A critical review of teaching and learning artificial intelligence (AI) literacy: Developing an intelligence-based AI literacy framework for primary school education

Artificial intelligence (AI) literacy education mainly targets secondary and university students, often overlooking the unique needs of younger students. This gap in AI literacy primary school education presents theoretical and pedagogical challenges. Despite the pervasive influence of AI, which can exacerbate inequalities and raise ethical challenges, primary students often lack an understanding of AI principles and mechanisms. Recent developments in age-appropriate AI learning tools have extended AI literacy to primary schools, but AI literacy frameworks for this age group remain underdeveloped. This study aims to conceptualize AI literacy by analyzing existing theoretical frameworks and proposing a new inclusive AI literacy framework for young students. A scoping review is employed using four credible index databases, and 19 articles are selected, with 17 AI literacy frameworks identified across all educational levels, from early childhood to university. This study reveals that the predominant methodologies for developing AI literacy frameworks involve empirical research studies and literature reviews, adhering to national government or institutional standards. These frameworks commonly incorporate 1) Bloom's taxonomy or a similar progression framework, such as Use-Create-Modify, 2) constructionism, and 3) computer science perspectives such as theories of computation. The findings reveal that AI literacy is situated at the intersection of digital literacy, data literacy, computational thinking, and AI ethics, emphasizing the need for a transdisciplinary and interdisciplinary approach that encompasses both technological and societal impacts. However, the study argues that the current paradigms of AI literacy frameworks for young students often emphasize constructionist perspectives without fully considering the interactions between human and technological agents. This gap highlights the necessity for a new conceptual framework that acknowledges both human and non-human agents in AI literacy education for young students. The research contributes by conceptualizing AI literacy and guiding policymakers and curriculum designers to implement holistic AI literacy education for young students.