Yuting Chen, Morris Siu–Yung Jong, Michael Yi‐Chao Jiang, M Li
Generative artificial intelligence (GAI) has gained increasing attention in English as a foreign language (EFL) education, with growing evidence supporting its efficacy in enhancing oral performance. Nonetheless, limited research has examined how GAI’s multimodal capabilities shape learner behavior, and how different learning behavior clusters relate to motivation and oral performance dynamics. To address these gaps, this study explored (1) the learning behavior clusters emerging from learners’ interactions with the Multimodal GAI (MGAI), and (2) the relationship between these clusters, and associated changes in motivation and oral performance. From a three-week intervention with 60 EFL learners, data were collected on behavior, motivation and performance. K-means clustering identified four distinct learners’ behavior clusters: interaction-based taskers, comfort-oriented balancers, output-focused monitors, and resource-driven strategists. We found significant differences across clusters. Resource-driven strategists and output-focused monitors showed greater positive changes in intrinsic motivation, while interaction-based taskers exhibited greater positive changes in extrinsic motivation. In oral performance, resource-driven strategists and output-focused monitors exhibited greater positive changes than both comfort-oriented balancers and interaction-based taskers in terms of fluency and coherence. Qualitative insights (i.e., interview and dialogue data) in each cluster provided illustrative information for these quantitative results. These findings provide pedagogical insights for the integration of MGAI into language learning contexts, highlighting that multimodal support alone does not automatically lead to positive learning changes. Thus, pedagogically guided use of multimodal support is needed to help learners move beyond surface-level MGAI-human interaction and more effectively appropriate MGAI tools to support scaffolded oral learning.