Empirical Assessment of AI-Powered Tools for Vocabulary Acquisition in EFL Instruction

Abstract

The deep integration of Artificial Intelligence (AI) is gradually becoming a key force in innovating the teaching of English as a Foreign Language (EFL). This study aims to assess the practical effects of AI technology in providing customized instructional support and learning pathways in EFL instruction. The study reveals the benefits of AI in the instruction of English vocabulary, utilizing the Apriori algorithm from association rule mining and empirical analysis from survey data of 110 second-year university students across four different majors using AI-powered language learning platforms and AI-powered mobile language learning applications (such as UNIPUS AIGC platform and iTEST, intelligent assessment mobile application). It also deduces related teaching strategies and learning models. The results indicate that the use of AI-powered language learning platforms positively impacts English vocabulary learning outcomes in EFL instruction, and the combined use of AI-powered mobile language learning applications for self-testing and in-class tests effectively enhances vocabulary learning efficiency. The findings and conclusions of this study provide valuable insights for EFL educational practice and demonstrate the potential of AI in boosting the effectiveness of language learning, offering empirical support and guidance for future educational decision-making.