Research on generative AI-enabled generation of tiered college English reading materials and teaching adaptation
生成式AI赋能大学英语分级阅读材料生成与教学适配研究

摘要

在教育数字化转型的大背景之下，大学英语分级阅读教学一直存在着材料供给单一、学情适配欠缺、反馈环节迟缓等诸多结构上的难题，从而严重阻碍了学生语言能力的个性化发展以及高阶思维的培育。生成式人工智能技术取得的重大突破，给破解以上问题提供了颠覆性的技术途径和理论支持。本文主要研究生成式AI赋能大学英语分级阅读材料生成和教学适配的核心问题，对分级阅读的理论基础以及现实问题进行梳理，在智能语料生成、学情动态适配、多模态体验重构、人机协同反馈、伦理生态建设这五个方面提出了系统的解决办法。

Abstract

Against the backdrop of educational digital transformation, tiered reading instruction in college English has long faced structural challenges, including limited diversity of teaching materials, insufficient adaptation to students’ differentiated learning needs, and lagging feedback mechanisms. These bottlenecks greatly restrict the personalized development of students’ comprehensive language competence and the cultivation of their higher-order thinking skills. The major breakthroughs in generative artificial intelligence technology have provided a revolutionary technical approach and solid theoretical foundation for solving the above-mentioned problems. This paper mainly explores the core dilemmas in the application of generative AI to the generation and adaptive teaching of tiered college English reading materials. On the basis of reviewing the theoretical foundations and practical predicaments of tiered reading teaching, this study puts forward systematic optimization solutions from five core dimensions: intelligent corpus generation, dynamic adaptation oriented to learners’ learning progress, reconstruction of multimodal reading experience, human-machine collaborative teaching feedback, and construction of ethical education ecosystems.