ABSTRACT. [anonymized] is a two-year joint initiative of seven Ger- man universities that tackles the shortage of secondary-school computer science (CS) teachers by providing openly licensed, research-based learn- ing resources for CS pedagogy in CS teacher education. The consortium created 21 modules for CS teacher education, each pairing a concise theo- retical dossier with rich multimedia assets and tested tasks. Constructive alignment guided design, while a ring-review process ensured OER com- pliance, pedagogical coherence and strong reuse potential. Pilots across partner programs triggered iterative improvements that were distilled into implementation guidelines. Released under Creative Commons li- cense on the [anonymized] portal, the modules span core disciplinary topics and cross-cutting issues, such as programming pedagogy, physical computing, CS teaching methods, and inclusion in CS education, and can be flexibly combined for initial teacher education, in-service up-skilling or certificate courses. By showing how collaborative development, rig- orous quality assurance and open licensing accelerate capacity building, FAIBLE.nrw offers a transferable blueprint for strengthening computer- science pedagogy in Germany and beyond

ABSTRACT. The integration of artificial intelligence (AI) systems into educational environments presents both opportunities and challenges for teaching and learning. While AI relevant ethical frameworks emphasize principles such as fairness, accountability, and transparency, the implementation of these principles in educational AI tools remains a complex task; particularly in the context of explainability. This paper addresses explainability from the perspective of computing education. It argues that computing educators are uniquely positioned to mediate the transparency and explainability of AI systems by developing domain-tailored explanations that align with pedagogical values and user needs. To explore this role, we first synthesize ethical concerns related to AI in education, then, we present a case study in which explainability features were implemented in an AI-based educational tool by computing educators. This study initiates an ongoing exploration into the role of computing educators in enhancing the explainability and understandability of AI systems in educational contexts, laying the groundwork for more practical steps toward fostering trustworthy and transparent AI.

ABSTRACT. This study investigates media literacy education using a social media simu-lator, focusing on the formation of learners' attitudes beyond the mere de-tection of fake news. Building on the research by the previous research team (2025), who reported on the simulator and identified overconfidence as an indicator of attitude formation, this study first examines whether the detec-tion of fake news or the judgment of truthfulness is more important. Subsequently, a simplified simulator aimed at broader applicability is proposed, and its effectiveness is evaluated. To assess learners' attitude formation, this study uses ‘specificity’ (the rate of sharing non-fake news posts, indicating active sharing) and ‘sensitivity’ (the rate of blocking fake news, indicating detection ability). Initial results from the full simulator show a weak negative correlation between attitude formation (decrease in overconfidence) and specificity, suggesting that for learners with high overconfidence, selective sharing of non-fake content may be more important than identifying fake news. Finally, a simplified simulator created using MS Forms with five articles was tested on 300 university students. Despite having fewer articles than the original simulator and delayed feedback on results to learners, it demonstrated similar effects on attitude formation among learners with overconfidence tendencies, suggesting the potential for more accessible simulator-based learning when providing class data feedback.

ABSTRACT. The European Union expects a shortfall of eight million ICT professionals by 2030. In response, many countries have reformed their curricula to strengthen computing education. This study analyzes data from 24 European countries to examine the relationship between curricular models, mandatory standalone subjects, integrative approaches, optional offerings, and the share of students entering computer science (CS) degree programs. Spearman correlation and linear regression analyses reveal a significant association between mandatory CS instruction and CS enrollment, particularly among girls. Integrative and optional formats, by contrast, show no significant or even slightly adverse effects. While causal claims cannot be made, the results offer further evidence for policymakers seeking effective strategies to increase participation in CS, especially among underrepresented groups.

ABSTRACT. Informatics labs equip students and professionals with the skills to thrive in data-driven environments. However, the diversity of tasks and interdisciplinary nature of informatics pose challenges in defining the competencies required for effective lab participation and development. This paper proposes a structured competency model tailored to the unique demands of an informatics lab setting. The model identifies and categorizes core competencies based on educational frameworks, existing teaching material, and held workshops. It emphasizes a progression from foundational knowledge to advanced problem-solving and research skills while integrating soft skills such as communication and teamwork. The model supports curriculum development, assessment design, and role definition within informatics labs. By establishing a shared language of competencies, this work contributes to improved learning outcomes, more precise expectations, and better alignment between academic training and real-world informatics challenges.

ABSTRACT. This study explores the integration of generative AI (GenAI) into teacher education by assessing AI literacy among student teachers in Latvia, the Czech Republic, Israel, and Slovakia. Using Tenberga and Daniela’s six-component framework, which includes understanding, critical evaluation, ethics, application, awareness, and communication, an online survey (n = 473) identified cross-country differences in self-reported competencies. While critical evaluation and understanding were relatively strong, significant gaps emerged in basic GenAI knowledge and communication skills. The findings highlight the need to embed AI literacy in teacher training and recommend a human-centred, evidence-based approach to prepare future educators as ethical and informed GenAI users. Implications for curriculum, policy, and collaboration are discussed.

