AI Technology Readiness, Trust, and Supervisor Academic Support in Relation to Graduate Students’ Research Self-Efficacy

Research self-efficacy (RSE) is critical for graduate students. However, how emerging AI technologies shape RSE remains unclear. Grounded in the Technology Readiness Index (TRI) and Social Cognitive Theory, this study examined how AI technology readiness, AI trust, and supervisor academic support are associated with RSE. The study also examined whether these associations differ across disciplinary groupings. Cross-sectional data were collected from 425 master’s students at 19 Chinese universities. All participants self-reported regular use of AI in their research activities. The data were analyzed using partial least squares structural equation modeling (PLS-SEM) with multigroup analysis. Readiness facilitators were positively associated with AI trust and with perceived supervisor academic support. Readiness inhibitors were also positively associated with AI trust. Supervisor academic support was significantly associated with RSE. The direct association between readiness and RSE was not statistically significant. However, statistically significant indirect associations were observed via supervisor academic support and AI trust. Importantly, partial measurement invariance was established across groups. No between-group differences in path coefficients reached statistical significance. This indicates structural equivalence across the humanities and social sciences grouping and the science, engineering, and medicine grouping. The findings offer a preliminary extension of the TRI from adoption outcomes to efficacy beliefs in research training. They also inform tentative, discipline-uniform strategies for AI literacy education and supervisory support among master’s students who regularly use AI for research purposes.