AI-Driven Practical English Textbooks Show Gains in an Eight-Week Prototype Study

Summary

Artificial intelligence is shifting applied English materials from fixed paper sequences toward adaptive learning systems that diagnose learners, recommend tasks, and provide formative feedback. This paper proposes a five-layer architecture for an AI-driven practical English textbook: knowledge mapping, learner profiling, task generation, feedback orchestration, and teacher-side governance. A prototype was used for eight weeks with 186 non-English-major undergraduates and compared with a static digital textbook. Unit completion accuracy increased from 72.4% to 84.9%, while average speaking-task scores rose by 10.8 points. The system also reduced teachers’ correction time by 31.6%. The authors conclude that AI-driven textbooks can preserve curriculum stability while supporting personalized learning paths, richer practice materials, and traceable classroom data.