Research on the Innovation of “Chinese + Music” Integrated Education Empowered by Artificial Intelligence: An Empirical Study on Multimodal Learning of International Students in Zhejiang Province

人工智能赋能“中文+音乐”融合教育创新研究—以来浙江省华留学生的多模态学习实证为例

This study constructs and validates an AI-integrated “Chinese + Music” intelligent teaching model aimed at simultaneously enhancing international students’ Chinese language proficiency and musical professionalism. Through a 12-week controlled experiment (N=60), the findings reveal that, in terms of language learning, the intelligent speech feedback system improved students’ tone recognition accuracy by 27.2% (p<0.01) and grammar mastery by 37%. Notably, music-based tone training significantly reduced the confusion between the second and third tones, with error rates dropping by 72%. In the domain of professional instruction, the adaptive terminology system achieved an 88.7% mastery rate of musical terms, while spatiotemporal visualization technology increased the learning efficiency of music history by 40% and reduced study time by 43.8%. The AI composition platform further boosted the accuracy of pentatonic scale application by 61%. Qualitative analysis shows that intercultural creative tasks enabled 76% of students to deeply interpret the cultural connotations of musical symbols. The study also identifies boundary conditions in the application of AI: a 35% misjudgment rate in creative work evaluation, 20% of students requiring additional support for abstract theoretical instruction, and 15% exhibiting dependency on technology. In response, the paper proposes a “Dual-Channel Digital Humanities” development path, emphasizing the integration of a cultural annotation database, an adaptive difficulty matrix, and a human-machine collaborative mechanism to achieve a deep fusion of technological empowerment and humanistic education. This model provides an innovative paradigm for discipline-specific language teaching in the era of intelligent education.

本研究构建并验证了一种融合人工智能技术的“中文+音乐”智能教学模型，旨在同步提升留学生的中文语言能力与音乐专业素养。通过为期12 周的对照实验（N=60），研究发现：在语言学习方面，智能语音反馈系统使学生的声调识别准确率提升27.2%（p<0.01），语法掌握度提高37%，其中音乐化声调训练显著改善了阳平-上声混淆问题（错误率降低72%）；在专业教学方面，自适应术语系统实现88.7%的术语掌握率，时空可视化技术将音乐史学习效率提升40%且耗时减少43.8%，AI 作曲平台更使五声音阶应用正确率增长61%。质性分析显示，跨文化创作任务促使76%的学生能深度阐释音乐符号的文化内涵。研究同时发现技术应用的边界条件：AI 在创意评估（35%误判率）和抽象理论教学（20%学生需辅导）方面存在局限，且需解决15%学生的技术依赖问题。基于此，本文提出“数字人文双通道”发展路径，强调通过文化标注数据库、自适应难度矩阵和人机协同机制，实现技术赋能与人文教育的深度融合，为智能时代的专业语言教学提供创新范式。