Adaptive Learning Driven by Web Technologies: A Model-Driven Development Framework for AI Enhanced English Intelligent Tutoring Web Applications
TL;DR
A Model-Driven Development framework that integrates Web technology and artificial intelligence (Web-AI-I), and builds a lightweight, cross-device English intelligent teaching application through the Web native technology stack to solve the coupling problem between teaching logic and Web application architecture through MDD mode is proposed.
Abstract
There are some problems in the existing English teaching network applications, such as low personalized adaptability, outdated teaching strategies and weak multimodal interaction, limited teaching paths, single feedback mechanism, weak adaptability of learners, low learning efficiency and low retention rate. In order to solve these problems, this paper proposes a Model-Driven Development (MDD) framework that integrates Web technology and artificial intelligence (Web-AI-I), and builds a lightweight, cross-device English intelligent teaching application through the Web native technology stack. Multi-modal data perception, AI adaptive decision-making and real-time feedback optimization are integrated to solve the coupling problem between teaching logic and Web application architecture through MDD mode. Through the experiment, the response time of learning path adaptation can be effectively shortened to 0.2 s, the English vocabulary mastery rate is 52% higher than that of traditional Web teaching applications, the grammar error correction accuracy rate is 91.8%, the average resource occupancy rate in cross-device scenarios is only 18.3%, and the MTA is 412, which is helpful to realize the large-scale landing of English intelligent teaching Web applications.