Replication materials: Generative AI use in geometry learning and pre-service mathematics teachers' readiness to teach with AI

The study used PLS-SEM, MICOM and PLS-MGA with survey data from 297 pre-service mathematics teachers at two public teacher-education universities (group E, n = 203; group W, n = 94).

Participants did not consent to the public release of individual responses, so individual-level data are not shared. The package provides aggregate data (item correlation matrices for the full sample and each group, item and dimension descriptives, aggregated sample characteristics), the Python analysis code, a script that reproduces all PLS-SEM point estimates from the shared correlation matrices, and the complete numerical results behind Tables 1 and 2 and Figures 1 and 2.