Design and Validation of Generative Artificial Intelligence-Assisted Personalized English Learning Pathways

Abstract

The traditional model of English language teaching struggles to accommodate individual learner differences. Generative Artificial Intelligence (GAI), with its capabilities for dynamic content generation and deep contextual understanding, offers a new paradigm for constructing genuinely adaptive personalized learning pathways. This study systematically explores the design and validation of such a pathway: first, it elucidates the theoretical rationale of how its "emergent generation" surpasses the traditional logic of "predefined selection"; subsequently, it proposes a comprehensive framework integrating dynamic learner modeling, sequential content generation, real-time pathway adaptation, and human-AI collaborative decision-making; finally, it constructs an evaluation framework encompassing personalization metrics, content reliability, and technical ethics. The study also looks ahead to evolutionary directions such as multimodal interaction and affective computing, aiming to provide a theoretical foundation and design references for next-generation, deeply personalized language learning systems.