The rapid expansion of generative artificial intelligence (AI) in language education has created new possibilities for personalized and adaptive learning, while raising questions about how learners from different linguistic and educational backgrounds interact with AI systems. This exploratory mixed-methods study examines culturally interpretable patterns of AI-mediated language learning among 100 adult English language learners from Japan, China, Saudi Arabia, Egypt, North Macedonia, and Bulgaria across four CEFR levels. Data were collected over eight weeks through interaction logs, learner diaries, pre- and post-assessments, surveys, and semi-structured interviews. Quantitative indicators are used descriptively, while qualitative evidence provides the primary basis for interpretation. The findings suggest broadly positive language-development trajectories alongside substantial variation in how learners request, interpret, evaluate, and use AI-generated feedback. Four overlapping feedback-utilization patterns, calibration, negotiation, experimentation, and uptake, were identified, although their realization varied across learners and contexts. Narrative cases further indicate that interaction can develop from correction-focused use toward clarification, comparison, dialogue, and strategic engagement, while substantial within-group variation highlights the importance of individual agency. Because all participants interacted with the same AI system under uniform conditions, the observed differences reflect learner interpretation and adaptive use rather than system-level cultural customization. The study therefore treats generative AI as a culturally interpreted rather than inherently culturally responsive system and advances three design-oriented hypotheses concerning adaptive feedback calibration, interaction-style modulation, and learner-controlled customization. These hypotheses provide an exploratory foundation for future research examining whether culturally responsive learning agents can support more inclusive and meaningful AI-mediated language learning.