Structure and Implementation of AI-Driven Practical English Textbooks

Forum topic · 小凯 · 2026-09-06

Summary

This paper (arXiv:2509.00001) by Ya Wang, Lei Zhang, and Xueguang Yang studies the structure and application of a new practical English textbook driven by artificial intelligence. AI is transforming applied English learning materials from fixed paper sequences into adaptive systems that diagnose learners, recommend tasks, and provide formative feedback. The authors propose a five-layer architecture consisting of knowledge mapping, learner profiling, task generation, feedback orchestration, and teacher-side governance. A prototype was tested with 186 non-English-major undergraduates over eight weeks of instruction. Compared with a static digital textbook, the AI-driven system raised unit completion accuracy from 72.4% to 84.9%, improved average speaking task scores by 10.8 points, and reduced teacher correction time by 31.6%. The results suggest AI-driven textbooks can deliver personalized learning paths, richer practice material, and traceable classroom data while preserving curricular stability.

Overview

Field: AI/ML
Authors: Ya Wang, Lei Zhang, Xueguang Yang
Published: 2026-09-06
arXiv: 2509.00001

Abstract

Artificial intelligence is changing the form of applied English materials from fixed paper sequences to adaptive learning systems that can diagnose learners, recommend tasks, and provide formative feedback. This paper studies the structure and application of a new practical English textbook driven by artificial intelligence. A five-layer architecture is proposed: knowledge mapping, learner profiling, task generation, feedback orchestration, and teacher-side governance. A prototype was tested on 186 non-English-major undergraduates for eight weeks of teaching. Compared with a static digital textbook, the proposed system increased the unit completion accuracy from 72.4% to 84.9%, raised the average score for speaking tasks by 10.8 points, and reduced the teacher's correction time by 31.6%.

Key Findings

Five-layer architecture: knowledge mapping, learner profiling, task generation, feedback orchestration, and teacher-side governance.
Unit completion accuracy improved from 72.4% to 84.9% versus a static digital textbook.
Speaking task scores rose by an average of 10.8 points.
Teacher correction time decreased by 31.6%.
The study involved 186 non-English-major undergraduates over an eight-week teaching trial.
AI-driven textbooks can offer personalized learning paths, richer practice material, and traceable classroom data while maintaining curricular stability.