声楽訓練におけるAI支援フィードバックと反省:メタコग्निションと歌唱能力への影響
Wen Li1,2, Xuerong Cui3, Pravina Manoharan2
1School of Teacher Education, Aba Teachers College, Wenchuan, Sichuan, China.
Frontiers in psychology
|September 3, 2025
まとめ
人工知能 (AI) とe-learningツールにより,声楽のトレーニングにおけるメタコグニッションが向上しました. 人工知能による学習は歌う能力を向上させましたが 認知能力の向上と相関せず 伝統的な方法を大幅に上回りませんでした
科学分野:
- 音楽 教育
- 教育技術
- 認知科学
背景:
- メタコग्निションは学習に不可欠で 優秀なミュージシャンには高いレベルです
- 思考とフィードバックは メタコग्निションを育むための重要な戦略です
- 音楽教育にAIとe-learningを統合することは新しい研究分野です.
研究 の 目的:
- 人工知能と電子学習ツールが認知能力と歌唱能力に与える影響を調査する.
- 音声音楽の訓練におけるフィードバックと反省の介入の有効性を調査する.
- 音楽教育にAIを導入するための枠組みを提供すること.
主な方法:
- 制御グループ (従来の指導) と実験グループ (AIツール,フィードバック,反省) との6週間の実験研究.
- 介入には自己評価ツール,大規模な言語モデル (AIチャットボット) と反省的なジャーナル作成が含まれていました.
- 双方向反復測定 ANOVAと線形混合モデルを使用して分析したデータ.
主要な成果:
- 人工知能によるトレーニングは 認知能力の発達に大きく影響を与えました
- 2つのグループで歌唱のパフォーマンスの有意な改善が見られたが,時間とグループの間の有意な相互作用効果は認められなかった.
- メタコग्निションと歌唱能力の相関は認められなかった.
結論:
- 人工知能とe-learningツールによって 個別化されたフィードバックを提供し,音楽教育における反省的な関与を促すことができます.
- これらの技術は,音楽訓練におけるメタコグニティブ開発の強化に有効な枠組みを提供する.
- AI,メタコग्निション,およびパフォーマンスの結果の微妙な関係を調査するには,さらなる研究が必要です.
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