Generative AI-Driven Standardized Knowledge Governance Framework for College Classical Music Aesthetic Teaching
Aiming at the problem of non-music majors in college failing to convert abstract musical auditory information into stable aesthetic knowledge, this study constructs a framework for music teaching knowledge management. It establishes parametric coding rules to translate six core musical elements into standardized visual prompt knowledge and develops a full-process dynamic evaluation system to measure students' knowledge absorption, discussion participation, and knowledge migration. Taking Schubert's “The Devil” as a typical teaching knowledge carrier, three rounds of prompt iteration experiments optimize the matching between visual knowledge carriers and musical narrative knowledge nodes. Empirical results prove this knowledge governance framework reduces learners' cognitive burden, improves the internalization and long-term retention of music aesthetic knowledge, and clarifies the application boundary of intelligent knowledge tools. This research provides quantitative management standards for the whole-cycle production, iteration, and evaluation of music teaching knowledge.