Informatics in Education

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.

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.