Impact of Artificial intelligence tools on learning motivation in English instruction: a network meta-analysis
TL;DR
Results indicate that all AI tools significantly outperform traditional instruction and suggest that interactive and adaptive AI tools are particularly effective in fostering motivation by fulfilling students' basic psychological needs of autonomy and competence.
Abstract
The rapid expansion of English-Medium Instruction (EMI) programs and the growing demand for English language proficiency have introduced significant motivational challenges for EFL learners who face linguistic barriers. Artificial intelligence (AI) tools offer a potential solution by providing personalized scaffolding and interactive support. This study conducts a network meta-analysis (NMA) of 16 empirical studies (N = 1,923) to compare the effectiveness of three AI interventions, including Generative AI Chatbots, AI Writing Assistants, and AI Language Learning Applications, on the learning motivation of K-12 and university EFL students in English instruction contexts. Results indicate that all AI tools significantly outperform traditional instruction. AI Language Learning Applications (g = 0.907, k = 2) showed the largest effect, followed by Generative AI Chatbots (g = 0.824, k = 11) and AI Writing Assistants (g = 0.692, k = 3). These findings suggest that interactive and adaptive AI tools are particularly effective in fostering motivation by fulfilling students' basic psychological needs of autonomy and competence. The study provides evidence-based recommendations for the strategic integration of AI into English instruction curricula to optimize student engagement and academic success.