Adoption and Use of Generative Artificial Intelligence in Teaching Practice
Description
Honestly, we still don't know enough about what teachers actually think about generative AI and whether they're really willing to use it in their classrooms. That's exactly where this study comes from—trying to listen to the people who are actually teaching and figure out if these tools can realistically become part of everyday university life. We set out to explore how the use of generative AI by faculty members connects to what they see as real benefits, because that perception is probably what will determine whether they end up adopting it or not. So we built a model that tries to capture that sense of usefulness and the potential they see in these technologies as teaching aids. We tested that model with a group of professors from different universities in El Salvador, checking its reliability, its validity, and whether it actually measures what we're after. In the end, we're hoping to end up with a refined, practical instrument—one that doesn't just sit on a shelf, but that can help identify training gaps, guide institutional decisions, and most importantly, give us a clearer picture of how ready faculty really are to innovate with these tools. Because at the end of the day, this isn't really about technology—it's about people who teach, and they need more than good intentions to bring AI into their practice in a thoughtful, meaningful way.
Files
Steps to reproduce
Steps for Data Analysis Stage 1: Sample characterization 1. Calculate measures of central tendency (mean, median, mode) for numerical variables. 2. Calculate measures of dispersion (standard deviation, variance) for numerical variables. 3. Perform counts and percentage distributions for categorical variables. Stage 2: Exploratory Factor Analysis (EFA) 4. Apply Unweighted Least Squares (ULS) extraction method. 5. Apply Promax rotation. 6. Verify sampling adequacy with KMO (> 0.70) and Bartlett's test of sphericity (significant). 7. Retain items with factor loadings > 0.40. Stage 3: Confirmatory Factor Analysis (CFA) 8. Estimate the model using Maximum Likelihood with Robust Standard Errors (MLR). 9. Evaluate fit indices: CFI > 0.90, TLI > 0.90, RMSEA < 0.08, SRMR < 0.08. Stage 4: Reliability tests 10. Calculate Cronbach's alpha (α) for each dimension. 11. Calculate McDonald's omega (ω) for each dimension. 12. Verify that both coefficients are > 0.80. Stage 5: Contrast of factors and variables of interest 13. Apply chi-square (χ²) test of independence for categorical variables. 14. Calculate Spearman's rho (ρ) correlation for ordinal variables. 15. Interpret the significance and magnitude of the associations found.
Institutions
- Universidad Dr. Andrés BelloSan Salvador Department, San Salvador