devices were provided with software such as 3D modeling tools, an integrated game development engine, and a sandbox-style video game. During each session, three mentors—undergraduate and graduate students from the Faculty of Information and Data Sciences and the Graduate School of Engineering—supported learners’ activities. A staff member from Nagasaki City was also present to ensure safety and operational stability. To investigate the actual conditions of mentor support, a questionnaire survey was administered in October 2024 after the completion of the four-month program. The participants of the survey were 14 mentors who were involved in learner support and program operation. The questionnaire consisted of four sections: support for overall activities (13 items), support for the use of digital devices (14 items), interaction and attitudes toward others (7 items), and mentors’ own growth and learning (6 items). Each item was evaluated using a four-point Likert scale ranging from “strongly agree” to “strongly disagree.” In addition to the scaled items, open-ended questions were included to collect qualitative data regarding difficulties encountered when supporting learners and situations in which mentors felt that particular support was required. Responses to the Likert-scale items were classified into positive and negative categories. A binomial test was conducted to examine the statistical significance of differences in response distributions. Conclusions, Expected Outcomes or Findings This study examined the actual conditions of mentor support in a creative learning environment for students ranging from elementary to high school levels. The results of a questionnaire survey conducted with undergraduate and graduate student mentors revealed that support related to activity rules, basic operation of digital devices, and responses to learners’ questions was generally sufficient. Mentors actively engaged in supporting experiential and creative activities, contributing to learners’ exploratory learning processes. However, the findings also identified several challenges. Support for goal setting, reflection on activities, collaborative learning, and inquiry-based learning was found to be insufficient. In addition, although mentors reported that they independently learned how to use digital devices, there was a noticeable bias in the types of devices utilized for learner support. These results suggest that mentors’ prior experience and familiarity with specific technologies influenced the range of support provided. Furthermore, mentors reported challenges related to insufficient information sharing among mentors, shortages in staffing during busy sessions, and difficulties in guiding learners from experiential activities, such as VR experiences, toward more creative activities such as 3D modeling and game development. While mentors perceived communication among themselves to be adequate, inconsistencies in explanations and support practices highlighted the need for more structured and practice-oriented information sharing. Overall, the results indicate that the support system for facilitating the transition from experiential learning to creative learning was not sufficiently developed. To improve the quality of learner support in creative learning environments, the introduction of concrete tools and mechanisms for guiding learners toward creative activities, regular information sharing among mentors, and structured lectures or training sessions on advanced digital tools are necessary. Future work should focus on the growth of mentors through systematic training in both digital technologies and learner support strategies. References Davies, S., & Seitamaa-Hakkarainen, P. (2024). Research on K–12 maker education in the early 2020s: A systematic literature review. International Journal of Technology and Design Education, 35(2), 763–788. https://doi.org/10.1007/s10798-024-09921-6 Dignam, C. (2024). Makerspace and the 5 C’s of learning: Constructing, collaborating, communicating, critically thinking, and creatively thinking. International Journal of Educational Technology, 6(1), 104–127.https://doi.org/10.46328/ijses.135 Fior, G., Fonda, C., & Canessa, E. (2025). Hands-on STEM learning experiences using digital technologies. Journal of STEM Learning, 29(2), 112–129.https://doi.org/10.3934/steme.2025009 Ioannou, A., Makri, M., & Evangelou, C. (2024). Understanding, practicing, and assessment of 21st-century skills for learners in makerspaces and FabLabs. Education and Information Technologies, 30(7), 8829–8846. https://doi.org/10.1007/s10639-024-13178-w Otieno, C. (2017). Makerspaces: A qualitative look into makerspaces as innovative learning environments (Doctoral dissertation). University of Colorado. Papagiannis, P., & Pallaris, G. (2024). Evaluating 21st century skills development through makerspace workshops in computer science education. arXiv. https://arxiv.org/abs/2411.05012 Quintana-Ordorika, A., Garay-Ruiz, U., & Portillo-Berasaluce, J. (2024). A systematic review of the literature on maker education and teacher training. Education Sciences, 14(12), Article 1310. https://doi.org/10.3390/educsci14121310 Roldan, W., Hui, J., & Gerber, E. M. (2018). University makerspaces: Opportunities to support equitable participation for women in engineering. International Journal of Engineering Education, 34(2), 751–768. Unterfrauner, E., Voigt, C., & Hofer, M. (2021). The effect of maker and entrepreneurial education on self-efficacy and creativity. International Journal of STEM Education, 8(1), Article 48. https://doi.org/10.1186/s40594-021-00287-1 Vossoughi, S., & Bevan, B. (2014). Making and tinkering: A review of the literature. National Research Council Committee on Out-of-School Time STEM. 16. ICT in Education and Training
