The integration of computational thinking (CT) into mathematics education has attracted increasing attention; however, there is limited methodological clarity about how CT can be systematically embedded in primary mathematics instruction. In particular, empirical research linking CT to multi-step mathematical problem-solving remains scarce. This paper presents the design and implementation of a task-based intervention model developed within the DigiMaths4All project. The model incorporates CT into primary mathematics through a structured framework for task selection and design, combined with implementation in a technology-enhanced learning environment (ViLLE). Mathematical tasks focus on arithmetic fluency and multi-step problem solving, while CT tasks are tailored to operationalize key processes such as decomposition, abstraction, algorithmic thinking, and debugging. The intervention was carried out through a class-based randomized controlled trial in primary education, comparing traditional instruction with technology-supported approaches, including an integrated mathematics CT model. The study uses a mixed-methods approach, incorporating assessments and learning analytics data to examine implementation processes. The main contribution of this paper is methodological. It offers a replicable framework for (1) designing interventions that incorporate CT into mathematics, (2) selecting and constructing aligned task sets, and (3) implementing these tasks within an analytics-driven digital environment. The findings enhance understanding of how CT can be operationalized to support mathematical problem solving in primary education.
This study addresses an implementation problem for informatics education: whether teachers’ reported participation in AI-related professional learning, interpreted as realised access to one professional learning condition for teacher AI literacy, is associated with declared need or instead follows existing patterns of digital, professional and organisational advantage. The study does not measure teacher AI literacy, AI competence, computing teaching practice, computational-thinking instruction or classroom implementation directly. Rather, it analyses reported participation in AI-related professional learning as a realised opportunity condition for developing teacher AI literacy at scale. Using TALIS 2024 data from 108,136 lower-secondary teachers nested within 10,840 schools across 55 education systems, three-level multilevel linear probability models and random slopes at the education-system level were estimated. Results showed substantial cross-system inequality in reported participation. Variance decomposition located 8.5% of total variation at the education-system level and 9.7% at the school level. Declared need was only partially associated with reported participation: teachers reporting low or moderate need were more likely to have participated in AI-related professional learning than those reporting no need, whereas teachers with the highest need showed no significant advantage. Digital self-efficacy and professional collaboration were consistently associated with higher participation. At the school level, digital resource shortages and school digital leadership support were significant predictors. Random-slope estimates showed that the association between high declared need and participation varied significantly across education systems. The findings suggest that equitable teacher AI literacy requires deliberate opportunity structures, not only competence frameworks or voluntary participation in professional learning.
Debugging is integral to programming. It comes into play as soon as novices make their first mistakes in creating programming artifacts. It is also consistently reported to be a skill that is difficult to learn as well as to teach effectively. Research in Informatics Education has often focused on the process of debugging, by breaking it down in steps connected by temporal and causal dependencies. In this work, we focus instead on debugging as a skill, from the standpoint of Cognitive Load Theory, and break it down into a tree-shaped model of subskills that enable one another. Debugging may thus be seen as a meta-skill that requires the coordination of multiple others. From the standpoint of Cognitive Load Theory, such a skill is cognitively expensive, which may explain the learning-related difficulties tied to debugging. Using the framework of the four-component instructional design, we hypothesize a categorization of each debugging subskill as either recurrent or nonrecurrent, dividing those that are applied consistently to different contexts from those that require problem solving. All subskills may be practised and potentially assessed with targeted exercises, whose design depends on their recurrent/nonrecurrent nature. We provide extensive examples of such exercises. Our decomposition of debugging into subskills is a novel way to address debugging in educational contexts and complements the work done on debugging processes. Although it is currently a theoretically grounded conjecture, the model provides concrete guidance for instructors on analyzing existing materials and planning cognitive-load-informed learning trajectories.
