在钢琴教育中使用人工智能驱动的情感智能:深度学习模型用于表达性表现教练
1Department of Keyboard Music Education, Shenyang Conservatory of Music, Shenyang, China.
Acta psychologica
|January 20, 2026
概括
人工智能 (AI) 反系统显著提高了音乐学生的钢琴表现力. 与传统教学方法相比,基于人工智能的系统在表达式钢琴演奏方面取得了更大的收益.
科学领域:
- 音乐教育 技术 音乐教育 技术
- 音乐中的人工智能
- 钢琴教学 钢琴教学
背景情况:
- 人工智能 (AI) 在高等音乐教育中的整合需要评估它对学生表现的影响.
- 智能反系统为增强音乐表演表达品质提供了一个潜在的途径.
- 评估人工智能在提高钢琴表达力的有效性对于教学进步至关重要.
研究的目的:
- 调查基于人工智能的分析性能评估系统是否比传统指导更有效地提高钢琴表现力.
- 量化AI反对特定表达元素 (如动态,措辞和情感传递) 的影响.
- 为AI在音乐教育中的实用性提供经验证据.
主要方法:
- 开发一种使用BiLSTM注意力技术的AI系统,用于分析钢琴表达力.
- 随机对照试验,对290名本科钢琴学生进行随机对照试验,这些学生被分配到人工智能支持的或传统的教学组.
- 一个为期三个月的培训期间,随后进行表现力的混合评分,结合客观分析和专家评估.
主要成果:
- 人工智能支持的小组在钢琴表达力上表现出明显的改善 (ΔM = 12.97,p < .001,d = 1.80),这表明效果大小很大.
- 接受传统指导的对照组表现出表现能力的最小和统计学上无意义的变化 (p = .092,d = 0.14).
- 基于人工智能的反经验证明可以显著提高学生的情感表达力.
结论:
- 基于人工智能的分析性绩效评估系统可以显著提高本科生钢琴表现力.
- 智能反系统为培养音乐表达能力提供了传统指导的优越替代方案或补充.
- 这些发现支持在高等音乐教育中更广泛地应用人工智能技术,以有针对性的技能发展.
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