Data_Alg_Juarez-et-al

This article formalizes AI-assisted assessment as a discrete-time algorithm and evaluates it in a digitally transformed higher-education setting. We integrate an agentic retrieval-augmented generation (RAG) feedback engine into a sixiteration dynamic evaluation cycle and model learning with three complementary formulations: (i) a linear difference update linking next-step gains to feedback quality and the gap-to-target, (ii) a logistic convergence model capturing diminishing returns near ceiling, and (iii) a relative-gain regression quantifying the marginal effect of feedback quality on the fraction of the gap closed per iteration.