TITLE:
Generative AI-Enabled Design of Logic-Oriented English Reading Homework for Senior High Schools
AUTHORS:
Qinlin Wu
KEYWORDS:
Generative AI, Logical Thinking, Senior High School English Reading Homework, Homework Design
JOURNAL NAME:
Open Journal of Social Sciences,
Vol.14 No.8,
August
27,
2026
ABSTRACT: The Senior High School English Curriculum Standards (Ministry of Education, 2017 Edition, 2020 revision) identifies “thinking quality” as one of the four core competencies in English language education, with logical thinking serving as its foundational component. In practice, however, senior high school English reading homework has largely remained at lower-order cognitive levels such as vocabulary memorization, grammar drills, and literal comprehension, failing to provide systematic training in logical reasoning. Drawing on Du Guoping’s five-dimensional framework of logical thinking, this study employs a mixed-methods approach to examine the distribution of logical thinking dimensions in current reading homework. The findings reveal a significant imbalance across the five dimensions: foundational dimensions such as clarifying concepts and making accurate judgments are relatively well represented, whereas higher-order dimensions including rigorous reasoning, constructing sound arguments, and identifying fallacies are critically underrepresented. To address this issue, the study proposes four AI-empowered pathways: intelligent item generation, chain-of-thought-driven reasoning visualization, tiered task adaptation, and precision feedback aligned with logical dimensions. A case study using the reading text “Travel Peru” illustrates the practical application of these pathways. The study offers a theory-grounded framework and practical routes for the systematic integration of generative AI into reading homework design as an effective vehicle for fostering logical thinking.