Assessing the Effectiveness of Generative Artificial Intelligence in Hazard Identification on Construction Sites

The construction industry remains one of the most perilous, where hazard identification is often inconsistent. Hazards are still missed when teams rely mainly on traditional approaches like checklists, Job Safety Analysis (JSAs)/Job Hazard Analysis (JHAs), and individual experience. This study evaluated whether a GEN-AI-assisted approach improved hazard identification performance compared with traditional approaches, using expert-verified ground truth for scoring. A quantitative within-subjects experiment was conducted with 51 participants. Each participant completed hazard identification in four conditions: traditional–pre, GEN-AI–pre, traditional–post, and GEN-AI–post, with a short training session on hazard identification delivered between the pre- and post-stages. Effectiveness was measured using the F1 score, combining both precision and recall. For analysis, traditional and GEN-AI performance were compared at each stage using paired-sample t-tests, and the overall pattern was tested using a 2 × 2 repeated measures ANOVA. The results showed that GEN-AI support produced significantly higher performance than the traditional approaches at both stages (p < 0.05). The repeated measures ANOVA confirmed a strong overall method effect. However, the overall intervention effect was small, and the method × intervention interaction was negligible, with no statistically significant change over time (p > 0.05). Overall, the findings indicated that GEN-AI support improved hazard identification accuracy relative to traditional approaches in this dataset, with limited evidence of additional gains from the training intervention. This study contributes towards providing empirical evidence that GEN-AI improves hazard identification and strengthens proactive prevention, but final outputs need human validation.