AI-Mediated Exposure in Traditional Voice Lessons: Anxiety and Feedback Study

A one-semester study explores the role of AI in traditional university voice instruction compared to human mock-jury intervention and regular self-practice. The dataset likely contains measurements of state anxiety, autonomic regulation (RMSSD, LF/HF), and video-based meaning-making feedback analysis across four time points. It was authored by Yanyan Lin and last updated in May 2026.

Use Cases

• Analyzing the progression of state anxiety in voice students based on measurements taken across four time points.
• Comparing autonomic regulation metrics (RMSSD, LF/HF) between AI-exposure and control groups.
• Evaluating the evolution of feedback styles based on the described negotiating feedback ratio analysis.

Strengths

• Data collection spanned a one-semester longitudinal study.
• Measurements include physiological (autonomic regulation) and behavioral (feedback analysis) metrics.
• The dataset is licensed under CC-BY-4.0 for open use.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is small at 1.6 MB, indicating limited scale.

Provenance

Source

figshare

Collection Method

A one-semester study comparing AI-exposure, human mock-jury, and self-practice groups.

Time Range

One academic semester.

Freshness

Last updated 2026-05-28 07:36:10; freshness should be verified.