Conceptualizing AI literacy: An exploratory review

Analysis

Why This Paper Matters

As artificial intelligence permeates industries from business to education, public understanding of AI technologies remains limited. This paper addresses a critical gap by conceptualizing "AI literacy," a newly emerging competency essential for the next generation. With over 2,100 citations, it has become a foundational reference for educators and researchers seeking to define and teach AI literacy.

The exploratory review responds to the urgent need for a sound theoretical foundation to define, teach, and evaluate AI literacy. By synthesizing literature from 30 peer-reviewed articles, the authors propose a structured framework that adapts classic literacies to the AI context. This work is particularly significant because it moves beyond technical skills to include ethical considerations, reflecting the growing recognition that AI literacy must encompass responsible use and critical evaluation.

Given the rapid integration of AI into daily life and work, this paper's timely contribution helps shape educational agendas and policy discussions. It provides a common language for stakeholders to discuss AI literacy, facilitating curriculum development and assessment design across educational levels.

Technical Contributions

• Four-aspect framework: Proposes that AI literacy comprises (1) know and understand, (2) use and apply, (3) evaluate and create, and (4) ethical issues. This framework adapts classic literacy dimensions to AI.
• Consolidated definition: Synthesizes a definition of AI literacy from existing literature, offering clarity for future research and practice.
• Teaching and ethical considerations: Highlights pedagogical approaches and ethical concerns, emphasizing that AI literacy includes understanding societal impacts.
• Foundation for assessment: Lays groundwork for developing competency frameworks and assessment criteria, addressing a gap in AI education research.

Results

As an exploratory review, the paper does not report quantitative metrics or comparative performance. Instead, its results are conceptual: the identification of four aspects for fostering AI literacy and a consolidated definition. The review of 30 articles reveals that AI literacy is under-explored and lacks a unified definition, which the authors address by proposing their framework. No empirical validation is provided, but the framework is grounded in literature and classic literacy theories.

Significance

The paper's significance lies in establishing a theoretical foundation for AI literacy, which is increasingly recognized as a core competency. By defining AI literacy through four aspects, it provides a roadmap for educators to design curricula that cover technical knowledge, practical skills, critical evaluation, and ethical awareness. This holistic approach contrasts with narrower technical training and aligns with broader calls for responsible AI education.

The framework has implications for competency development and assessment, as it outlines dimensions that can be operationalized into learning objectives and evaluation rubrics. Future research can build on this work to develop validated instruments and test interventions. Moreover, the emphasis on ethical issues underscores the importance of preparing learners to navigate the societal impacts of AI, including bias, privacy, and job displacement.

With over 2,100 citations, the paper has influenced discourse in AI education and literacy. It serves as a reference point for subsequent studies and curriculum initiatives, demonstrating the demand for conceptual clarity in this emerging field. As AI continues to evolve, the framework may need refinement, but its foundational role is undeniable.