Balancing Efficiency and Depth in the Integration of Generative Artificial Intelligence into EAP Learning for Chinese Undergraduates

Abstract

This study explores the integration of generative artificial intelligence tools into English for Academic Purposes (EAP) learning among Chinese undergraduates. The study adopts a questionnaire to investigate behavioral patterns, attitudinal structures, and perceived advantages of AI-assisted academic English learning. Findings indicate high level of AI exposure and positive perceived functionality of AI tools, especially in facilitating literature reading and writing. However, students demonstrated limited awareness of academic conventions and AI-related integrity issues, revealing an efficiency-depth paradox. Exploratory factor analysis identified three attitudinal dimensions, including perceived usefulness, self-assessed evaluation skills, and ethical apprehension. While students recognized dependence and plagiarism risks, such concerns coexisted with high reliance on AI. The findings highlight the need for targeted instructional strategies, such as offering dual literacy development, ethical training, and AI-integrated tasks in EAP courses, to balance productivity with critical skill development in AIGC-enhanced EAP contexts.