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Abstract
The continuous evolution of artificial intelligence technology has fundamentally transformed the operational paradigms and application logic of contemporary information retrieval, presenting unprecedented opportunities for reforming information literacy education in high schools. Within modern intelligent learning environments, students' information retrieval practices frequently reveal significant challenges. These include limited search strategies, insufficient critical analysis skills, and a notably low efficiency in practical knowledge application. Consequently, traditional teaching approaches have become increasingly inadequate for meeting the complex demands of digital education in the twenty-first century. To address these critical gaps, this study adopts a human-computer collaborative educational perspective, meticulously integrating students' cognitive development characteristics with real-world teaching contexts. It systematically examines the underlying mechanisms by which artificial intelligence enhances information retrieval capabilities. Furthermore, the research identifies both the strengths and inherent limitations of artificial intelligence tools when deployed in basic education settings. Based on these empirical insights, the study establishes a comprehensive, intelligent retrieval training framework specifically tailored to diverse student needs, and proposes actionable teaching optimization strategies for educators. By focusing on the core challenges inherent in developing robust information retrieval competencies, this research ultimately aims to cultivate precise, intelligent, and highly critical information retrieval and application skills. In doing so, it provides a strategic roadmap for solidifying the foundational pillars of information literacy, ensuring that modern high school students are adequately prepared for future academic and professional endeavors in an increasingly automated world.
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