Independent Engineering Transfer After Traceable Generative-AI-Assisted Learning: A Six-University Controlled Trial with Deterministic Cluster Allocation in Agricultural Engineering Education

Abstract

Generative artificial intelligence (GenAI) can support engineering problem solving, but whether AI-assisted practice transfers to independent performance after the tool is removed remains unclear. This multicentre controlled trial evaluated a traceable five-stage GenAI-assisted learning configuration in agricultural engineering education. Twenty-eight second- and third-year classes from six universities in Uzbekistan were assigned within nine teacher blocks by deterministic constrained minimization to GenAI (14 classes) or structured active-control (14 classes) groups. Both groups completed the same 16-week module, tasks, software, contact time, feedback, and verification requirements. They differed in the source and adaptivity of a provisional alternative used after an independent attempt: bounded adaptive GenAI dialogue versus a version-controlled curated alternative. The full assigned cohort included 656 students; likelihood-based available-outcome analyses included 641 immediate and 589 delayed outcomes. Kenward–Roger analyses estimated an adjusted immediate difference of 2.78 points (95% confidence interval (CI) [2.02, 3.55]; p < 0.001; model-based d = 0.72) and a delayed difference of 2.34 points (95% CI [1.54, 3.14]; p < 0.001; d = 0.51). The results show a positive adjusted association for the evaluated traceable instructional configuration, but deterministic post-baseline allocation limits causal interpretation.