Engagement Profiles in AI-Assisted ESAP: Associations with AI Use Indicators and Speaking Performance

Generative AI (GenAI) offers opportunities for repeated practice and feedback in English language learning. However, how students participate in these learning activities requires further investigation. This study examined learning engagement profiles and their associations with GenAI use and speaking performance among 500 STEM master’s students in an AI-assisted ESAP speaking course at a Chinese university. Latent profile analysis used self-reported behavioral, emotional, and cognitive engagement scores collected in Week 15. Separate R3STEP models examined associations with self-reported weekly GenAI use frequency and average weekly online duration. BCH analyses compared posttest speaking performance across profiles while adjusting for pretest scores. Four profiles emerged: Low Engagement Profile (P1, 22.4%), High Engagement Profile (P2, 38.2%), Low Emotional Engagement Profile (P3, 24.0%), and Low Cognitive Engagement Profile (P4, 15.4%). Proportions were based on most likely assignments. After Holm correction, longer online duration was associated with higher odds of membership in P2, P3, and P4 relative to P1. More frequent GenAI use was associated with membership in P2 and P4 relative to P3. P2 had significantly higher baseline-adjusted overall speaking scores than all other profiles. These findings provide person-centered evidence of learner heterogeneity in AI-assisted ESAP. They highlight the value of considering multidimensional engagement configurations alongside GenAI use indicators when interpreting differences in speaking performance.