Poster Artificial Intelligence in Creative Writing - Project "wrAIte" University Klagenfurt, Austria Presenting Author:Artificial intelligence is changing the way people write, learn, and express themselves. In creative writing, AI opens up new possibilities for adult learners and teachers. Artificial intelligence, for example, is creating new conditions as it finds its way into various areas of life, including education. As a result, adult educators currently need knowledge about how to use generative large language models in order to support creative writing processes and apply them to the learning process of participants in an enriching way. The goal is to guide and empower learners. "wrAIte" explores the potential and challenges of AI-assisted creative writing in educational settings. This European research project aims to equip adult educators with the necessary knowledge and appropriate tools to integrate AI into their teaching methods, ensuring that technology enhances human creativity rather than replacing it. wrAIte has four main objectives: To explore the potential and limitations of AI as a tool in creative writing for adult learners. To expand the knowledge and skills of adult educators in using innovative digital tools. To empower adult educators to incorporate AI-supported creative writing into their educational offerings. To promote AI-supported creative writing as a means of strengthening learners' expressive abilities, digital literacy, and creativity. Methodology, Methods, Research Instruments or Sources Used When implementing the project, participatory research is an essential tool for ensuring the quality of the internal process and involving those affected and making them participants in the research. In contrast to traditional research methods, which aim to establish a clear separation between researchers and research subjects, it deliberately promotes collaboration between scientists and practitioners. Key characteristics are: Active and collaborative participation: includes the joint development of research questions, data collection, and analysis and interpretation of results. Open dialogue: Participatory research promotes the exchange of knowledge. Empowerment: Enabling participants to learn more about their context and contribute their perspectives. Action-oriented results: Beyond pure knowledge generation, participatory research strives to improve concrete social situations. Conclusions, Expected Outcomes or Findings First results from expert interviews show that artificial intelligence can be used in adult education settings to help identify inconsistencies and contradictions in texts and broaden writers' perspectives. Artificial intelligence can provide the central theme of a creative text and thus serve as a framework. The content is always provided by the writers,because artificial intelligence cannot replace human thinking. In this regard, it is important that collaboration between humans and machines is implemented. This way, machines can provide initial inspiration and humans can develop creative content based on their own experiences and emotions. This is how self-expression and empowerment can be achieved in the adult learning process. References Brandt, E. (2012). Schreiben befreit! Ziele und Wirkung schreibpädagogischer Seminarkonzepte in der Erwachsenenbildung (Writing liberates! Aims and impact of writing pedagogy seminar concepts in adult education). Magazin erwachsenenbildung.at, 15. https://doi.org/10.25656/01:7455 Knowles, M. (1980). The modern practice of adult education: From pedagogy to andragogy. (Rev. and updated.). Cambridge Adult Education. https://archive.org/details/modernpracticeof0000know_j2r4/page/n5/mode/2up Rauter, E., Wetschanow, K., & Logar, Y. (2024). Writing support with or despite AI? Journal for Interdisciplinary Writing Research, 11, 42-55. https://doi.org/10.48646/zisch.241103 Sinelnikova, V., Ivchenko, T., Pistunova, T., Regesha, N., & Skazhenyk, M. (2022). Enhancing the performance of Andragogic education. Journal of Curriculum and Teaching, 11(1), 245-254. https://doi.org/10.5430/jct.v11n1p245 Unger, H. Von. (2012). Participatory Health Research: Who Participates in What? Forum: Qualitative Social Research, 13(1). https://doi.org/10.17169/fqs-13.1.1781 von Hippel, A., Kulmus, C., & Stimm, M. (2022). Didaktik der Erwachsenen- und Weiterbildung (Didactics of adult and continuing education [2nd ed.]). https://doi.org/10.36198/9783838559568 Wang, V., Torrisi-Steele, G., & Reinsfield, E. (2020). Transformative learning, epistemology and technology in adult education. Journal of Adult and Continuing Education, 27(2), 324-340. https://doi.org/10.1177/1477971420918602 wrAIte. (2025). Artificial Intelligence in creative writing. Project overview. https://wraiteproject.eu/ 16. ICT in Education and Training
Poster Digital Competencies in Inclusive Early Primary Education: Insights from Digital Storytelling Julius-Maximilians-Universität Würzburg, Germany Presenting Author:The landscape of education research is increasingly shaped by new constellations of actors, data‑driven technologies, and shifting expectations regarding the societal role of academic knowledge. The digital transformation profoundly affects both society and schooling, and as digital technologies permeate everyday life, schools face the responsibility of enabling students to use digital media competently, reflectively, and responsibly (Irion, 2018). Against this backdrop, the question of how students’ digital competencies are conceptualized and fostered in schools becomes especially salient. When examining the concrete competencies students need for engaging with digital media, it becomes evident that a wide range of complex, overlapping, and interdisciplinary discourses converge in this field (Mensonides et al., 2024). Within this landscape, the present contribution focuses specifically on the discourse surrounding digital competence, which investigates the knowledge, skills, and attitudes necessary for the effective and meaningful use of digital media across diverse learner groups. Existing frameworks conceptualize digital competencies across multiple domains, including creating, problem solving, selecting, operating, communicating, informing, and protecting (e.g., Ackermans et al., 2023; Pedaste et al., 2023; Vuorikari et al., 2022). Although a substantial body of research has developed models of digital competence for secondary education (e.g., Mensonides et al., 2024) and an increasing number of studies now examine digital competencies at the end of primary schooling (e.g., Köhn et al., 2020), the early primary years remain a largely underexplored area. Competence domains for this age group are often normatively defined rather than empirically grounded (e.g., Medienberatung NRW). A systematic review by Godaert et al. (2022) underscores this gap by highlighting that “basic technical skills”, which serve as prerequisites for the appropriate use of digital media, are still insufficiently specified. This challenge is even more pronounced in inclusive primary education, where digital precursor skills must be formulated in ways that do not inadvertently exclude children with intellectual disabilities or limited literacy skills (Geuting & Keeley, 2023). Despite these demands, it remains unclear how digital competence domains can be meaningfully specified for this specific group of learners in inclusive early primary education (Geuting & Keeley, 2023; Godaert et al., 2022). This poster addresses this research gap by delineating and refining digital competence domains for inclusive early primary education through the instructional approach of digital storytelling. By examining how young learners—with and without intellectual disabilities—participate in and shape digital storytelling processes, the study seeks to derive competence domains that are both developmentally appropriate and pedagogically meaningful for the early primary years. To pursue this aim, two research questions are formulated:
Methodology, Methods, Research Instruments or Sources Used To this end, a qualitative video study was conducted in eight inclusive first and second grade inclusive classrooms in Bavaria. In these learning environments, students with and without intellectual disabilities collaboratively continued a literary story in pairs or small groups. They worked with both conventional modes of expression (e.g., drawing or writing with pencil) and extended multimodal forms (e.g., audio recordings) using the Book Creator app on iPads. This design allowed for a rich documentation of how students engaged with digital storytelling tools and how they navigated multimodal meaning making processes. Videography captured the instructional setting from three complementary perspectives: the small group interactions, the screencasts of students’ work on the iPads, and the overall classroom activity. In total, video data from 38 students and eight teachers were collected. These recordings were subsequently processed in DaVinci Resolve, including cropping, anonymizing, and synchronizing the different camera angles. The prepared material was then transcribed verbally and organized for analysis using the Interact software. The analytical focus lay on coded instructional situations related to individual cognitive activation (Warmdt et al., 2025), as deeper thinking processes are closely linked to competence development and therefore directly relevant for understanding digital competence domains (Klieme et al., 2006). Using content structuring qualitative content analysis, an inductive deductive category system was developed (Kuckartz & Rädiker, 2024). In an iterative process, and through consensual coding, digital competence domains identified in existing publications (e.g., Ackermans et al., 2023) were concretized and expanded based on inductive video examples from the inclusive small group digital storytelling activities. The five resulting main categories were differentiated into three sublevels and specified through detailed descriptions, coding rules, anchor examples, and delimitation criteria. Subsequently, these categories were applied to the data and analyzed both for the entire class (research question 1: n = total number of codings) and for individual students (research question 2: nØ = average number of codings per student). Conclusions, Expected Outcomes or Findings Based on the video analyses, five competence domains can be delineated for the specific target group (research question 1). These domains represent core dimensions of students’ digital engagement during digital storytelling: Creating a Media Product (e.g., shaping content through representational forms), Using Media Functions (e.g., operating a medium via basic functions), Selecting a Medium (e.g., choosing a digital tool based on verbal guidance), Solving Media‑Related Problems (e.g., responding to media‑related difficulties triggered by irritation), and Basic Media Experience (e.g., observing a peer’s mediated negotiation process). Across all observations, 474 instructional situations address these digital competencies. Students most frequently create media products (n=149), solve media‑related problems (n=126) or use media functions (n=101). Selecting a medium occurrs in about 15% of cases (n=73), whereas basic media experiences are comparatively rare (n=25). A closer examination of individual student engagement reveals clear differences between children with and without intellectual disabilities (research question 2). Children without intellectual disabilities show almost twice as many codings (nØ=17.71) as those with intellectual disabilities (nØ=9.15). Qualitative differences are evident as well. Children with intellectual disabilities exhibit digital competencies across all five domains: they primarily use media functions (nØ=2.61), followed by creating media products (nØ=2.00), solving problems (nØ=1.56), selecting media (nØ=1.49), and gaining basic media experiences (nØ=1.49). In contrast, students without intellectual disabilities demonstrate activity in only four domains: they frequently create media products (nØ=6.55), often solve media-related problems (nØ=5.03), and less frequently use media functions (nØ=3.16) and select a medium (nØ=2.98). Overall, these findings highlight the need for a differentiated perspective on digital competencies in inclusive primary classrooms. They also point to overlaps and discrepancies with existing digital competence models in secondary education (e.g., Ackermans et al., 2023) and emphasize the need for future research to examine the stability and specificity of these competence domains in digital storytelling. References Ackermans, K., Bakker, M., Gorissen, P., van Loon, A.