Teacher-regulated generative AI support, student agency, and perceived learning gains in higher education: the...

Generative artificial intelligence is increasingly used in higher education, yet its educational value depends not only on technological access but also on how its use is pedagogically regulated. This study examined how teacher-regulated generative AI support is associated with university students' perceived learning gains in higher education. It further tested whether student agency mediates this association and whether perceived fairness conditions the strength of the association between teacher-regulated generative AI support and student agency.

We adopted a cross-sectional quantitative survey design and collected data from 428 university students from six universities in western China who had used generative AI in course-related learning tasks under teacher guidance and within teacher-defined expectations. Structural equation modeling was used to test the direct and indirect associations in the proposed model, and a manifest interaction analysis was used to examine the first-stage moderation effect of perceived fairness.

In the proposed model, teacher-regulated generative AI support was positively associated with perceived learning gains both directly and indirectly through student agency. Student agency was statistically consistent with a mediated pathway linking teacher-regulated generative AI support to perceived learning gains. In addition, perceived fairness was associated with a stronger positive relationship between teacher-regulated generative AI support and student agency.

These findings suggest that students reported greater learning benefits when generative AI use was guided more clearly by teachers, taken up more actively by learners, and experienced in a fairer learning context. The study therefore highlights student agency as one plausible pathway linking teacher-regulated AI use to perceived learning gains in higher education.