From AI dependence to reflective collaboration: psychological ownership, competency anxiety, and perceived support in AI-assisted learning.

Generative artificial intelligence (GenAI) can improve efficiency in academic work. However, it also raises a central psychological question: when AI helps produce a task, why do some learners still experience the final work as their own, whereas others experience it as polished but psychologically distant? This study examines psychological ownership and competency anxiety in AI-assisted learning, distinguishing between dependent AI outsourcing and reflective human-AI collaboration. An exploratory qualitative interview-based design with supplementary background information was used. The background form was used only for sample description and interview preparation, followed by in-depth interviews with 50 undergraduate and postgraduate students from five Chinese universities, interviews with 10 faculty members and administrators, and 128 student critical incident records. Data were analyzed through a hybrid deductive-inductive thematic analysis, with NVivo 14 used as a supporting tool for code management and matrix comparison. In participants' accounts, dependent AI outsourcing was associated with weaker psychological ownership, described in terms of reduced cognitive and authorial presence during planning, reasoning, and revision. This weakened sense of ownership was, in turn, linked to shallow memory, difficulty explaining an individual's work, reduced meaning-making, and stronger competency anxiety. Reflective human-AI collaboration, by contrast, was associated with retained ownership when students planned before using GenAI, evaluated AI output, revised it in their own voice, and could explain their final decisions. Perceived educational support, including feedback, clear AI-use guidance, and psychologically safe learning environments, was described as helping students treat AI use as a learnable practice rather than a hidden shortcut. Faculty and administrators also framed these patterns as issues of assessment feasibility, workload, policy clarity, and curriculum design, rather than solely as student choice. Because the data were collected at five Chinese universities, the proposed model is presented as context-sensitive: the observed dynamics may be amplified where assessment is strongly product-oriented, formative feedback is constrained, and AI use is difficult to discuss openly. This study contributes to psychological ownership theory by extending it to human-AI interaction and proposing that, in this setting, ownership depends on cognitive and authorial presence rather than solely on task completion.