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
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.