AI-Enabled Construction of Personalized Learning Paths and Collaborative Cultivation of Students’ Digital Literacy in Basic Education
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Abstract
This study focuses on the collaborative cultivation of personalized learning paths and students’ digital literacy in basic education empowered by artificial intelligence. Based on constructivism, multiple intelligences theory, and lifelong education theory, it constructs a personalized learning path mechanism centered on “data collection-portrait generation-intelligent matching-dynamic optimization.” The study analyzes the mutually reinforcing relationship between personalized learning and digital literacy, as well as their integration paths in key links including resource selection, process interaction, achievement creation, and evaluation reflection. On this basis, practical strategies are proposed, including optimizing AI education platforms, designing integrated learning tasks, building dynamic student portraits, and improving evaluation feedback mechanisms. Case verification shows that AI technology can accurately construct personalized learning paths and form a synergistic effect with digital literacy cultivation. The research provides a theoretical framework and practical guidance for the digital transformation of basic education, helping students achieve personalized development while strengthening essential capabilities in digital information acquisition, communication, content creation, and ethical use.
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