Revolutionizing Language Learning: AI-Assisted Learning as a Catalyst for High Proficiency Attainment

Abstract

This action research project investigates whether an AI-Assisted Learning Framework can help instructors and learners identify, track, and reduce recurring errors and non-native structures among intermediate and advanced Chinese learners at the Defense Language Institute Foreign Language Center. Recurring grammatical, lexical, and discourse patterns were coded as key forms when they met three criteria, distinguishing them from fatigue slips or one-off mistakes. Over a 19-week cycle, five learners engaged in an AI-enhanced intervention integrating five coordinated strategies: Error-Pattern Detection, Micro-Targeted Adaptive Exercises, Multimodal Feedback, AI-Mediated Conversational Tutoring, and Adaptive Review through Spaced Repetition. Data sources included pre- and post-test scores, logs, and learner journals. AI tools were used to assist instructors in identifying key forms across student output and generating targeted instructional materials, while all pedagogical decisions and validation remained instructor-mediated. Learners engaged in approximately 4.2 hours per week of AI-supported activities in addition to full-time classroom instruction. Findings indicate that three of the five learners demonstrated measurable ILR gains, while the frequency of key form errors declined by approximately 40%. Learner reflections suggest increased awareness of habitual error patterns and greater engagement with feedback processes. Although improvements cannot be attributed to AI use alone, the results suggest that structured, continuous diagnostic cycles may enhance the salience and treatment of persistent difficulties within high-intensity language programs. The study proposes an AI-assisted learning framework as a practical, instructor-guided approach for integrating diagnostic feedback, targeted practice, and longitudinal monitoring into intensive language instruction.