AI-driven emotional intelligence in piano education: Deep learning models for expressive performance coaching

Area of Science:

• Music Education Technology
• Artificial Intelligence in Music
• Piano Pedagogy

Background:

• The integration of artificial intelligence (AI) in higher music education necessitates evaluating its impact on student performance.
• Intelligent feedback systems offer a potential avenue for enhancing expressive qualities in musical performance.
• Assessing the efficacy of AI in improving piano expressivity is crucial for pedagogical advancement.

Purpose of the Study:

• To investigate whether an AI-based analytical performance assessment system enhances piano expressivity more effectively than traditional instruction.
• To quantify the impact of AI feedback on specific expressive elements such as dynamics, phrasing, and emotional conveyance.
• To provide empirical evidence for the utility of AI in music education.

Main Methods:

• Development of an AI system utilizing BiLSTM attention technologies for analyzing piano expressivity.
• Randomized controlled trial with 290 undergraduate piano students assigned to AI-supported or traditional instruction groups.
• A three-month training period followed by hybrid scoring of expressivity, combining objective analytics and expert evaluations.

Main Results:

• The AI-supported group showed a substantial improvement in piano expressivity (ΔM = 12.97, p < .001, d = 1.80), indicating a large effect size.
• The control group receiving traditional instruction exhibited minimal and statistically nonsignificant changes in expressivity (p = .092, d = 0.14).
• AI-based feedback was empirically demonstrated to significantly enhance students' emotional expressivity.

Conclusions:

• AI-based analytical performance assessment systems can significantly enhance piano expressivity in undergraduate students.
• Intelligent feedback systems offer a superior alternative or supplement to traditional instruction for developing musical expressivity.
• The findings support the broader application of AI technologies in higher music education for targeted skill development.