A study on vocational education students' cognition of human-ai co-creativity: An empirical analysis based on Q methodology

Based on Actor-Network Theory (ANT), this study adopts Q methodology to conduct an empirical exploration of 24 vocational education students, aiming to reveal the internal structure of their cognitive landscape. The experimental results confirm a prominent Middle Collapse phenomenon: the ideal "symmetric co-creative intentionality" is statistically completely absent, and the student group does not form a continuously progressive cognitive spectrum, but presents a drastic structural polarization. The data extract five typical factors, which are clearly divided into two camps: "defensive polarization" (rejecting the status of AI actants to defend subjectivity) and "dependent polarization" (transferring agency to technological black boxes or macro-environments). Topological analysis further shows that there are deep semantic ruptures between different cognitive groups, indicating systemic blockages in the problematization and interessement stages during the "translation" process. The conclusion of this study points out that the current human-AI collaborative cognition of vocational education students does not naturally move toward symbiosis, but is driven by skill-oriented pressure and employment anxiety, falling into an either-or "translation dilemma". This finding reveals the deep cognitive fractures that vocational education faces in cultivating “responsible co-creators” in the intelligent era, and provides key empirical evidence for reconstructing the educational paradigm of human-AI collaboration.