Source code comments are usually treated in software engineering as documentation artifacts that support readability, maintainability, and long-term comprehension. In programming education, however, comments may also function as pedagogical scaffolds by helping learners externalize reasoning, clarify intent, and reflect on code. This article presents a PRISMA-informed qualitative systematic review with layered evidence on the pedagogical role of source code comments in programming education. Searches in Scopus and ERIC produced 50 unique records; 36 were assessed in detail and resolved into 18 primary synthesis studies, 7 supporting/contextual studies, and 11 advanced-stage exclusions. Because the evidence base is heterogeneous, the review uses qualitative layered synthesis rather than meta-analysis. The findings suggest that comments are best understood pedagogically as explanation-centered learning supports, especially for code comprehension, self-explanation, debugging and reflective reasoning, and formative assessment of student thinking. Direct comment-focused evidence remains limited and concentrated mainly in highereducation and novice programming contexts; adjacent explanation-centered studies clarify plausible mechanisms but do not by themselves establish comment-specific effects. The review concludes that comments can become pedagogically meaningful when deliberately integrated as scaffolds for explanation, comprehension, and reflection.
We investigate the pedagogical impact of Graphical Loop Invariant Based Programming (GLIBP) in an introductory programming course. This approach encourages students to visually model the objects and variables handled in the loop, before implementing it. To evaluate the efficiency of this GLI model, a four-condition A/B/C/D test was conducted across two problems, with students receiving varying levels of scaffolding (from no support to a fully constructed GLI). Analysis of students’ code showed that a well-designed GLI reduced errors related to the loop guard and the update of variables. However, many students struggled to understand or represent a GLI. The fill-in-the-blank GLI version, in particular, often added cognitive load rather than reducing it. Three recommendations emerged: train students to interpret a provided GLI when writing code; second, teach students to sketch their own model by recognizing similarities to previously solved problems; finally, guide students with questions to ensure all necessary variables and relationships are properly identified.
Education is about supporting humans in their growth, with a special focus on exploring their intellectual potential. Learning to act following a given (even complex) pattern is losing its educational value very fast, because all well described activities can be automated. Education therefore should focus on developing those cognitive process dimensions of pupils where technology cannot compete with humans (Dagienė et al. (2020), Hromkovič and Lacher (2017), Hromkovič et al. (2020)). The contribution of this paper is conceptual. In the paper we show that starting with the algorithmic view on the historical development of number representations and calculations offers a natural, more understandable way for teaching mathematics in primary schools. We show that going consequently from concrete to abstract empowers pupils to be able to design own representations of numbers, rediscover the execution of arithmetic operations on their own, and even develop elementary calculations in own designed number systems. We show here how a successful process of rediscovery of arithmetic algorithms can be designed by using classical algorithm design methods as “induction” and “divide and conquer”. We show how that algorithmic thinking can essentially contribute to improving education in mathematics.
In this article, we examine a case study of the Bachelor’s degree programme “Computer Science” at the University of Latvia. We explore several factors that enabled it to (a) obtain the European Informatics Quality Label three times, (b) be ranked first in the national employer survey as the most recommended educational Programme for nine years, and (c) adopt a student-centred approach. Using a case study methodology, we highlight several innovations that together make the Programme highly regarded both academically and in the labour market. At the end of the paper, we divide the key outcomes of the study into two sets of innovations. National-level solutions, such as learning outcome comparison and the development of industry terminology with student participation, are important primarily in the local context. Whereas (a) the framework for gaining both industry and academic experience through the Practice Course and Qualification thesis, and (b) curriculum expansion with Special Seminars and the creation of opportunities for students to acquire additional knowledge through Excellence Studies and Remedial Courses, can be transferred internationally.
