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TRUVACE RECORD VERSION
record: TRV-2026-2206
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-23T06:45:51.540829Z
status: published
lens: g_space
sector: education
headline: Within-person reciprocal processes between AI feedback literacy and agentic engagement: a three-wave random-intercept cross-lagged panel study
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: deviations in AI feedback literacy predicted later deviations in agentic engagement ( β = 0.222, p < 0.001)
problem_reading: (none)
problem_evidence: (none)
quick_read: 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.
limitation: The cultural interpretation is presented only as a possibility, and the sample covers Chinese undergraduates over a single semester, with some participants lost to incomplete data, so generalization is untested.
tag: Evidence-backed gain
key_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.
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.
sources:
- peer_reviewed | Frontiers in Psychology | https://doi.org/10.3389/fpsyg.2026.1893874 | 2026-10-22
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