TEACHER–AI DIALOGIC COLLABORATION AS A PEDAGOGICAL PARADIGM: MECHANISMS FOR INTEGRATING GENERATIVE ARTIFICIAL INTELLIGENCE INTO ADAPTIVE INSTRUCTIONAL DESIGN (Modern American Journal of Linguistics, Education, and Pedagogy, 2026)
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.