Computational Thinking (CT) is widely recognised as a transversal competence essential for learning, problem solving, and knowledge transfer across disciplines. However, its effective integration into school education remains strongly dependent on the availability of assessment instruments that are pedagogically meaningful, psychometrically sound, and applicable across diverse educational contexts. This paper presents COMATH, a cross-national assessment instrument designed to evaluate CT in students aged 9-14. The instrument adopts a phase-based development and validation framework that analytics, and teacher and student feedback. The assessment was iteratively developed and piloted between 2023 and 2025 in six European countries, with data collected from 6,480 students and 155 teachers. The findings demonstrate that a phased assessment approach enables systematic calibration of task difficulty, robust evaluation of item functioning, and meaningful interpretation of student performance across age groups and national contexts. The results further highlight how well-designed CT assessment can support instructional decision-making rather than serve solely as a summative measure. The study argues for conceptualising CT assessment as a dynamic and iterative process that links measurement, psychometric validation, and pedagogical use in school education.
Computational thinking (CT) is widely recognized as a key 21st-century competence, yet its integration across disciplines remains unclear for many educators. This study explores how prospective teachers identify and express CT through scripts representing computational processes in school subjects of their choice. The challenge of integrating CT in teacher preparation programs in non-STEM-related fields is also addressed. Using a mixed-methods approach, we analyze projects and accompanying reflective analyses from 375 prospective teachers who created Scratch-based scripts aligned with computational processes in STEM and non-STEM subjects. Data analysis yielded a taxonomy of pedagogical strategies reflecting diverse instructional approaches. The study underscores the value of guided, discipline-specific CT activities in teacher preparation programs and highlights how script development of computational processes fosters both subject-matter understanding and computational thinking. The results suggest holistic lens in evaluating CT integration and offer evidence-based insights for embedding CT meaning-fully into teacher preparation programs across disciplines.
Computer science (CS) students are expected to grasp numerous CS concepts during their CS education. Researchers have previously pointed to some concepts that are challenging for many students to conquer during their education. In this study, we investigate how CS students encounter indirection, scope, references, and parameter transfer during their studies. We focus on the first three study years, as previous studies have indicated that students do not significantly improve their grasp of these concepts during that time. We surveyed the teachers of courses in three CS study programs, exploring teachers’ perspectives on students’ knowledge of the concepts and how explicitly the concepts are taught and graded. Our investigation highlights several ways in which curricula diverge from previous recommendations and how an understanding of these study programs can support learning outcomes.
This article examines pre-service teachers’ data agency, defined as the ability to act according to one’s own values and goals rather than being directed by algorithmic systems. Data agency involves understanding how computational systems, such as algorithms, data-driven profiling, and platform infrastructures, collect, process, and use data, and how these practices shape individuals and society. This article introduces a self-assessment instrument developed to measure data agency and applies it to a sample of 163 Finnish pre-service teachers. The findings show that pre-service teachers evaluated their competencies across different dimensions of data agency rather cautiously. The study highlights the importance of strengthening future teachers’ understanding of the mechanisms behind algorithmic and data-driven decision-making. Such knowledge is increasingly essential for preparing future teachers to address challenges related to datafication, including commercial data collection and algorithmic influencing in contemporary education.
This study explores the application of large language models (LLMs) to create computational thinking tasks for the Bebras International Challenge through a single-case study approach. Using exemplar-based prompting with seven authentic Bebras tasks from the 2024 cycle as contextual input, a task was developed that was subsequently accepted for inclusion in the 2025 international Bebras challenge. Comparison with the exemplar tasks confirmed that the generated content drew from multiple sources rather than replicating any single task, combining grid-based constraint satisfaction, rule-based filtering, and logical deduction into a novel navigation puzzle with engaging narrative context. International expert reviewers evaluated the task using established Bebras quality criteria, confirming successful alignment with core pedagogical requirements including age-appropriateness, clarity, and cultural neutrality. However, two significant gaps emerged in the broader authoring workflow: accessibility compliance in the researcher-authored visual components and technical inaccuracies in the LLM-generated informatics framing. Following collaborative revision by international editors that addressed these concerns while preserving the LLM’s creative contributions, the task achieved acceptance for international use. The findings reveal a collaborative pipeline comprising contextual preparation, LLM-guided generation, human technical implementation, expert community review, and collaborative revision. Results from this case suggest that LLMs can efficiently generate educationally sound creative foundations while requiring integrated human expertise to meet specialised standards and ensure inclusive design, with the task’s acceptance providing encouraging evidence for the viability of this collaborative approach.
