This study investigates how learners interpret and engage with AI-supported feedback in second language (L2) learning, with a focus on Thai as a foreign language (TFL). As artificial intelligence becomes increasingly integrated into language education, understanding how learners respond to AI-generated feedback is critical for informing teaching and learning practices. Using Q methodology, this study explores the subjective perspectives of 40 Chinese university students learning Thai in a less commonly taught language (LCTL) context. The analysis identified four distinct learner typologies: Human-Centered Traditionalists, Optimistic Tech-Adopters, Guided Personalization Seekers, and Cautious Pragmatists. These typologies reflect systematic variation in how learners evaluate AI-supported feedback, teacher engagement, and opportunities for communicative practice. While some learners value AI as a tool for extending autonomous learning and improving writing and grammar, others emphasize the importance of teacher-mediated interaction for interpreting meaning, developing communicative competence, and addressing culturally embedded language use. The findings indicate that AI-supported feedback is not uniformly accepted but is interpreted in relation to learners’ expectations of instruction, feedback quality, and communicative relevance. From a pedagogical perspective, the study highlights the need to integrate AI as a complementary resource that supports teacher guidance. It further suggests that effective AI integration in L2 classrooms requires a context-sensitive design, learner training in feedback use, and alignment with communicative learning goals. This study can contribute to research on technology-enhanced language learning by providing a learner-centered account of how AI-supported feedback is experienced in L2 contexts and by offering practical implications for classroom implementation and instructional design.