Impact of Artificial Intelligence Tools on Learning Motivation in University EMI Courses: A Network Meta-Analysis

English-Medium Instruction (EMI) programs have proliferated across global higher education, which create motivational obstacles for English as a Foreign Language (EFL) learners. These students frequently struggle with language barriers that hinder their engagement and self-confidence. Meanwhile, Artificial Intelligence (AI) technologies present innovative solutions, delivering customized and dynamic assistance to overcome such problems. To evaluate their impact, this research executed a network meta-analysis (NMA) on 15 empirical investigations, involving 1,847 university students in EMI settings. The analysis pitted three AI tools (i.e., Generative AI Chatbots, AI Writing Assistants, and AI Language Learning Applications) against conventional teaching methods to measure gains in learning motivation. Researchers employed standardized mean differences via Hedges' g for effect quantification, incorporating checks for heterogeneity and publication bias to ensure reliability. Findings demonstrated that every AI intervention markedly surpassed traditional approaches. Among them, AI Language Learning Applications achieved the strongest outcome at g  = 0.907. Generative AI Chatbots trailed slightly behind with g  = 0.892, while AI Writing Assistants registered a solid g  = 0.692. Innovatively, the study uncovered how the responsive and flexible elements of chatbots and applications adeptly meet learners' needs for autonomy and competence, drawing from self-determination theory and promoting enduring motivation. Furthermore, subgroup evaluations showed no influence from variables like program length or student skill levels and implied widespread utility. Consequently, these insights furnish practical advice for embedding adaptive AI in EMI syllabi to heighten involvement and academic performance They also advocate for subsequent studies examining longitudinal effects and adaptations across cultures.