Innovative Approaches in Foreign Language Learning Using AI and Project-Based Education

Abstract

The current situation of foreign language acquisition and the problems existing in traditional pedagogical models are proposed to achieve higher learner engagement, improved language retention, and adaptive feedback mechanisms. This study aimed at opening new pathways for AI-assisted linguistic competency development that affects curriculum design and instructional methodologies and putting forward corresponding policy recommendations and technological advancements. In this study, the Analytical Hierarchy Process (AHP) regression method in AI-assisted foreign language education combines the project-based learning (PBL) approach based on collaborative problem-solving, real-world application, and digital assessment tools. Based on the innovative design method of AI-driven adaptive learning, the extracted feature representations from student interactions are used to build a more accurate predictive learning performance model. The research results show that the constructed model can systematically analyze language proficiency development trends, and AI-supported language learning is more efficient, more learner-centric, and more scalable to diverse educational contexts.