While graph theory plays a foundational role in informatics and computational thinking (CT), its instruction in elementary education remains underexplored, particularly through embodied or arts-based methods. This study examines a low-tech, psychodramabased pedagogical intervention designed to introduce graph theory as a data structure to fifth-grade students in a Brazilian public school. Students engaged in dramatizing connections and structural changes within friendship networks, enabling experiential learning of concepts such as adjacency, traversal, and modification of graph-like structures. Data were collected through teacher interviews, classroom observations, and post-intervention assessments. Findings indicate strong student engagement, symbolic appropriation of key graph concepts, and the development of abstraction and reflection skills central to computational thinking. These results suggest that educational psychodrama offers a culturally responsive, embodied strategy for introducing core CT concepts in early education, expanding the repertoire of practices in computing education.
Debugging is a vital but challenging skill for beginner programmers to learn. It is also a difficult skill to teach. For secondary school teachers, who may lack time or programming experience, honing students’ understanding of debugging can be a daunting task. Despite this, little research has explored their perspectives of debugging. To this end, we investigated secondary teachers’ experiences of debugging in the classroom, with a focus on text-based programming. Through thematic analysis of nine semi-structured interviews, we identified a common reliance on the teacher for debugging support, embodied by many raised hands. We call this phenomenon the ‘hands-up problem’. While more experienced and confident teachers discussed strategies they use to counteract this, less confident teachers discussed the negative consequences of this problem. We recommend further research into debugging-specific pedagogical content knowledge and professional development to help less confident teachers develop approaches for supporting their students with debugging.
Transcripts play a crucial role in qualitative research in computing education, with significant implications for the credibility and reproducibility of findings. However, unreflective and inconsistent transcription standards may unintentionally introduce biases, potentially undermining the validity of research outcomes and the collective progress of the field. In this article, we introduce transcription as a theoretically guided process rather than a mere preparatory step, illustrating its role using a case example. Additionally, through a systematic review of 107 qualitative research articles in computing education, we identify widespread shortcomings in the reporting and implementation of transcription practices, revealing a need for greater intentionality and transparency. To address these challenges, we propose a three-step framework for selecting, applying, and documenting transcription standards that align with the specific context and goals of a study. Rather than advocating for overly complex, one-size-fits-all transcription strategies, we emphasize the importance of a context-appropriate approach that is clearly communicated to foster trust and reproducibility. By advancing a more robust transcription culture, this work aims to support computing education researchers in adopting standards that enhance the quality and reliability of qualitative research in the field.
The integration of Artificial Intelligence (AI) literacy into college curricula is a pressing but complex challenge, particularly in non-technical fields like business. This paper presents a case study of a pedagogical intervention designed to embed AI literacy within a required college mathematics course. The intervention employed a Problem-Based Learning (PBL) framework where 120 business students used tools like ChatGPT and matrix calculators to solve an authentic data-driven business problem. We analyzed data from the final assessment artifact – a digital magazine – to evaluate the development of specific AI literacy competencies. Initial findings from this pilot implementation indicate that the PBL approach was effective in developing students’ skills in data-driven argumentation, critical evaluation of AI-generated outputs, and the ability to connect abstract mathematical models to practical AI applications. The study demonstrates a promising replicable model for integrating AI literacy into foundational courses, but also highlights key challenges, including the need for explicit scaffolding in critical AI evaluation. This paper contributes empirical insights from an initial implementation, offering a practical framework and actionable lessons for educators designing AI literacy curricula.
