Artificial intelligence-assisted personalized language learning: systematic review and co-citation analysis

Abstract

Artificial intelligence (AI) for personalized learning has attracted increasing attention in various educational contexts and domains, including language learning. This study systematically reviewed academic studies on AI-assisted personalized language learning (PLL) from the perspectives of article trends, top journals, countries/regions and institutions, AI technology types, learning outcomes and supports, participants, scientific collaborations, and co-citation relations. Results indicated Taiwanese institutions' predominance in the field and the prevalent use of intelligent tutoring systems, natural language processing, and artificial neural network in facilitating personalized diagnosis and learning path and material recommendations in language learning. Furthermore, students' improved language outcomes and positive perception, satisfaction, or motivation towards language learning and AI technologies were commonly reported. The co-authorship analysis results indicated the close inter-regional collaborations, while the cross-regional collaborations are expected to be enhanced. The co-citation network analysis results highlighted the significance of fuzzy systems and item response theory. Additionally, learner profiling mining and learning resource adaptation were important directions to realize mobile- and web-based PLL.