TEACHER–AI DIALOGIC COLLABORATION AS A PEDAGOGICAL PARADIGM: MECHANISMS FOR INTEGRATING GENERATIVE ARTIFICIAL INTELLIGENCE INTO ADAPTIVE INSTRUCTIONAL DESIGN

Abstract

The rapid proliferation of generative artificial intelligence (AI) tools in education demands a principled theoretical framework for understanding how teachers can effectively collaborate with these systems. This paper introduces and empirically validates the concept of Teacher–AI Dialogic Collaboration (TADC) as a foundational pedagogical paradigm for integrating generative AI into adaptive instructional design. Drawing on a quasi-experimental study conducted at two universities in Uzbekistan (n = 120; EG = 60, CG = 60), the paper argues that the defining characteristic of productive teacher–AI interaction is not command-based tool use, but iterative, reflective dialogue in which the teacher critically evaluates, refines, and pedagogically contextualises AI-generated content. Four interdependent pedagogical mechanisms are identified: motivational-axiological, cognitive-constructive, activity-design, and reflective-personal. The TALIA software system (registration certificate DT 202606091, 09.05.2026), developed on Google Gemini API, operationalises these mechanisms through a four-function content-generation architecture. Experimental results demonstrate statistically significant and practically large gains across all four competence components (p < .001; Cohen's d = 2.98–4.94). The findings reframe AI not as a productivity shortcut but as an intelligent pedagogical partner, with implications for teacher training policy and AI literacy standards in Central Asia and beyond.