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

The study presented on arXiv examines how artificial intelligence is reshaping practical English textbooks, moving from static printed materials to adaptive learning systems. The authors propose a five‑level architecture that includes creating a knowledge map, profiling students, automatically generating activities, coordinating feedback, and a management module for teachers. To validate the model, a prototype was deployed over eight weeks with 186 university students who were not majoring in English. Results showed a significant improvement in unit completion, rising from 72.4 % to 84.9 %, and a 10.8‑point increase in the average score on oral expression tasks. Additionally, the time teachers spent grading work dropped by 31.6 %. These findings suggest that an AI‑driven textbook can maintain curricular coherence while offering personalized learning paths, abundant practice materials, and traceable classroom data. The significance lies in the technology’s ability to optimize language instruction, enhance teaching efficiency, and tailor instruction to individual student needs, potentially accelerating the acquisition of communicative competencies across broader educational settings.

Read the original article on arXiv cs.AI

This summary is an informational synthesis produced by dataqbs.com. All rights to the original content belong to its author and the cited media outlet. We act solely as curators of technology news and claim no authorship.