AI AND ENGLISH LANGUAGE LEARNING OUTCOMES

Abstract

This study explores the transformative role of AI in enhancing English language learning outcomes for underperforming students (D grades) across diverse academic levels (1st to 8th semesters). Recognizing that standard AI tools fail to cater effectively to English for Specific Purposes (ESP) for students with varied abilities, the researchers devised a customized AI-driven approach centered on Outcome-Based Education (OBE) and student-centered strategies. The paper examines AI technologies, including natural language processing (NLP), machine learning, speech recognition, and intelligent tutoring systems, which enable personalized learning experiences. These tools were adapted to students' individual proficiencies, delivering real-time feedback, targeted practice, and contextualized tasks that enhance speaking and writing skills. The research emphasizes interactive and data-driven methodologies that foster deeper engagement and sustained motivation. Empirical data were drawn from an experimental study employing Jawaid TESOL Benchmarking Model (2014), highlighting benchmark outcomes. Critical challenges, including ethical concerns, data privacy, equitable access, and educators' preparedness for AI integration, are also discussed. The research culminates in a structured framework for embedding AI technologies in English language courses, emphasizing scalable, inclusive, and sustainable practices. By redefining language pedagogy, the study positions AI as a paradigm shift that advances educational equity and promotes student-centered learning in technologically enhanced environments.