Research on Stratified Teaching Design of CET‐4 Listening in Private Colleges Empowered by Artificial Intelligence

Yueping Zhang

doi:10.70088/hxpc4e48

Authors

Yueping Zhang Changchun University of Finance and Economics, Changchun, China Author

DOI:

https://doi.org/10.70088/hxpc4e48

Keywords:

artificial intelligence, higher education, english teaching, listening comprehension, stratified teaching

Abstract

Against the backdrop of smart-education development, artificial intelligence provides robust technical support for personalized college-English instruction. Currently, private-college students exhibit substantial proficiency gaps in listening comprehension: many demonstrate weak linguistic intuition, possess insufficient news-listening vocabulary, show poor adaptability to standardized exam audio, and lack targeted tiered training. Consequently, the conventional "one-size-fits-all" pedagogical mode creates typical instructional dilemmas, wherein low-achieving students fall behind, intermediate learners face persistent improvement bottlenecks, and high-proficiency students lack advanced training opportunities. Grounded in Krashen’s Input Hypothesis and the fundamental principle of teaching students in accordance with their individual aptitude, this paper constructs a comprehensive, AI-driven stratified teaching framework specifically designed for CET-4 listening among private-college undergraduates. A structured 16-week phased instructional scheme is meticulously designed with three distinct proficiency tiers, namely basic, intermediate, and advanced levels. This innovative framework incorporates systematic news-vocabulary accumulation, tier-specific listening resources, dynamic student regrouping mechanisms, and multi-dimensional evaluation metrics. Tailored specifically to the unique learning realities of private-college environments, this design significantly improves learners’ news-listening competence and overall CET-4 listening performance. Ultimately, this study offers practical, evidence-based references for advancing stratified-listening pedagogy and optimizing English language instruction within private higher-education institutions globally. Furthermore, the integration of intelligent algorithms ensures continuous feedback and adaptive learning pathways, thereby maximizing educational outcomes and fostering long-term linguistic development for diverse student populations.

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