Will future music teachers accept generative AI?
Among 301 pre-service music teachers, perceived risk, social influence, and habit shaped intention to use generative AI; intention and perceived risk predicted actual use.
Source
Exploring pre-service music teachers' acceptance of generative artificial intelligence: a PLS-SEM-ANN approach
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Will future music teachers accept generative AI?
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301 valid questionnaires were analysed.
A total of 301 valid questionnaires were collected.
Pilot reliability Cronbach’s alpha was 0.91.
Cronbach's alpha value of 0.91, demonstrating that the questionnaire is reliable.
What they did
Extended UTAUT2 with Perceived Compatibility and Perceived Risk, surveyed pre-service music teachers, and analysed drivers of Behavioral Intention and Actual Usage with PLS-SEM plus ANN.
What they found
Perceived Risk, Social Influence, and Habit significantly influenced Behavioral Intention; Behavioral Intention and Perceived Risk predicted actual use. Pilot Cronbach’s alpha was 0.91; sample met power target of 301.
The limits
What it doesn't show
Cross-sectional self-report in one national training context cannot prove classroom outcomes or causal effects of training interventions.
Key terms
- UTAUT2
- Unified Theory of Acceptance and Use of Technology model extended for consumer/user contexts.
- PLS-SEM
- Partial least squares structural equation modelling for testing hypothesized paths.
- ANN
- Artificial neural network used for nonlinear predictive/sensitivity analysis.
- Behavioral Intention (BI)
- Stated readiness to use generative AI in future teaching.
- Perceived Risk (PR)
- Added UTAUT2 factor capturing downside concerns about AI use.
- Perceived Compatibility (PC)
- Added factor for fit between generative AI and teaching needs/practice.
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Common questions
Sample size?
301 valid questionnaires.
Key predictors of intention?
Perceived Risk, Social Influence, and Habit.
What predicted actual use?
Behavioral Intention and Perceived Risk.
Analysis methods?
PLS-SEM and ANN.