Artificial Intelligence (AI) literacy serves as a concrete manifestation of talent development requirements in the era of AI advancement, and the evaluation of AI literacy among primary and secondary school students constitutes a critical mechanism for enhancing their competencies in this field. To clarify the current landscape of AI literacy assessment for these students, this study conducted a systematic literature analysis across three dimensions: the conceptual framework of AI literacy, evaluation components, and assessment tools. Key findings indicate that existing assessment methods predominantly rely on conventional approaches such as self-reported scales, artifact analysis, or hybrid models combining knowledge tests with interviews. These methodologies exhibit limitations in accurately evaluating students' ability to apply AI literacy to solve real-world problems. Context acts as a vital bridge connecting theoretical knowledge with practical realities, fulfilling both connective and supportive roles in problem-solving processes. Guided by the principles of contextualized assessment design, this study proposes a novel situational evaluation framework for AI literacy tailored to primary and secondary school students. This framework aims to advance practical research on AI literacy assessment by addressing the critical need for authentic competency evaluation in educational contexts.

Yuan Luxin. Research on Contextualized Assessment of Artificial Intelligence Literacy in Primary and Secondary School Students and Implications. Frontiers in Educational Research (2025), Vol. 8, Issue 4: 164-169. https://doi.org/10.25236/FER.2025.080424.

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