Informatics in Education

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.

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.

In K-12 computing education, there is a need to identify and teach concepts that are relevant to understanding machine learning technologies. Studies of teaching approaches often evaluate whether students have learned the concepts. However, scant research has examined whether such concepts support understanding digital artefacts from everyday life and developing agency in a digital world. This paper presents a qualitative study that explores students’ perspectives on the relevance of learning concepts of data-driven technologies for navigating the digital world. The underlying approach of the study is data awareness, which aims to support students in understanding and reflecting on such technologies to develop agency in a data-driven world. This approach teaches students an explanatory model encompassing several concepts of the role of data in data-driven technologies. We developed an intervention and conducted retrospective interviews with students. Findings from the analysis of the interviews indicate that students can analyse and understand data-driven technologies from their everyday lives according to the central role of data. In addition, students’ answers revealed four areas of how learning about data-driven technologies becomes relevant to them. The paper concludes with a preliminary model suggesting how computing education can make concepts of data-driven technologies meaningful for students to understand and navigate the digital world.

With the development of technology allowing for a rapid expansion of data science and machine learning in our everyday lives, a significant gap is forming in the global job market where the demand for qualified workers in these fields cannot be properly satisfied. This worrying trend calls for an immediate action in education, where these skills must be taught to students at all levels in an efficient and up-to-date manner. This paper gives an overview of the current state of data science and machine learning education globally and both at the high school and university levels, while outlining some illustrative and positive examples. Special focus is given to vocational education and training (VET), where the teaching of these skills is at its very beginning. Also presented and analysed are survey results concerning VET students in Slovenia, Serbia, and North Macedonia, and their knowledge, interests, and prerequisites regarding data science and machine learning. These results confirm the need for development of efficient and accessible curricula and courses on these subjects in vocational schools.