Modern Social Science Research

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.

Generative AI; College English; Tiered reading; Teaching material generation; Teaching adaptation; Human-machine collaboration