Personalized Learning in College English Education: Leveraging Large Language Models for Adaptive Instruction

Large Language Models (LLMs) have emerged as powerful tools for enabling personalized learning in college English education, offering adaptive, engaging, and effective instructional experiences tailored to diverse student needs. This paper explores the transformative potential of LLMs, such as OpenAI's GPT-4 and ByteDance’s Doubao, in college English education. Through a case study of their implementation in a first-year English course, the study examines applications like adaptive content delivery, instant feedback, and teacher support. Findings demonstrate that LLMs improve student outcomes, increase engagement, and reduce teacher workload. A detailed integration framework is proposed, outlining step-by-step strategies for implementing LLMs, emphasizing the synergistic role of teachers and combining LLMs with other resources. Analysis of student acceptance, based on questionnaire data, reveals that while most students find LLMs engaging and helpful, concerns about accuracy, fairness, and cultural sensitivity highlight the need for human oversight and transparent communication. Challenges such as bias, over-reliance on technology, and technical barriers must be addressed to ensure responsible integration. This paper provides actionable insights for educators, administrators, and policymakers, advocating for a hybrid approach that combines AI and human instruction. By addressing these challenges, LLMs can create more inclusive, equitable, and innovative learning experiences in college English education.