‑M., Kral, M. & Camp, G. (2024). Development and validation of a test for measuring primary school students’ effective use of ICT: The ECC‐ICT test. Journal of Computer Assisted Learning, 40(3), 960–972. https://doi.org/ 10.1111/jcal.12924 Geuting, J. & Keeley, C. (2023). Chancen und Herausforderungen digitaler Bildung für Schüler:innen mit dem Förderschwerpunkt geistige Entwicklung. In D. Ferencik-Lehmkuhl, I. Huynh, C. Laubmeister, C. Lee, C. Melzer, I. Schwank, H. Weck & K. Ziemen (Hrsg.), Dokumentarische Schulforschung. Inklusion digital! Chancen und Herausforderungen inklusiver Bildung im Kontext von Digitalisierung (S. 94–110). Julius Klinkhardt. Godaert, E., Aesaert, K., Voogt, J. & van Braak, J. (2022). Assessment of Students’ Digital Competences in Primary School: A Systematic Review. Education and Information Technologies, 27, 9953–10011. https://doi.org/10.1007/s10639-022-11020-9 Irion, T. (2018). Wozu digitale Medien in der Grundschule? Sollte das Thema Digitalisierung in Grundschulen tabuisiert werden? Grundschule aktuell: Zeitschrift des Grundschulverbandes(142), 3–7. https://doi.org/10.25656/01:15574 Klieme, E., Lipowsky, F., Rakoczy, K. & Ratzka, N. (2006). Qualitätsdimensionen und Wirksamkeit von Mathematikunterricht: Theoretische Grundlagen und ausgewählte Ergebnisse des Projekts "Pythagoras". In M. Prenzel (Hrsg.), Untersuchungen zur Bildungsqualität von Schule: Abschlussbericht des DFG-Schwerpunktprogramms (S. 127–146). Waxmann. Köhn, V., Fricke, K., Todorova, M. & Windt, A. (2020). Disparitäten bei Grundschulkindern bezüglich computer- und informationsbezogener Kompetenzen im Bereich Produzieren und Präsentieren. Zeitschrift für Grundschulforschung, 13, 47–64. https://doi.org/10.1007/s42278-019-00067-2 Kuckartz, U. & Rädiker, S. (2024). Qualitative Inhaltsanalyse. Methoden, Praxis, Umsetzung mit Software und künstlicher Intelligenz (6. Aufl.). Beltz Verlagsgruppe. Medienberatung NRW. (2020). Medienkompetenzrahmen NRW. https://medienkompetenzrahmen.nrw/fileadmin/pdf/LVR_ZMB_MKR_Broschuere.pdf Mensonides, D., Smit, A., Talsma, I., Swart, J. & Broersma, M. (2024). Digital Literacies as Socially Situated Pedagogical Processes: Genealogically Understanding Media, Information, and Digital Literacies. Media and Communication, 12, Artikel 8174. https://doi.org/10.17645/mac.8174 Pedaste, M., Kallas, K. & Baucal, A. (2023). Digital competence test for learning in schools: Development of items and scales. Computers & Education, 203, 104830. https://doi.org/10.1016/j.compedu.2023.104830 Vuorikari, R., Kluzer, S. & Punie, Y. (2022). DigComp 2.2: The Digital Competence Framework for Citizens [With New Examples of Knowledge, Skills and Attitudes]. Publications Office of the European Union. 10.2760/115376 Warmdt, J., Frisch, H., Kindermann, K., Pohlmann-Rother, S. & Ratz, C. (2025). Medienkompetenzen in inklusiven Grundschulklassen im Bereich Digital Storytelling. In A. Füting-Lippert, M. Eisenmann, S. Grafe, H.-S. Siller & T. Trefzger (Hrsg.), Digitale Medien in Lehr-Lern-Konzepten der Lehrpersonenbildung in interdisziplinärer Perspektive: Ergebnisse des Forschungsprojekts Connected Teacher Education (S. 103–120). Springer. 16. ICT in Education and Training
Poster Lesson Study as an Approach to Facilitate the Integration of Gen-AI into EFL Curriculum Design in Higher Education Leiden University, Netherlands, The Presenting Author:Lesson Study as an Approach to Facilitate the Integration of Gen-AI into EFL Curriculum Design in Higher Education
Objectives or Purposes:
This study aims to investigate how university EFL teachers systematically integrate Generative Artificial Intelligence (Gen-AI) tools into curriculum design using the Lesson Study (LS) framework.
The specific objectives are:
l To identify Gen-AI’ s key pedagogical functions discovered by teachers during LS cycles.
l To analyze patterns of teacher collaboration in applying Gen-AI to curriculum design.
l To reveal how LS fosters critical reflection on Gen-AI and improves teacher professional development.
Through a qualitative analysis of teacher interaction with Kimi chat documents, experiences, and reflections during the LS process, this research contributes to the growing body of knowledge on Gen-AI integration in EFL higher education. The findings offer insights into the effective use of Gen-AI tools in EFL curriculum design, paving the way for more informed and innovative teaching practices in the era of artificial intelligence.
Perspective(s) or Theoretical Framework:
Lesson Study (LS), a professional development approach originating in Japan, offers a promising framework for addressing these challenges through its emphasis on collaborative inquiry, reflective practice, and continuous improvement (Camilleri & Calleja, 2023; Huang et al., 2017; Lewis et al., 2012). At its core, LS operates through a systematic cycle of 'research-planning-implementation-reflection' (Lewis, 2012). Research suggests that the LS framework can effectively support Gen-AI integration across multiple dimensions (Huang et al., 2024). First, it provides comprehensive pedagogical support by helping teachers systematically develop targeted educational materials, from customized lesson plans and learning activities to assessment tools. Through its collaborative structure, the framework enables teachers to engage in joint planning and reflection, fostering professional growth through innovative teaching approaches and contextual design. The collective nature of LS also facilitates robust data analysis and personalized feedback mechanisms, promoting resource sharing and detailed improvement documentation.
Second, the iterative cycle of LS offers a structured pathway for integrating four fundamental curriculum components: learning objectives, content, activities, and assessment. Each phase serves a distinct purpose: the research phase guides teachers in identifying content and assessment criteria based on student needs; the planning phase focuses on optimizing teaching activities and materials; the implementation phase captures teaching outcomes through systematic observation; and the reflection phase enables teachers to synthesize these components into a cohesive instructional framework.
