This study explores advanced models for second language instruction within the artificial intelligence landscape, spotlighting the integration of mixed quantile regression and Bayesian inference to refine teaching strategies and bolster learning achievements. By adopting mixed quantile regression, this research constructs a model that surpasses traditional assumptions of normality, enabling the handling of complex, multilevel heterogeneous data. Bayesian inference was applied for parameter estimation, enhancing the precision and reliability of our findings. An empirical investigation involving 658 students from College M revealed an average adaptability score in second language learning of 3.663, with all dimensions scoring above 3—learning attitude ranking highest at 3.956. Key factors influencing learning capacity, including motivation, intellectual literacy, self-efficacy, and the availability of resources, demonstrated a positive correlation. These findings suggest the potential of mixed quantile regression and Bayesian inference in assessing learning adaptability and determinants, offering a novel approach to AI-supported second language education.