AI Music Platform Study for Piano Majors with Anxiety and Heart Rate Data

A 2-week randomized controlled trial conducted with undergraduate piano majors, comparing an AI-driven music education platform against traditional instruction. The dataset includes results from a structured music theory test, the State-Trait Anxiety Inventory (STAI-S), and heart rate variability (HRV) indices recorded during rest and practice. The protocol and representative results were authored by Beibei Zhou and published on figshare in April 2026.

Use Cases

• Compare learning outcomes between AI-driven and traditional music instruction based on the structured music-theory/analysis test.
• Analyze the relationship between instructional method and state anxiety based on the State-Trait Anxiety Inventory–State (STAI-S) scores.
• Investigate physiological correlates of cognitive load and anxiety during practice based on heart rate variability (HRV) indices like RMSSD, SDNN, and HF power.

Strengths

• Dataset is based on a randomized controlled trial protocol, a rigorous experimental design.
• Includes multiple outcome measures: knowledge acquisition (theory test), psychological state (anxiety inventory), and physiological data (HRV).
• File size is 630.9 KB, indicating a focused and manageable dataset for analysis.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The study sample is specific to undergraduate piano majors, which may limit generalizability.

Provenance

Source

figshare

Collection Method

Data collected via a randomized controlled trial protocol involving screening, randomization, platform use, HRV recording, and outcome measurement.

Time Range

Covers a 2-week intervention period.

Freshness

Last updated 2026-04-20 03:55:23; freshness should be verified.

Geography

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