ABSTRACT. Practicing SQL through game-based learning (GBL) has become increasingly popular, with storytelling often used as a motivating design element. In this paper, we explore the role of storytelling in SQL learning apart from games through a short, school-based intervention. Two workshop versions were developed: one embedding SQL tasks within stories, the other using the same tasks in a neutral, non-story format. A total of six workshops with, in total, 73 students aged 13–14 with no prior knowledge were conducted. Based on a post-workshop questionnaire, we analyzed learners’ perceptions regarding storytelling elements, motivation, task difficulty, and the general attitude toward reading in learning contexts. Differences between the two groups were examined using descriptive statistics, visualized as boxplots with additional median and mode.

This paper offers first insights into the effects of storytelling in short SQL workshops for young learners. Students responded positively to stories, especially when they had experienced them, and showed high interest and appropriate challenge levels. This paper contributes to a better understanding of lightweight GBL elements in database education and opens up perspectives for AI-assisted personalization of learning materials in the future.

ABSTRACT. The integration of computer simulations into teaching offers a promising opportunity to promote disciplinary and interdisciplinary skills in STEM subjects. Simulations not only represent a third pillar of the scientific knowledge process, but can also help to make complex societal challenges such as climate change or pandemics clear and comprehensible to students. This article shows how computational modeling can bridge the gap between disciplinary expertise and interdisciplinary skills in order to prepare students for the challenges of the 21st century.

ABSTRACT. Natural language processing (NLP) systems are being embedded in more areas that rely on knowledge retrieval, education, and decision-making. Now more than ever, it is important for NLP systems to map linguistics inputs to meaning-rich inputs. This paper examines four cutting-edge NLP models (i.e., BERT, SMITH, BigBird, and GPT-3) and looks at their approaches to semantic mapping at scale. We examine each models underlying architecture, how they encode contextual data, and their ability to account for long-form and ambiguous input. Using insights from a growing body of work in hybrid NLP, knowledge graphs, and attention-based, we then look at the trade-offs for each process relating to scalability, semantic accuracy, and interpreta bility. Our findings provide a conceptual and structural comparison of the models to enable researchers and practitioners to identify the most suitable models for semantically complex tasks.

ABSTRACT. Informatics became a mandatory subject in Japan’s national high school curriculum starting with students entering in 2022. The first cohort educated under this curriculum took the Common Test for University Admissions in 2025, where Informatics was newly added as a subject. Over 300,000 students—more than one quarter of all 18-year-olds—took the Informatics section, highlighting the need for assessment methods that address a wide range of learners.

To meet this need, we launched a five-year project titled Evaluation Methods for Informatics Competence with a Focus on University Entrance Examinations (EMIU). As part of this initiative, two computer-based mock examinations were conducted on the TAO platform: EMIU Informatics Mock Exam Summer 2024 and Spring 2025. These exams contained multiple-choice items designed using Item Response Theory (IRT) alongside conventional written-response questions.

Analysis showed that IRT-based items performed stably across both exams and correlated moderately with conventional items. Common items enabled cross-cohort comparison, confirming the stability and validity of the assessment framework. These results indicate that IRT-based mock examinations can serve as a strong foundation for designing university entrance assessments in informatics.

The study further contributes to international discussions on large-scale informatics assessment. Adopting an IRT approach demonstrates that computational and programming competencies can be measured consistently across cohorts, supporting national and international efforts to build comparable frameworks.

Future work includes developing detailed item-design guidelines and exploring feedback-based question formats, particularly for programming and problem-solving tasks.

ABSTRACT. The teaching of computer programming is increasingly recognized as a key component of digital competence and computational thinking. To explore effective learning pathways from block-based to text-based coding, we are currently conducting an online experimental study testing a hybrid programming interface. This interface allows real-time, bidirectional translation between blocks and text, enabling learners to code in either modality inter-changeably. Participants are randomly assigned to one of three interface groups (block, text, or hybrid) and complete a set of programming tasks, followed by self-reported measures of programming-related anxiety. The study aims to evaluate performance outcomes, interface-related anxiety differences, and the potential of hybrid tools to improve transition to textual programming. While data collection is ongoing, we present the experimental design, interface implementation, and methodological choices that underlie this research. This work is expected to contribute insights into inclusive interface design and pedagogical strategies for digital competence development.