Moreover, Gen-AI can serve as a 'knowledgeable other' within the LS process, providing multifaceted support across all phases. By assisting with research analysis, task planning, observation tool development, and data analysis, Gen-AI significantly streamlines these traditionally time-intensive aspects of the LS cycle, allowing teachers to focus more on pedagogical decision-making and reflection. Methodology, Methods, Research Instruments or Sources Used A qualitative case study tracked six EFL teachers (including one senior mentor) at a Chinese public university over six months (July-December 2024) through five LS cycles. Each cycle focused on a unit from the comprehensive course in this university: 1)LS Structure: Plan (2-3 hrs collaborative design with Kimi) Teach/Observe (1 hr lesson implementation + observation) Post-Discussion/Reflection (1 hr each). 2)Technology: AI tool ‘Kimi’ was selected for its long-text processing, multi-modal resource generation, and compliance with local regulations. 3)Data collection: We systematically documented each of the five LS cycles through comprehensive video recordings, totaling approximately eight hours per cycle. The documentation captured three key components of each cycle: collaborative planning meetings (four hours), research lesson implementation (two hours), and post-lesson and reflection discussions (two hours). We systematically documented each of the five LS cycles through comprehensive video recordings, totaling approximately eight hours per cycle. The documentation captured three key components of each cycle: collaborative planning meetings (four hours), research lesson implementation (two hours), and post-lesson and reflection discussions (two hours). Each phase of the LS cycle also generated distinct data. During the Plan phase, we gathered teacher-Gen-AI interaction documentation (i.e., chat logs, screenshots, and prompt refinement records), collaborative planning sessions field notes, initial and revised lesson plans, planning meeting minutes; for the Teach and Observe phase, we collected detailed observation notes from team members, student work samples, and implementation documentations. In the Post-lesson and Reflection phase, we collected teachers’ reflection notes and conference minutes, which documented the decisions made regarding lesson modifications. Although student responses were observed and discussed during teacher reflections, direct student perspectives were not collected as the primary aim was to understand the teacher collaborative process in Gen-AI integration. Teachers' observations of student engagement and learning were documented through their reflective discussions and lesson study notes. Additionally, semi-structured interviews were conducted with each of the six participating teachers.These interviews aimed to explore teachers' experiences with Gen-AI integration in curriculum design, focusing on the perceived benefits and challenges, the interplay between Gen-AI and the LS process, and the impact on their professional development. Furthermore, we collected teachers' reflection journals and student feedback forms to enrich our dataset. 4)Data analysis: To address our research questions, we employed thematic analysis to explore how teachers discover, utilize, and reflect on Gen-AI integration in curriculum design. This analysis combined inductive and deductive coding approaches and followed Braun and Clarke (2006) six-step framework. Conclusions, Expected Outcomes or Findings Our study investigated how higher education EFL teachers discover, utilize, and critically reflect on Gen-AI capabilities in EFL curriculum design through LS. Through a six-month study from July to December 2024, we documented teachers' developmental journey in integrating Gen-AI into their curriculum design practices. Our findings revealed three key dimensions of this integration process: teachers' systematic discovery of AI's curriculum design capabilities, their strategic development of application patterns, and the crucial role of LS's structured support mechanisms. Our study makes several significant theoretical and practical contributions. Theoretically, it extends existing frameworks of Gen-AI integration in education, particularly Kim's (2024) TAC development theory, by demonstrating how structured professional development can facilitate teachers' progression from passive recipients to strategic users of AI. Methodologically, it illustrates how LS can serve as an effective framework for supporting higher education EFL teachers' Gen-AI integration efforts while maintaining their professional autonomy. As documented by our findings, we have developed practical guidelines that can support institutions and teachers in implementing Gen-AI integration through structured professional development frameworks. These guidelines emphasize the importance of systematic discovery, strategic development, and collaborative enhancement while maintaining teacher autonomy. They provide concrete strategies for teachers to effectively integrate Gen-AI in curriculum design while preserving their professional judgment and collaborative and cooperative teamwork. References References: Braun, V., & Clarke, V. (2006). Using thematic analysis in psycholog. Qualitative Research in Psychology, 3(2), 77-101. Kim, J. (2024). Leading teachers’ perspective on teacher-AI collaboration in education. Education and Information Technologies, 29(7), 8693-8724. Huang, R., Fang, Y., & Chen, X. (2017). Chinese lesson study: A deliberate practice, a research methodology, and an improvement science. International Journal for Lesson and Learning Studies, 6(4), 270-282. Lewis, C. C., Perry, R. R., Friedkin, S., & Roth, J. R. (2012). Improving Teaching Does Improve Teachers: Evidence from Lesson Study. Journal of Teacher Education, 63(5), 368-375. 16. ICT in Education and Training
Poster Gen-AI in Education: What do Teachers Do, Know and Need? Windesheim University, Netherlands, The Presenting Author:Generative Artificial Intelligence (GenAI) is rapidly transforming societies and economies. It promises considerable benefits for global challenges, but also presents substantial risks especially about mis- and dis-information (OECD, 2024). In an educational context, the discussion proceeds along similar lines, usually based on the potential impact of GenAI on teaching and learning (see, for instance, Unesco or OECD reports such as the one from Miao & Holmes, 2023 or OECD, n.d.). The perceived impact can, on the one hand, lead to unbridledly being convinced of benefits and therefore ‘completely embracing’ the use of GenAI. On the other hand, the perceived impact can also result in being convinced of doomsday scenarios and ‘completely banning’ the use of GenAI in education (Walter, 2024). The discourse about GenAI in education generally takes the form of speculations and visions about the future. For instance, Microsoft (in Helmond, 2024) asks to “Imagine a future where every student has a personalized learning path, where faculty can focus on teaching instead of administrative tasks, and where academic research accelerates breakthrough discoveries. This is not a distant vision – generative AI is making it possible today.” According to Wiliamson (2024), these visions are accompanied by terms like personalized learning, customization, 24/7 availability, and improved learning. Those opposed to GenAI