A Study on Task-Based English Teaching Design in Higher Education Based on Large Language Models

Abstract

With the rapid development of artificial intelligence, Large Language Models (LLMs) as ChatGPT utilize demonstrated strong capabilities in natural language understating at generation, providing new possibilities for innovative teaching in higher education. The explores integration of LLMs into task-based English teaching to enhance students’ language competence through interactive, meaningful in contextualized learning activities. Has analyzing the theoretical foundation of Task-Based Language Teaching (TBLT) at the pedagogical affordances of LLMs, the system design incorporates a modular pipeline consisting of a prompt pre-processor, an LLM-based task response engine with adaptive feedback module, allowing for seamless integration into existing teaching platforms. Experimental deployment in two undergraduate English courses, with one group using the LLM-enhanced system group relying on conventional task-based instruction. Quantitative results experimental group outperformed the control group in Task Performance Score and Learner Engagement Index with statistical significance. Furthermore, qualitative feedback from learners and instructors indicates increased engagement, confidence in linguistic creativity. The results suggest that LLMs support learner autonomy at engagement, improve linguistic accuracy fluency, over-reliance on AI in the teacher's evolving role in offers suggestions for future integration of LLMs in higher education English pedagogy.

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