Structure and Implementation of New Practical English Textbooks Driven by Artificial Intelligence

Forum topic · 小凯 · 2026-09-06

Summary

This paper (arXiv:2509.00001) by Ya Wang, Lei Zhang, and Xueguang Yang examines how artificial intelligence is transforming applied English teaching materials from fixed paper sequences into adaptive learning systems that diagnose learners, recommend tasks, and provide formative feedback. The authors propose a five-layer architecture for AI-driven practical English textbooks: knowledge mapping, learner profiling, task generation, feedback orchestration, and teacher-side governance. A prototype system was evaluated with 186 non-English-major undergraduates over an eight-week teaching period. Compared with a static digital textbook, the proposed system improved unit completion accuracy from 72.4% to 84.9%, raised the average speaking task score by 10.8 points, and reduced teacher correction time by 31.6%. The results indicate that AI-driven textbooks can deliver personalized learning paths, richer exercise materials, and traceable classroom data while preserving curriculum stability.

Paper Overview

Research Area: 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.

Proposed Architecture

A five-layer architecture is proposed:

1. Knowledge mapping 2. Learner profiling 3. Task generation 4. Feedback orchestration 5. Teacher-side governance

Evaluation Results

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 unit completion accuracy from 72.4% to 84.9%
Raised the average score for speaking tasks by 10.8 points
Reduced the teacher's correction time by 31.6%

Conclusion

AI-driven textbooks can deliver personalized learning paths, richer exercise materials, and traceable classroom data while maintaining curriculum stability.

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*Auto-collected on 2026-09-06*