stress the potential risks in terms like bias, incorrect information, de-contextualization, technology dependency, integrity, and loss of autonomy. In practice, the discussion seems to produce a spectrum of stances: from ignorance towards rethinking education and everything in between (Lodge et al., 2023). Of course, speculations like this and descriptions of GenAI’s potential for education are more and more underpinned by research. This research is often focused on the use of GenAI for teaching tasks (Bond et al., 2024). Bond et al., but also Crompton and Burke (2024), mention studies with possible benefits with regard to adaptive instruction, personalized feedback and conducting assessments. Looking at what teachers actually do at the micro level, then GenAI is being used for very specific, smaller teaching tasks. For instance, teachers create summaries, exam questions and prepare or improve materials (Open Universiteit, 2025) – all of which are tasks that take a lot of time. According to Corbin et al. (2025), teaching tasks that require pedagogical reasoning skills (which can also be time-consuming) constitute the added value of a teacher, and GenAI does not possess this ability. In the meanwhile, Bond et al. see many educational institutions struggling with the question of what the policy for GenAI use should be, how teachers themselves can be trained in responsible use of GenAI, and how they can prepare their students in general and specifically for their future profession. Much is still uncovered of how teachers precisely think about using GenAI in teaching and learning, what they actually do in their teaching practices and how they think their GenAI-use relates to learning (e.g., Cabellos et al., 2024). More insights are needed when education is tasked with preparing students for using GenAI in a responsible and critical way (AI Act – European Parliament, 2024; Npuls – Renkema et al., 2025). This study addresses this call by focusing on what teachers think and do when it comes to using GenAI in learning contexts. Methodology, Methods, Research Instruments or Sources Used A mixed-methods design was set up for conducting an exploratory study on the question What perceptions, actions and needs express teachers in Higher Education about the use of GenAI? A quantitative questionnaire was developed, containing questions about tools, tasks, and reasons for using these tools (or not). Predefined statements were combined with an open question for additions and explanations. Table 1 contains example statements. The questionnaire was spread between March-May 2024 among the teachers from an educational and an economics program of a University of Applied Sciences in the Netherlands. Teachers answered anonymously (n=59). Table 1. Example questions Focus on Example (Answer options) Teaching tasks Please tick the box that indicates which of the following categories you use GenAI for: - creative and visual applications, e.g., generation of images, visuals or video etc. (5-point Likert scale: no use – several times/year – several times/month – once/ week – several times/week Reason for use Why are you using GenAI applications? - time savings, e.g., the use of GenAI helps me complete my tasks faster (5-point Likert scale: totally disagree > totally agree) Reason for non-use Are the examples below a reason for you not to use GenAI? - data security, e.g., because I don't know what GenAI does with my data (5-point Likert scale: totally disagree > totally agree) Using an interview guideline, the quantitative date were enriched by semi-structured focus group interviews, with which we got a more specific portrait of what teachers think of GenAI and how they use it specifically in teaching tasks. Teachers (n=10) volunteered for this one-hour interview, which took place in September 2025. The interviews were recorded and transcribed. Descriptive statistics, correlations and reliability were calculated for the quantitative data. Two scales emerged, one for tasks and reasons to use GAI for these purposes and one for reasons not to use GAI.. Reliability ranged from good to acceptable (Cronbach’s α = .87use, respectively .71non-use). The qualitative data were analyzed deductively, based on a predefined codebook. The codes were derived from the themes of the questionnaire, which were further fine-tuned by inductively labeling the patterns that became visible. Two members of the research team carried out the interviews. The first author coded the interviews. A second researcher encoded two randomly selected transcripts; the two encodings were compared and found to show hardly any differences. Conclusions, Expected Outcomes or Findings A majority (87%) of the teachers used GenAI for professional tasks, mostly to search for ideas and gather information. 31 % of the teachers used GenAI also for specific teachers’ tasks: creating lesson plans, looking for teaching methods, searching for actual cases, or creating example questions . Teachers in educational programs used GenAI also for pedagogical support , teachers in economical programs used it instead to produce content. Ease of use (64%) and time savings (61%) were reasons for GenAI-use. Teachers of the economic programs (44%) used GenAI for its contribution to the quality of own work. For teachers in the educational programs (73%) this was precisely the reason not to use it, they write better texts themselves. Preparation for future work was a common reason for economic teachers to use GenAI (94%), but not for teachers in educational programs (27%). In the focus group, these differences were explained by referring to key characteristics of future professions. Creating products, entrepreneurship and efficiency are important in preparing students for economic professions, GenAI’s meaning for learning processes is important in teaching professions. All users conducted GenAI-activities with students, such as discussing (im)possibilities of GenAI (86%), talking about privacy and ethical issues (71%), analyzing GenAI’s output (48%), or training prompting skills (32%). The economic teachers conducted significantly more activities with regard to the last two activities. Some teachers (14%) did not use GenAI for professional tasks at all; they did not feel the need yet, or were unfamiliar with possibilities. Ethical considerations (such as academic integrity, bias, and reliability) did not come forward spontaneously, although the teachers in the focus group would like to discuss the impact of GenAI on themselves, on others and on the world. Finally, teachers expressed an urgent need for more frameworks and guidelines, as well as for becoming more AI literate. References Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., ... Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: a call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, (2024) 21:4. https://doi.org/10.1186/s41239-023-00436-z Cabellos, B., de Aldama, C., & Pozo, J.I. (2024). University teachers' beliefs about the use of generative artificial intelligence for teaching and learning. Frontiers in Psychology, 15:1468900. https://doi.org/10.3389/fpsyg.2024.1468900 Corbin, T., Tai, J., & Gene Flenady (2025): Understanding the place and value of GenAI feedback: a recognition-based framework. Assessment & Evaluation in Higher Education, 50(5), 718–731. https://doi.org/10.1080/02602938.2025.2459641 Crompton, H., & Burke, D. (2024). The educational affordances and challenges of ChatGPT: State of the field. Techtrends, 68:380-392. https://doi.org/10.1007/s11528-024-0093-0 European Parliament. (2024). EU AI Act: first regulation on artificial intelligence. Accessed 15 December 2025, from EU AI Act: first regulation on artificial intelligence | Topics | European Parliament Helmond, A. (2024, 7 november). Big AI (in education). Cloud infrastructure dependence and the industrialisation of artificial intelligence. [presentation]. GDS & Kennisnet Symposium. Utrecht. Hirabayashi, S., Jain, R., Jurković, N., & Wu, G. (2024). Harvard undergraduate survey on generative AI. arXiv preprint arXiv:2406.00833. Lodge, J.M., Howard, S., & Broadbent, J. (2023, May 1). Assessment redesign for generative AI: A taxonomy of options and their viability. [Post]. https://www.linkedin.com/pulse/assessment-redesign-generative-ai-taxonomy-options-viability-lodge/ Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. Unesco. OECD. (2024). OECD Digital Economy Outlook 2024 (Volume 1): Embracing the Technology Frontier. OECD Publishing. https://doi.org/10.1787/a1689dc5-en OECD. (n.d.). Artificial intelligence and education and skills. Retrieved November 14th, 2024, from https://www.oecd.org/en/topics/sub-issues/artificial-intelligence-and-education-and-skills.html Open Universiteit. (2025). Hoe gebruiken onze studenten en docenten AI? [How do our students and teachers use AI?]. Accessed 15 January 2026, from https://www.ou.nl/en/-/hoe-gebruiken-onze-studenten-en-docenten-ai Renkema, M., van den Boom-Muilenburg, E., Friso-van den Bos, I., Theelen, H., Wopereis, I., & Schildkamp, K., (2025). AI-GO! Een Raamwerk voor AI-Geletterdheid in het Onderwijs (AI-GO Framework). Npuls/AI- en Data geletterdheid. Walter, Y. (2024). Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21,15 (2024). https://doi.org/10.1186/s41239-024-00448-3 Williamson, B. (2024, November 7). AI in schools. Keywords of a public problem. [Presentation]. GDS Kennisnet symposium, Netherlands, Utrecht. 16. ICT in Education and Training
Poster Enhancing 10th graders English Language Learning through Video Dubbing: A Task-Based, Creative Pedagogical Approach Nazarbayev Intellectual school in Aktau, Kazakhstan Presenting Author:This study investigated how video dubbing activities can enhance the foreign language proficiency of 10th-grade students through creative and task-based methods. The research emphasized how dubbing muted videos motivates learners and improves their speaking fluency, pronunciation, and creativity within an assessment context. The study was conducted during Term 3 among Grade 10 students at the Nazarbayev Intellectual School of Chemistry and Biology in Aktau. Traditional assessment methods for speaking often rely on subjective evaluation and limited feedback opportunities, which restrict students’ ability to self-reflect and improve. Incorporating video dubbing as a task-based activity allowed the researchers to evaluate linguistic competence in a more authentic, engaging, and measurable way. This approach enabled students to become active participants in both learning and assessment while improving linguistic, analytical, and creative skills through multimedia interaction. Research Questions
Grounded in Task-Based Language Teaching (TBLT) and Sociocultural Theory (Vygotsky, 1978), the study examined how collaborative language production supports communicative competence through authentic interaction. Methodology, Methods, Research Instruments or Sources Used This study adopts a mixed-method approach, combining qualitative and quantitative techniques to investigate the impact of video dubbing on foreign language learning. The research focuses on how dubbing activities enhance pronunciation, vocabulary, creativity, and collaborative skills among learners. The participants consisted of 20–30 10 grade students at pre-intermediate levels, working in small groups of three or four. This structure encourages communication, teamwork, and equal involvement in all project phases. The procedure followed five main stages. 1. Preparation: The teacher selected a short, muted video clip (4–5 minutes) that aligns with the lesson theme. Relevant vocabulary and grammar will be introduced before the activity. 2. Task Introduction: Students observed visual and paralinguistic cues from the video—facial expressions, gestures, and body movement—to infer meaning and context. 3. Script Development: Each group created an original storyline and writes dialogues using the target language structures. The teacher provided feedback on accuracy and contextual relevance. 4. Practice and Recording: Students rehearsed pronunciation, stress, and intonation, recording their voices multiple times for self-assessment before synchronizing their dialogue with the video. 5. Presentation and Evaluation: The final dubbed videos were shared in the classroom for peer and teacher evaluation, focusing on language use, creativity, synchronization, and collaboration. To gather data, several research instruments were used. An observation checklist tracked student engagement, motivation, and participation throughout the process. Pre- and post-tests measured improvements in pronunciation, vocabulary, and fluency. Reflection journals allowed students to describe personal experiences and learning outcomes. Interviews or focus group discussions with students and the teacher offered qualitative insights into challenges and perceived benefits. The dubbed video recordings served as primary evidence for analyzing linguistic accuracy, pronunciation, and contextual understanding. Finally, peer evaluation forms encouraged students to assess one another’s performance constructively. For data analysis, qualitative data from observations, journals, and interviews undergoed thematic analysis to identify emerging patterns and insights. Quantitative data from tests were analyzed statistically to measure linguistic improvement. All participants provided informed consent, and data remained confidential. This methodology aimed to demonstrate how video dubbing, as a creative and task-based activity, effectively enhances learners’ communicative competence while increasing motivation and cooperative learning. Conclusions, Expected Outcomes or Findings After implementing the dubbing activities, students demonstrated substantial progress in pronunciation, fluency, and contextual language use compared to traditional speaking tasks. The repetitive rehearsal and collaborative editing processes fostered phonetic accuracy and stronger rhythm and intonation patterns in student speech. Learners also exhibited increased motivation and engagement, reporting higher confidence when performing in groups. Video dubbing provided opportunities for peer learning and allowed students to self-assess by listening to their recordings multiple times. These task-based activities created an environment in which language learning became both interactive and reflective. From an assessment perspective, the dubbing method allowed teachers to evaluate speaking proficiency with greater objectivity, consistency, and creativity. Students’ recorded performances served as tangible evidence for tracking linguistic progress over time. Overall, the findings confirmed that integrating dubbing into language classes enhances communicative competence while encouraging self-directed learning. This