Work

Optimizing AI-Driven EdTech Solutions for Enhanced English Learning: A TOPSIS Approach

Year: 2025

Type: article

Abstract: The development of educational technology (EdTech) and artificial intelligence (AI) brings about a revolution in English learning by providing flexible, effective, and customized solutions. The purpose of this study is to assess the impact of AI and EdTec on education. In this article, we defined the multi-criteria decision-making (MCDM) procedure to manage ambiguity and awkward information by integrating the Technique for Order of Preference by Similarity to the Ideal Solution (Topsis) method with Circular q-Rung orthopair fuzzy set (Cq-ROFS), and Bonferroni mean (BM) operators to evaluate and prioritize AI-driven EdTech tools. The methodology incorporates multiple attributes, such as adaptability, learner engagement, cost-effectiveness, and scalability, within an MCDM framework. These results highlight the huge potential of intelligent teaching programs, flexible learning environments, and AI-powered language models to improve English ability. This research demonstrates how AI has advanced in education from simple computer-assisted language learning to complex AI-driven platforms like chatbots and intelligent systems for teaching. These developments, such as automated grading and feedback, have given teachers the ability to improve administrative effectiveness and instructional quality. Furthermore, customized and interactive learning experiences that are adapted to the needs and preferences of each student have been made possible by AI-based EdTech solutions. The study used the TOPSIS technique to rank important criteria for optimizing various solutions, highlighting their contribution to preservation, interaction, and overall efficacy in English language learning.

Source: IEEE Access

DOI: 10.1109/access.2025.3561694

Authors: Liu Yang, Lu Li, Li Wang

Institutions: Northwest University, Xi'an International Studies University

Language: English

Topic: Artificial Intelligence in Education

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $2,075