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.

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.