ABSTRACT. Generative AI (GenAI) is rapidly being integrated into computer–supported collaborative learning (CSCL) as a potential partner in socially shared regulation of learning (SsRL), yet little is known about the moments when learners themselves recognise a need for regulated learning support and actively recruit a GenAI agent. To investigate this, we analysed 119 student queries to the GenAIRL (GenAI for Regulated Learning) tool from four undergraduate bio–engineering groups (N = 14) during an eight–week online collaborative STEM inquiry project. Using a mixed–methods approach on time–stamped AI logs and Zoom recordings of 16 group sessions, we identified the precipitating triggers for student queries, the discourse functions of requests, key Trigger–Request pairings, and temporal shifts in student–GenAI interaction. The results reveal that student queries were driven overwhelmingly by metacognitive needs, with 72% of triggers relating to planning, monitoring, or evaluation rather than simple knowledge gaps. The discourse functions of requests mirrored this, with students primarily soliciting regulatory support (55% of requests). Analysis of Trigger–Request pairings identified highly predictable patterns that represent key ‘windows of opportunity’ for proactive GenAI support. Longitudinally, these interactions showed a clear developmental trajectory, evolving from initial requests for orientation to sophisticated, late–stage requests for ex-pert evaluation and strategic finalisation. These findings validate a developmental model of human–GenAI partnership and offer a data–driven blueprint for designing trigger–aware GenAI that enhances students' regulatory processes in collaborative learning.

ABSTRACT. The rapid emergence of generative artificial intelligence (GenAI) has created an urgent need for educator AI literacy development. While existing frameworks provide comprehensive competency lists, they generally lack practical pedagogical guidance for implementation. This paper, based on research and analysis by 20 expert educators before, during and after EDUsummIT 2025 proposes a streamlined framework that distinguishes between "learning about AI" (foundational knowledge) and "learning with AI" (practical integration), offering a clear pedagogical approach to AI literacy development. Drawing from recent research on AI literacy frameworks and teacher agency theory, we argue that this approach provides an actionable foundation for professional development and classroom practice.

ABSTRACT. This study explores how digital inclusion and differentiated support can address foundational literacy, numeracy, and writing gaps among primary learners in low-resource settings. Conducted in Bububu A and B Primary Schools in Zanzibar, the research investigates the intersection of absenteeism, teacher preparedness, parental engagement, and technology use in shaping learner outcomes. Adopting a mixed-methods approach, the study involved literacy and numeracy assessments, student and teacher questionnaires, focus group discussions (FGDs) with teachers, and interviews with parents. Results reveal that Bububu B—characterized by more experienced teachers, moderate digital tool usage, and stronger parental support—outperforms Bububu A in all foundational competencies. A strong correlation between literacy and numeracy skills underscores the need for integrated, learner-centered interventions. Limited access to digital tools and low parental involvement in Bububu A contributed to significant learning gaps and higher absenteeism. These findings emphasize the potential of adaptive, low-cost digital platforms to personalize foundational learning and track individual progress. Recommendations include enhancing teacher capacity in inclusive, tech-supported pedagogy, deploying adaptive learning solutions tailored to learners’ diverse needs and skill levels; organizing community forums to strengthen home–school collaboration; and providing targeted incentives to improve attendance and engagement. This case study contributes to the broader discourse on equitable, adaptive, and personalized learning environments in sub-Saharan Africa, informing scalable models for foundational education reform.

ABSTRACT. In recent years, technology and artificial intelligence have developed significantly and have become a natural part of various areas of everyday life. As a result of these technological progresses, robots are now more sophisticated and able to interact with their environment through various sensors and actuators. Students' experiences with robots in real life or in films and media have a significant impact on their perception and understanding of how robots work. This includes robots communicating with their environment, processing data from their environment through sensors and then responding to it. Students come to the classroom with a wide range of prior knowledge, which are called preconceptions. Our main research goal in this study is to identify and analyze 10-14 year old students' preconceptions about robotic sensors. We use a qualitative data collection method (open-ended questionnaire) and mixed methods for data analysis, to identify different preconceptions about the functionality of sensors and how robots communicate with their environment. The paper presents the results obtained from 204 lower secondary school students (years 5 to 9, i.e. 10 to 14 years old). We identified more than 20 different models of preconceptions of the functions and operation of sensors in robotics students have.

ABSTRACT. In this research, we have developed "cureshot", an attendance management system using recent web technologies and smartphones. The design goals are: (1) to provide a cost-effective fraud prevention method using current technology, (2) to offer a playful attendance experience, and (3) to develop a system that addresses varying attendance management level requirements from teaching staff across different classes while encouraging student attendance engagement. The distinctive features of "cureshot" are: (a) attendance registration using front and rear camera capture method, (b) photography following different "themes" that may change each time, and (c) integration of automatic facial recognition using AI. We conducted a six-month operation across four actual university classes. The results showed that the fairness and convenience of the front and rear camera capture method were appreciated by students, and the system successfully addressed varying attendance management level requirements from teaching staff for different classes. A considerable number of students found enjoyment in photographing the changing themes each time, and we observed indications of improved engagement towards attendance.