• R-squared = 0.969 → The model explains 96.9% of the variance in overall alienation scores, meaning it is strong but not overfitted like the previous model.

• All four alienation types are statistically significant (p < 0.001), confirming that each form of alienation contributes to the overall perception of AI-related alienation.

• Coefficient Interpretation:

  • Alienation from Species-Being (coef = 0.2336) → Most influential in predicting overall alienation.

  • Alienation from Labour Process (coef = 0.1883) → Second strongest predictor.

  • Alienation from Interpersonal Relations (coef = 0.1673) → Significant but less than the first two.

  • Alienation from the Product of Labour (coef = 0.1537) → Still significant but has the lowest impact.

Key insights for the thesis analysis

1. Teachers feel the most alienated in terms of their species-being, meaning AI affects their sense of meaning, fulfilment, and creativity in teaching.

2. Alienation from the labour process is also a major concern, highlighting issues of standardisation and reduced autonomy in lesson planning.

3. Interpersonal alienation (AI-mediated tools distancing teachers from students and colleagues) plays a role but is less significant than species-being alienation.

4. Alienation from the product of labour is the least impactful, suggesting that while AI alters grading and assessment, it does not drastically impact teachers' sense of responsibility for student outcomes.