approach proved practical, resource-efficient, and highly motivating for secondary-level foreign language learners. References Biegel, S. (1998). Authentic video production in the foreign language classroom: A task-based approach to language learning. Foreign Language Annals, 31(2), 205–212. Brooke, M. (2003). Integrating video projects in second language classrooms: A communicative perspective. Language Learning Journal, 28(1), 54–62. Dubreil, S. (2003). Video production and language acquisition: A student-centered approach. CALICO Journal, 21(1), 91–110. Melillo, M. (2000). Video creation as a pedagogical tool in ESL classrooms. TESOL Journal, 9(4), 23–29. Scruggs, M., & Reed, K. (2001). Motivation through media: The effect of student-generated video projects on language learning. Journal of Educational Media, 26(3), 135–146. Vygotsky, L. S. (1978). Mind in Society: The Development of Higher Psychological Processes. Harvard University Press. Willis, J. (1996). A Framework for Task-Based Learning. Longman. Richards, J. C., & Rodgers, T. S. (2001). Approaches and Methods in Language Teaching (2nd ed.). Cambridge University Press. Dörnyei, Z. (2001). Motivational Strategies in the Language Classroom. Cambridge University Press. Swain, M., & Lapkin, S. (2001). Focus on form through collaborative dialogue: Exploring task effects. In M. Bygate, P. Skehan, & M. Swain (Eds.), Researching Pedagogic Tasks: Second Language Learning, Teaching and Testing (pp. 99–118). Pearson Education. 16. ICT in Education and Training
Poster Would Schoolchildren Benefit from AI-generated Feedback on Complex Online-inquiry Tasks? University of Turku, Finland Presenting Author:The internet has become an integral part of our world, both in everyday life and at school. Online enquiries have become part of the school learning process.Also AI has undergone significant development in recent years, particularly since large language models (LLMs) became available, which means that this technology is also on its way to being integrated into school education. This integration is accompanied by the following expectations. AI can improve teaching and learning processes by offering personalisation and providing intelligent support for teachers and learners (Miao, Holmes, Huang, & Zhang, 2021). There are many concerns about using AI in a school context, with ethics being particularly emphasised in this context (e.g. Adams et al., 2023). When discussing the possibilities of using AI for learning, feedback options are often mentioned (e.g. Celik et al., 2022). Feedback is a very powerful way to improve learning, also for younger students (e.g. Hattie & Timperley, 2007). Hattie and Timperley (2007) describe four levels of feedback, including those relating to the task and the process. The task level covers correctness and simple errors, while the process level covers strategies and deeper reasoning. This study focuses on how pupils use AI-generated feedback in complex tasks. In this project 5th and 6th grade students write a synthesis based on multiple sources. They use a closed www environment, in which they complete an online inquiry task independently. The tasks have several steps, finding, evaluating and selecting the for the task relevant sources, finding in these relevant sources the main ideas and structuring these ideas, and writing a synthesis based on these structured ideas to answer the inquiry. For each of these three phases, there is feedback specific to the respective phase as well as feedback relating to the entire task. To avoid cognitive overload the pupil can choose feedback options. The student can request feedback on either a specific phase or their overall performance. On the other hand, they can select further questions created by the AI and offered by the environment in the feedback process. This gives students the opportunity to receive more detailed feedback, which they need. Methodology, Methods, Research Instruments or Sources Used In our early-stage study nine-graders (N=16) from one class will take part in April 2026. These pupils had previous experiences of the closed learning environment from a former study. THe closed learning environment. A learning environment for working on online inquiry tasks is created. This includes the sources, their evaluation, the selection of the main ideas in the form of text passages from these sources, their structuring, and the writing of a synthesis as an answer to the inquiry. The schoolchildren have a positive general attitude towards the learning environment and the feedback created by AI, although there were some reservations regarding AI and the feedback, for example, the feedback does not meet my needs or the feedback is incomprehensible. Furthermore, most of the pupils used feedback. In addition to the three single-phase feedbacks, they also have the cognitively very demanding entire-task feedback. For them the feedback for the entire task is a new feedback possibility. The log data is available as a data basis. This data is analysed to determine who requested which feedback and when, and which ‘further questions’ within the feedback process they used. Furthermore, performance data such as successfully selected sources and main ideas as well as the quality of the synthesis are analysed. After completing the task, the pupils will answer a questionnaire concerning their attitude towards AI, Ai generated feedback and the feedback. The research questions are: - What kind of feedback did the schoolchildren request? - What is the children's attitude towards AI and AI generated feedback after this AI learning experience? - Is there a correlation between requesting entire-task feedback and performance quality? Conclusions, Expected Outcomes or Findings As data collection will not take place until April 2026, we will put forward a number of hypotheses in this proposal. A group of children will not use the feedback at all or will use it very little. Another group of children will mostly use the feedback as task feedback, i.e. in relation to correctness. The third group of children will also use the feedback at the process level, i.e. in relation to the strategy. The last group in particular will use the further questions offered in the feedback process. The experience gained from the two studies will give schoolchildren a positive attitude towards AI and AI-generated feedback in general. The hypothesis for the third question is that there is a positive correlation. The cognitively difficult entire task feedback is more likely to be requested by schoolchildren who want to improve their strategy and who already have a good understanding of the task and its individual phases, enabling them to perform quite well. References Adams, C., Pente, P., Lemermeyer, G., & Rockwell, G. (2023). Ethical principles for artificial intelligence in K-12 education. Computers and Education. Artificial Intelligence, 4, Article 100131. https://doi.org/10.1016/j.caeai.2023.100131 Celik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The Promises and Challenges of Artificial Intelligence for Teachers: a Systematic Review of Research. TechTrends, 66(4), 616–630. https://doi.org/10.1007/s11528-022-00715-y Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. Miao, F., Holmes, W., Huang, R., & Zhang, H. (2021). AI and education: Guidance for policy makers. UNESCO. https://doi.org/10.54675/PCSP7350 | ||