Artificial Intelligence Music Platform for Piano Majors Improves Learning and Reduces Anxiety: Randomized Study with...

Area of Science:

• Music Pedagogy
• Artificial Intelligence
• Educational Technology

Background:

• Limited reproducible workflows exist for evaluating AI in music education, particularly concerning knowledge acquisition and anxiety.
• Existing research often overlooks the integration of AI tools into routine studio-class settings.

Purpose of the Study:

• To describe a randomized controlled workflow for implementing and assessing an AI-driven music education platform in undergraduate piano instruction.
• To evaluate the effects of AI-driven instruction on music theory knowledge and anxiety regulation.

Main Methods:

• A randomized controlled trial involving undergraduate piano majors comparing an AI platform to traditional instruction over two weeks (twelve 30-min sessions).
• Outcome measures included a music theory test, State-Trait Anxiety Inventory-State (STAI-S), and heart-rate variability (HRV) during rest and practice.
• HRV indices (RMSSD, SDNN, HF power) were analyzed alongside cognitive load measures.

Main Results:

• Participants using the AI-driven platform demonstrated significantly higher adjusted post-test music theory performance (partial η² = 0.206).
• AI intervention led to a significant reduction in post-intervention state anxiety compared to controls (t(38) = -3.486, p = 0.001; Cohen's d ≈ 1.10).
• HRV patterns offered physiological context, and secondary outcomes indicated reduced extraneous cognitive load.

Conclusions:

• The developed protocol provides a standardized, reproducible workflow for deploying AI in music pedagogy.
• AI-driven music education platforms show promise in enhancing theoretical knowledge and mitigating performance anxiety in piano students.
• This approach integrates physiological and cognitive measures for a comprehensive evaluation of AI's impact on learning.