Given the emergence of GenAI, students should develop GenAI literacy to promote its benefits and mitigate its drawbacks. However, many studies focus on enhancing their learning experience with GenAI, not understanding GenAI literacy. Two studies are dedicated to GenAI literacy, but they either require additional sessions or focus on overly specific tasks. We integrate GenAI into a data science course and its assessments to specifically promote GenAI literacy for non-computing students. The course design expects students to learn from their direct experience with GenAI, especially regarding GenAI usability, reliability, ethics, and privacy. Students are encouraged to use and acknowledge GenAI for some assessments and to align GenAI-generated programs to their own styles. Our evaluation involving 113 students showed that the course design might help students to understand GenAI characteristics and change their behaviour. Students are unlikely to be involved in GenAI misuse. Further, they align GenAI-generated programs and acknowledge their use. From the educational viewpoint, students could also achieve the course learning objectives.
The rapid integration of Artificial Intelligence (AI) into society demands a new generation of professionals skilled in navigating its complex ethical dimensions. This design-based research study investigates the effectiveness of ‘EthicsDebateAI,’ a bespoke online platform developed to address this need. Implemented through a multi-session workshop with third-year computer science undergraduates in Algeria, the intervention uses a Structured Controversy debate framework and authentic case studies to cultivate critical ethical reasoning. The intervention’s impact was assessed using a mixed-methods approach. A custom pre/post-assessment revealed a significant improvement in ethical reasoning (t(49) = 45.50, p < .001, d = 2.28), while high student satisfaction (M = 4.48) and perceived career preparedness (M = 4.44) were confirmed via survey data. Thematic analysis of student reflections further illuminated the development of nuanced analytical skills. Findings demonstrate that targeted, interactive interventions can effectively bridge the gap between theoretical knowledge and the practical ethical competencies required of future AI professionals.
Generative Artificial Intelligence (Gen AI) is rapidly reshaping the landscape of creative practice in the applied arts. While these tools accelerate ideation and support iterative prototyping, they also challenge traditional notions of authorship, authenticity and professional identity. This qualitative study explores how applied arts professionals integrate Gen AI into their workflows, what challenges they face, and what new skills and literacies they see as essential. Through purposive sampling, ten professionals, including designers, art directors, and filmmakers from diverse cultural contexts, were interviewed using semi-structured interviews. Thematic analysis identified two central themes: AI-driven workflow transformations and shifts in professional identity. Participants described Gen AI as a co-creator that enhances early conceptual work but also raised concerns around creative homogenization and ethical use of training data. These findings reinforce broader discussions in the literature about the dual role of AI as both a catalyst for innovation and a force that challenges creative diversity and cultural representation. The study highlights the need for a balanced approach to AI literacy in creative fields, one that integrates technical fluency with critical and ethical awareness. These insights provide a foundation for more nuanced, culturally sensitive, and ethically grounded approaches to AI adoption in the applied arts.
This editorial connects policy framework suggestions for AI literacy in elementary and secondary schools and the papers published in this special issue. The suggested framework emphasizes a human-centered vision for AI education, encompassing four domains for students – Human-Centered Mindset, AI Ethics, AI Technology and Application, and AI System Design – and five dimensions for teachers, including AI-Empowered Pedagogy and Professional Development, aligning with UNESCO AI Competency Frameworks for Students and for Teachers. Collectively, the featured papers illustrate how this policy vision can be enacted through evidence-based practice: a systematic review of AI in primary education highlights pedagogically grounded, equity-driven approaches; an empirical study on an ethical reasoning curriculum demonstrating how responsible AI thinking can be taught and assessed; a constructionist review showcases hands-on, design-based strategies that foster active learning and creativity; a qualitative study on generative AI in the applied arts reveals new professional literacies for an AI-augmented creative economy; a GenAI-integrated data-science course illustrates how usability, reliability, privacy, and ethics can be woven into disciplinary learning; a survey of preservice STEM teachers identifies affective and experiential predictors of AI self-efficacy for educators; a Structured Controversy platform shows how debate and case-based reasoning can cultivate nuanced ethical judgment in computer science students; and a problem-based mathematics course demonstrates how we can teach students to discern which types of AI tools can better support different problem-solving tasks in real-world business contexts. Together, these studies illuminate a coherent pathway from policy to practice – one that advances human-centered, ethical, and sustainable AI literacy across lifelong learning and development.
