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 im mediate and 589 delayed outcomes. Kenward–Roger analyses estimated an adjusted imme diate difference of 2.78 points (95% confidence interval (CI) [2.02, 3.55]; p < 0.001; model basedd=0.72)andadelayeddifferenceof2.34points(95%CI[1.54, 3.14]; p<0.001; d = 0.51). The results show a positive adjusted association for the evaluated traceable instruc tional configuration, but deterministic post-baseline allocation limits causal interpretation.