From Text to Thought: A Discourse-Based Approach to Teaching Expository Reading in Chinese Senior Secondary Classrooms Using AI-Powered Analysis

Abstract

This study examines the use of AI-based discourse analysis tools to teach expository reading which is
an aspect of senior secondary EFL education in China. The research is based on the concept of Zone of Proximal Development (ZPD) of Vygotsky. It discusses the manner in which Artificial Intelligence can be used as a meditation tool to help students to grasp the intricate organizations of texts. The study has a qualitative case study design, and it is based on the information that is collected through learner feedback, tutor reflection journals, and classroom observations. According to the results, AI-aided instruction is pivotal in enhancing the awareness of the students regarding discourse issues such as problem-solution, cause-effect, and comparison-contrast models. It is also conducive to profound levels of understanding, and it makes the students self-reliant as they are provided with individual responses and in real time. The role of teachers was crucial, as they helped learners to process AI outputs and efficiently used feedback. The study has allowed it to see some benefits but also identifies some challenges as the tendency of the students to rely more on the suggestions provided by AI than the need to and initial technical issues that they have encountered. The research results suggest that AI tools have significant opportunities in EFL education, but their performance depends on their thoughtful integration and active teacher scaffolding. The study concludes with pedagogical support of finding a balance between the AI support and human instruction and gives directions on future research on the long-term effects of AI-mediated reading behaviors on the second language learning experience.