AI-Mediated Phonetic Automatization Theory (AIPAT): A Conceptual Model for Accelerated L2 Pronunciation Development in Adult Learners

The integration of artificial intelligence (AI) into second language (L2) pronunciation training has transformed how learners receive feedback, yet theoretical models in L2 phonetics have not kept pace. This paper proposes the AI-Mediated Phonetic Automatization Theory (AIPAT) a domain-specific conceptual model that explains how AI-driven feedback accelerates the transition from controlled to proceduralized speech production in adult EFL learners. Grounded in empirical observations with Arabic-speaking English teachers, AIPAT posits that true phonetic automatization requires the convergence of two independent markers: (1) reduced speech onset latency (a cognitive indicator of processing efficiency) and (2) stabilized acoustic parameters (e.g., consistent friction duration for /θ/). The model introduces four core assumptions, including the Temporal Precedence Principle, which holds that reaction time improvements precede acoustic stabilization a reversal of traditional sequencing in pronunciation pedagogy. AIPAT outlines a three-stage developmental trajectory and generates falsifiable predictions concerning phoneme markedness, adult plasticity, and non-linear learning curves. While not claiming universal applicability, this micro-theory offers a testable framework for experimental phonetics, intelligent tutoring systems, and L2 teacher education.