Informatics in Education

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.

As artificial intelligence (AI) becomes increasingly integrated into education, preservice science, technology, engineering and mathematics (STEM) teachers must develop both AI literacy and self-efficacy to effectively incorporate AI tools into instruction. This study examined the cognitive and affective orientations of 180 Turkish preservice STEM teachers toward AI, specifically AI literacy, self-efficacy, interest, and attitudes, and identified predictors of AI self-efficacy. Using a performance-based AI literacy test and validated scales, data was analyzed through Rasch modeling and hierarchical regression analysis. While participants demonstrated moderate AI literacy and self-efficacy, the regression results revealed that AI use frequency, interest in AI, and attitudes toward AI significantly predicted AI self-efficacy, whereas demographic, academic, and cognitive factors did not. The findings emphasize the importance of fostering interest and positive attitudes, alongside hands-on experiences with AI tools, in enhancing preservice teachers’ confidence to use AI. The study underscores the need for teacher education programs to integrate both conceptual knowledge and experiential learning opportunities about AI by providing preservice teachers with practical and meaningful activities to explore AI-based tools and applications within their required coursework.

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.

Teaching programming to elementary and high school students is important for developing problem-solving and logical reasoning skills. Block-based programming frameworks, such as Scratch and Kodular, have gained popularity for introducing programming concepts in an engaging and more didactic manner. However, these frameworks lack structured tools for analysing student learning processes, which makes it difficult to track progress, identify challenges, and understand student behaviour during application development. This manuscript presents EduPROV, a provenance-based approach that extracts, structures, and analyses student actions from log files generated by block-based programming frameworks. By storing this data in a queryable format, EduPROV supports the identification of learning bottlenecks, tracking programming trajectories, and can help refine teaching strategies. EduPROV was evaluated in a study with elementary and high school students from three schools in southern Brazil, using Kodular as the block-based programming framework. The results show that provenance analysis helps reveal student behaviour, contributing to more informed and effective programming education.

Integrating the understanding of digital threats into informatics education is crucial for preparing pupils to navigate the complexities of the digital world. This study provides foundational insights for embedding digital safety competencies within informatics curricula by prioritizing key threats faced by young people. Through data collected from 708 teachers and 278 parents in Austria, the study employs correspondence analysis to rank threats, including harmful content, data security, social risks, and online addiction. The findings highlight how these prioritized threats can inform the design of competence-based curricula, fostering computational thinking, data literacy, and ethical decision-making in pupils. This research bridges the gap between informatics education and digital safety, offering practical implications for educators and curriculum developers worldwide.

The introductory programming disciplines, which include the teaching of algorithms and computational logic, have high failure and dropout rates. Developing Computational Thinking in students can contribute to learning programming fundamentals by building algorithmic and problem-solving skills. However, keeping students engaged in training such skills is still a challenge. In this sense, this work proposes an intervention for teaching Computational Thinking in the initial semesters of the Technician in Informatics and Bachelor of Computer Science courses, using gamification as a motivational strategy and the Quizizz software as a gamified platform. To evaluate the results, a mixed-method case study was used to perform a quantitative and qualitative analysis of the data and, subsequently, integrate them. The results obtained were discussed based on the Theory of Self-Determination, indicating that students demonstrated a high level of oriented autonomy and motivation to learn, regardless of the performance obtained.

This paper presents research findings on primary school students’ awareness of the ethical aspects of using artificial intelligence (AI) tools for homework. The study used a self-constructed online questionnaire administered to 301 primary school students from grades five to eight attending two primary schools in Northwestern Croatia. The results show that 57.8% of students already use AI tools to complete their homework. Students in higher grades use it more often than those in lower grades. A significant Spearman correlation exists between respondents’ daily time spent on social media and the use of AI tools (r = 0.34, p < .001). Only 36.5% of students in the sample believe it is unacceptable to copy AI-generated answers when completing their homework. The results underscore the need to integrate AI education into the primary school informatics curriculum in the Republic of Croatia, including introducing content that addresses the ethical aspects of using AI tools.

While research on Learning Analytics (LA) is plentiful, it often prioritises perspectives on LA systems over the practical ways instructors use data to analyse and refine the learning process per se. The present study addresses this inadequacy by investigating how student data is employed by educators in UK Higher Education Institutions (HEIs) and how it could be optimised. Specifically, a mixed-methods approach was employed combining survey data, mainly from one institution (N = 85) with insights gleaned from interviews with academics (N = 11). The findings reveal a real desire for better data capabilities and access, underscoring the need for HEIs to enhance data capture, better integrate systems and invest in professional development to enhance data literacy and foster a culture of data-driven decision-making. Importantly, a similar emphasis to that given to assessment and attendance needs to be given to data for the differentiation and personalisation of learning.

The integration of management education with information technology tools, such as simulation games and business analysis platforms, is playing an increasingly important role in developing students’ decision-making skills as well as earn and acquire best business practices. It is particularly useful to test the didactic process effectiveness by the use of simulation game, i.e. to investigate the specificities of the use of the same software by teams from two countries. The analysis of the literature has shown that there are many cases where the same simulation tool is used by educationalists from different countries. The computer business game “Kietas riešutas” has been widely used in Lithuanian universities and colleges. The question naturally arises which aspects of the simulation game are perceived similarly by members of international teams and which are different. A questionnaire for students was developed for this purpose, on the basis of which the study was conducted. An additional questionnaire was used to investigate the relationship between the game and the uptake and usefulness of the game in computer science subjects for decision making. The paper compares the achievements obtained in the game, discusses the results of the simulation of international teams and the rationality of the decisions taken. The study showed that the business game not only helps to understand the purposeful use of information technology, but also motivates people to use it to achieve better game results.

Tables are fundamental tools for handling data and play a crucial role in developing both computational thinking (CT) and mathematical thinking (MT). Despite this, they receive limited attention in research and design. This study investigates pupils’ attitudes toward and approaches to working with tables in informatics education, focusing on the systematic development of CT. Specifically, we examine how primary school pupils (aged 8–10) think and act when engaging with two-dimensional frequency tables integrated into Programming with Emil. Our objective is to deepen the understanding of pupils’ cognitive processes when entering data into tables and interpreting their contents. Data were collected through individual semi-structured interviews and analysed using qualitative inductive coding. Identified codes were then iteratively consolidated into broader categories and final themes. Key findings include: (a) the opportunity to solve a problem by programming a character is highly motivating for pupils, and (b) the appropriate integration of different contexts and concepts, such as tables, into an engaging programming environment has the potential to foster advanced cognitive skills beyond CT.