Informatics in Education

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.

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.

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.

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.

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.

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.

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 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.

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.