DIDACTIC CONDITIONS FOR APPLYING DYNAMIC ARTIFICIAL INTELLIGENCE MODELS IN PREPARING STUDENTS FOR INTERNATIONAL STANDARDIZED EXAMINATIONS
Abstract
Artificial intelligence now forecasts examination outcomes faster than pedagogy has learned to use those forecasts well. This study asks a narrower question: what has to be true for a dynamic AI model to teach, not merely predict, when preparing students for the SAT, ACT, GRE, and GMAT? Drawing on Vygotsky's zone of proximal development, the TPACK framework, Bloom's taxonomy, and Knowles' andragogy, the analysis ties a diffusion-type stochastic model, based on a generalized Ornstein-Uhlenbeck process, to four conditions: continuous diagnostic monitoring, individualized content and pace, teacher readiness to interpret the model, and learner reflexive-regulatory competence. Each condition anchors to a distinct term in the same governing equation, which links pedagogical theory and mathematical formalism in a way not previously made explicit. Four limitations are named, and a testable hypothesis closes the paper.
Keywords
dynamic artificial intelligence models; didactic conditions; competence-based approach; individualization; zone of proximal development; international standardized examinations; diagnostic monitoring; TPACK; andragogy; Bloom's taxonomy.How to Cite
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Copyright (c) 2026 D.B Dushabaeva , M.T Kuchimova
This work is licensed under a Creative Commons Attribution 4.0 International License.
