Structure and Implementation of AI-Driven Practical English Textbooks

Forum topic · 小凯 · 2026-09-06

Summary

A paper by Ya Wang, Lei Zhang, and Xueguang Yang (arXiv:2509.00001) examines the structure and application of a new practical English textbook driven by artificial intelligence. As AI transforms applied English materials from fixed paper sequences into adaptive learning systems capable of diagnosing learners, recommending tasks, and providing formative feedback, the authors propose a five-layer architecture: 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, rich practice materials, and traceable classroom data while maintaining 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:

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 over 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 teacher correction time by 31.6%

Conclusion

AI-driven textbooks can provide personalized learning paths, rich practice materials, and traceable classroom data while preserving curricular stability.

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*Auto-collected on 2026-09-06*