Generative AI (GenAI) is rapidly transforming creative fields, raising critical questions about its impact on artistic identity and authorship, which also influence art education. While studies have explored GenAI adoption, the diversity of art students’ subjective perspectives on collaborating with these tools remains under-examined. This study uses Q methodology, a mixed-methods approach to investigating subjectivity, to explore the distinct perspectives art students hold regarding their creative identity when collaborating with GenAI. We conducted a Q study with 40 university students from various art-related disciplines (e.g., visual arts, design, music, animation) across different academic levels. Participants sorted 42 statements reflecting diverse viewpoints on GenAI collaboration, authorship, ethics, and creative control. By-person factor analysis revealed five distinct perspectives: (1) Human-centered directors, who treat GenAI as a tool under strong human intention; (2) Authorship guardians, who resist AI co-authorship and prioritise authenticity and ethical boundaries; (3) Conflicted co-creators, who use GenAI but struggle with ownership, disclosure, and earned pride; (4) Pragmatic integrators, who adopt GenAI for workflow efficiency while keeping craft expertise central; and (5) Enthusiastic explorers, who embrace AI’s creative possibilities, open to co-agency and novel expression. These findings offer a nuanced typology of student viewpoints, moving beyond simple acceptance or rejection. We further propose a two-dimensional framework that maps these perspectives according to students’ openness to GenAI and their sharing of creative agency with it. This research contributes empirical insights into how art students negotiate creative identity with GenAI, provides a valuable framework for educators to develop tailored pedagogical strategies addressing diverse student needs and concerns, and offers implications for designing more human-centered GenAI tools for creative practice.

This exploratory qualitative study examines how students’ reasoning becomes observable and develops within Socratic dialogues with Riffbot, a large language model (LLM)-based learning chatbot designed to guide students from descriptive and clarificatory questioning toward explanatory and reflective inquiry. The empirical dataset comprised 121 dialogue transcripts generated by 93 students across seven project-based courses at a university of applied sciences. All 121 transcripts were initially reviewed to examine participation, dialogue length, and interactional characteristics. Of these, 72 dialogues containing at least 10 turns were selected for detailed coding and reflexive thematic analysis (RTA), while 49 shorter dialogues were retained as contextual material. The analysis examined student responses in relation to successive chatbot prompts, using theoretically informed reasoning categories to trace interactional patterns within complete dialogues. Coded data were visualized in a chronological matrix to support the interpretation of reasoning trajectories. Descriptive reasoning predominated, while some dialogues showed emerging explanatory and reflective reasoning. Other trajectories remained primarily descriptive, involved repeated clarification, or moved recursively between reasoning categories, often including moments of uncertainty and redirection. These findings highlight the non-linear and interactionally situated nature of learning expressed during Socratic LLM–student exchanges, without establishing independent learning gains. Methodologically, the study demonstrates how systematic coding, chronological visualization, and reflexive thematic interpretation can be combined to make thinking-in-action more visible and analytically traceable. It contributes an empirical account of student reasoning within AI-mediated dialogue and a practical, digitally supported approach to investigating learning-related processes in higher education.