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Psychological empowerment and relational catalysis: a quasi-experimental study on graduate education evaluation
Introduction:
The rise of generative AI (e.g., ChatGPT, DeepSeek) challenges traditional graduate evaluation systems centered on individual theses and standardized examinations. Current institutional responses-task redesign and AI skills training-have shown limited effectiveness, as they overlook the psychological internalization process, teacher-student relational dynamics, and their synergistic effects.
Methods:
This study conducted a three-arm quasi-experiment with 155 doctoral students in education over one semester. Group A (traditional model) received no AI guidance; Group B received AI skills training only; Group C received comprehensive reform combining AI training, practical team-based projects, and humble mentor leadership training. Psychological empowerment was measured as a mediator, and humble mentor leadership as a moderator. Data were analyzed using regression with cluster-robust standard errors and bootstrap tests.
Results:
Group C scored significantly higher than Groups A and B on course satisfaction, professional competence, and psychological empowerment (all p < 0.001). Psychological empowerment showed a significant indirect association with learning outcomes (indirect effect = 0.22, 95% CI [0.12, 0.34]), with meaning and autonomy as the core dimensions. Humble mentor leadership significantly moderated the engagement-empowerment path within Group C (β = 0.24, p < 0.001), with the conditional indirect effect significant only under high humble leadership.
Discussion:
These findings suggest that the synergy of practical task redesign, psychological empowerment, and humble mentor leadership may be associated with positive student outcomes in AI-era educational reform. However, due to the cluster-assigned design with one cluster per condition and non-equivalent assessment tasks across groups, all results should be interpreted as exploratory and hypothesis-generating rather than causal. Future research should employ multi-cluster or individually randomized designs with common assessment tasks to permit valid causal inference.
