Within-person reciprocal processes between AI feedback literacy and agentic engagement: a three-wave random-intercept cross-lagged panel study

Artificial intelligence (AI) feedback has become a pervasive feature of university learning, yet whether students’ capacity to engage productively with such feedback co-develops with their broader learner agency remains poorly understood. Drawing on student feedback literacy theory, self-determination theory as a supplementary motivational lens, and the mutualism principle, this study tested whether AI feedback literacy and agentic engagement reciprocally predict each other over time at the within-person level a…

By the Truvace deskOctober 23, 20262 min read1 sourcePeer-reviewedTRV-2026-2206· cite & verify

In brief

Researchers followed 480 Chinese undergraduates across one semester, measuring them three times at six-week intervals, with 399 providing complete data. They tracked how well students could engage with AI-generated feedback and how actively they engaged in their own learning.

The results suggest AI feedback literacy is better seen as a developing resource than a fixed skill, since it grows alongside students' engagement. What remains unclear is whether the pattern holds beyond this single sample, a single semester, and the cultural setting studied.

Main points

Students who scored higher on AI feedback literacy across the semester also scored higher on agentic engagement, with a trait-level correlation of r = 0.315.
Within individuals, both cross-lagged pathways were positive and significant, at β = 0.222 and β = 0.263.
A Wald test found the two pathways did not differ significantly in size (ΔB = 0.002, p = 0.972), consistent with symmetric mutualism.
The random-intercept model fit substantially better than a traditional cross-lagged model and revealed larger within-person effects.

The gain

Among Chinese undergraduates, higher AI feedback literacy at one point predicted later increases in agentic engagement, and agentic engagement likewise predicted later gains in AI feedback literacy, indicating a mutually reinforcing development.

The rundown

The study used a three-wave panel design with measurements every six weeks during one semester. Of 480 Chinese undergraduates recruited, 399 had complete data across all waves, and the data were analyzed with a random-intercept cross-lagged panel model.

The model separates stable differences between students from changes within the same student over time. Once the stable trait-level covariation was isolated, the within-person reciprocal effects came out substantially larger than in a traditional cross-lagged model.

The authors draw on student feedback literacy theory, self-determination theory and the mutualism principle. They raise the possibility that, in a Confucian Heritage Culture context where agentic engagement has historically been culturally muted, AI feedback may offer a less culturally loaded venue for students to exercise agency.