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

A new artificial intelligence-driven practical English textbook architecture, proposed in an arXiv paper (arXiv:2609.02981v1), increased unit completion accuracy from 72.4% to 84.9%, raised average speaking task scores by 10.8 points, and reduced teacher correction time by 31.6% in an eight-week test with 186 non-English-major undergraduates. The five-layer system—knowledge mapping, learner profiling, task generation, feedback orchestration, and teacher-side governance—outperformed a static digital textbook while maintaining curriculum stability.

arXiv:2609.02981v1 Announce Type: new 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%. Therefore, an AI-driven textbook can maintain the stability of the curriculum while providing personalised learning paths, rich practice materials and traceable classroom data.