Artificial intelligence in educational technology and transformative approaches to English language using fuzzy framework with CRITIC-TOPSIS method - Jingdan Liu; Xujie Bao; Liji Chen (Scientific Reports)
Abstract Artificial intelligence (AI) is transforming educational technology by enabling personalized, adaptive, and data-driven learning experiences. Machine learning algorithms analyze student performance to tailor content delivery, while natural language processing facilitates interactive learning through voice assistants and essay evaluation. This article presents the potential of criteria importance through the inter-criteria correlation (ICCR) method, which is used to determine objective weights under theoretical concepts of standard deviation and coefficient correlation techniques. Furthermore, another approach using the tool for order preference by similarity to the ideal solution (TOPSIS) method is discussed to investigate the ranking of preferences under various criteria and experts’ opinions within the system of the q-rung orthopair fuzzy framework. To reveal the validation and superiority, a numerical example is discussed to evaluate an effective AI approach to improve English language and psychology pedagogy under different criteria. Furthermore, a comprehensive comparative study is conducted to assess the compatibility of the proposed optimization techniques in the CRITIC-TOPSIS method with existing optimization approaches. Finally, the findings and contributions, along with future directions, are discussed in the conclusion. Additionally, we can apply the discussed decision-making methodologies to resolve complex real-life applications, such as renewable energy, medical diagnosis, multi-robotic systems, social selections, and computational and environmental sciences.