A Case Study on AIGC Empowered Task - Based Teaching in University English

Abstract

Because Generative Artificial Intelligence (AIGC) has opened up new prospects for the reform of college English teaching, and
since improving students' English application ability is a central goal of instruction in the context of professional accreditation, this paper
naturally and logically turns to Task-Based Language Teaching (TBLT) to investigate the application of AIGC through four classroom teaching cases. The four cases correspond to the four task types: information matching, data analysis, strategic interaction, and experiential inquiry,
thus very naturally and clearly illustrating different roles of AIGC as a lesson preparation tool, classroom support tool, and learning scaffold.
From the analysis it follows unambiguously that AIGC behaves differently across task types and that its effectiveness is contingent upon task
design, teacher guidance, and proper tool selection. Therefore, AIGC-enhanced task-based teaching holds great promise for improving students' English application ability and offers concrete direction for college English teaching reform under accreditation-oriented education.