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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">LNE</journal-id><journal-title-group><journal-title>Lecture Notes in Education, Arts, Management and Social Science</journal-title></journal-title-group><issn>3083-1091</issn><eissn>2705-053X</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/LNE.v4i7.15956</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Reflections on the Development of Music-Education Learners’ Aesthetic Literacy in the Context of AI Technology</title><url>https://artdesignp.com/journal/LNE/4/7/10.26689/LNE.v4i7.15956</url><author>ZhouLijuan</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>4</volume><issue>7</issue><history><date date-type="pub"><published-time>2026-07-26</published-time></date></history><abstract>Generative AI, large-scale audio models, algorithm-driven music recommendation, and other technologies have permeated the whole process of public music reception, creation, and appreciation. For learners receiving music-related education, aesthetic practices are no longer confined to classroom-based scenarios; instead, much of their aesthetic engagement unfolds within online media environments aided by AI tools. Moving beyond classroom-centered research perspectives, this paper centers on learners&amp;rsquo; autonomous music-aesthetic practices. It sorts out the dual-faceted impacts brought by AI intervention in aesthetic activities, clarifies the intrinsic dimensions of music aesthetic literacy, and draws on surveys of learner groups to identify practical challenges including algorithm-induced information cocoons, aesthetic alienation in AI-generated musical works, and ambiguous aesthetic judgment criteria [1]. It further explores feasible approaches for learners to build independent music-aesthetic literacy amid AI-enabled environments. This study offers theoretical references for music-education learners to shape their aesthetic capabilities in digital-intelligent settings, and provides insights for advancing music aesthetic education in non-instructional contexts.</abstract><keywords>AI technology, Music learners, Music aesthetic literacy, Algorithm-driven aesthetics, Generative AI</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] Hou W, 2026, Exploration and Practical Reflection on the Path of Universities Empowering High-quality Development of Regional Basic Education&amp;mdash;A Ten-Year Case Study Based on Southwest University&amp;rsquo;s Service to Hainan Education. New Explorations in Education and Teaching, 4(8): 123&amp;ndash;126.
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