Informatics in Education

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.

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.

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.

Ethical thinking and reasoning is considered a core component of artificial intelligence (AI) literacy. However, there is a lack of strategies and assessments to promote and measure students’ ethical thinking in AI, particularly in K-12 education. In this paper, we discuss how RAICA, a project-based AI literacy curriculum integrates ethics into its framework and instructional resources. We employ a mixed-methods convergent design to obtain different yet complementary data on ethical thinking as an outcome mediated by diverse RAICA materials. Data analysis revealed that teachers utilized a variety of instructional strategies to foster students’ ethical thinking and that students actively engaged in ethical thinking activities, resulting in a developing understanding of stakeholders and potential benefits/harms of AI. Our work makes a key contribution to AI education by providing empirical evidence to support mechanisms for integration and assessment of ethical thinking within AI literacy curricula.

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.

This narrative literature review examines constructionist approaches to AI literacy education for school-aged children, synthesizing research from 2009–2024 to develop a pedagogical framework grounded in hands-on learning principles. Through systematic analysis of studies retrieved from Web of Science, Scopus, IEEE Xplore, and ACM Digital Library, five interconnected themes emerged: active hands-on learning, project-based inquiry, ethics integration, age-appropriate scaffolding, and teacher support with accessible tools. The findings demonstrate that constructionist methodologies – emphasizing learning through creating AI-powered artifacts – effectively foster conceptual understanding, ethical reasoning, and critical agency among young learners. The review reveals that AI literacy develops most effectively when students actively manipulate and experiment with AI systems rather than passively consuming theoretical content. Age-differentiated strategies are essential, with primary students benefiting from embodied analogies and narrative contexts, while secondary students engage with collaborative design projects addressing real-world challenges. Teacher preparation and accessible tools emerge as critical implementation factors. This framework provides educators and policymakers with evidence-based guidance for integrating meaningful AI literacy experiences into K-12 curricula through constructionist pedagogies.

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.

Artificial Intelligence (AI) is reshaping primary education across literacy, numeracy, inclusion, and classroom orchestration. This systematic review synthesizes empirical research from 2020 to 2025 to clarify how AI enhances learning and teaching in primary education. Drawing on 94 studies identified through a PRISMA-guided process, the evidence shows that AI adds the greatest value when it (a) personalizes feedback and practice, (b) scaffolds inquiry and computational thinking, and (c) augments teacher decision-making through learning analytics. Reported gains include reading fluency, problem-solving, motivation, and participation among diverse learners. Yet progress remains constrained by uneven teacher AI-TPACK and assessment literacy, infrastructural inequities, and ethical concerns regarding transparency, bias, and data governance. Across studies, the most sustainable outcomes emerged from human-in-the-loop approaches where teachers interpret and moderate AI insights. The review argues that adequate and equitable AI integration depends less on technical sophistication than on pedagogically grounded design, robust professional development, and policy frameworks ensuring accountability and equity by design. These findings inform future directions for educational policy, teacher preparation, and the ethical governance of AI-supported learning ecosystems.