Motivation and Satisfaction in English-Medium Instruction: The Role of GenAI in Adult Learning

This study explores how Generative Artificial Intelligence (GenAI) supports motivation and satisfaction among adult learners in an English Medium Instruction (EMI) course in a non-English-dominant context. EMI environments present linguistic and cognitive challenges, particularly for adults with limited prior exposure to academic English, which can hinder engagement and learning outcomes. The study investigates how satisfaction, engagement, and other factors, including the integration of GenAI tools, shape adult learners’ motivation in an EMI program. A mixed-methods convergent design was employed, with quantitative data analyzed using moderated mediation models grounded in Self-Determination Theory and qualitative insights drawn from learner diaries. Findings show that satisfaction strongly predicts motivation, fully mediating the effects of engagement and positive attitudes toward EMI. Qualitative data highlight three contributors to motivation: emotional support from instructors and peers, an L1-dominant interactive lecture model, and GenAI tools addressing language-related difficulties. The study underscores the importance of satisfaction, emotional scaffolding, and technological support in EMI.