Actionable companion · Motivation & engagement
Learning with agency: The impact of artificial intelligence literacy on learning engagement among university students: the mediating role of psychological capital and the moderating role of professional identity
AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.
Evidence and context
Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Verify the participant population and task before transferring the findings.
Original authors’ abstract, reproduced under the linked source license:
The rapid proliferation of artificial intelligence (AI) in higher education has positioned AI literacy as an essential student competency, yet the mechanistic pathways linking this competency to academic outcomes remain insufficiently understood. This study investigates the direct and indirect effects of AI literacy on learning engagement, with a specific focus on the mediating role of psychological capital (PsyCap) and the moderating role of career commitment.Employing a stratified random sampling design, we conducted a cross-sectional questionnaire survey among 1,198 undergraduate students across four universities in Zhengzhou, Henan Province. Path analysis was performed using partial least squares structural equation modeling (PLS‑SEM), and bootstrap resampling was applied to test both mediation and moderated mediation effects.The results reveal that AI literacy exerts a direct, positive influence on learning engagement. Notably, psychological capital partially mediates this relationship, indicating that AI literacy not only directly drives engagement but also indirectly facilitates it by enhancing students’ self‑efficacy, hope, optimism, and resilience. Furthermore, career commitment moderates the effect of psychological capital on learning engagement: the indirect effect of AI literacy through psychological capital becomes stronger as students’ career commitment intensifies.Collectively, these findings delineate a resource‑transformation pathway—from AI literacy to psychological capital to learning engagement—and identify career commitment as a critical boundary condition. The results carry theoretical implications for understanding the interplay between digital competencies and positive psychological resources, and offer practical guidance for designing educational interventions that synergistically integrate AI training, psychological capital cultivation, and professional identity reinforcement in the era of AI‑enhanced education.
What kind of evidence is this?
Supporting AI learning science · Evidence synthesis
Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.
Study methods · source passage
The survey was conducted among undergraduate students from four universities in Zhengzhou, Henan Province, between February and April 2026. To ensure a representative sample across key demographic characteristics, a stratified random sampling method was employed. The stratification variables were gender (male vs. female), grade level (freshman, sophomore, junior, and senior), and student leader status (holding an official leadership position in student organizations vs. not holding such a position). These variables were selected as stratification criteria based on prior empirical evidence demonstrating significant differences in AI literacy, learning engagement, psychological capital, and professional commitment across these demographic groups (Luo et al., 2008; Cui, 2012; Wang, 2013; Xu et al., 2014). The target population was first divided into mutually exclusive strata based on the full combination of the three stratification variables, yielding 16 strata (2 × 4 × 2). Within each stratum, participants were then randomly selected using a simple random sampling procedure. To maintain the representativeness of the sample, the sample size allocated to each stratum was proportional to the stratum’s representation in the overall population (proportional allocation). The survey was administered using a mixed-mode approach. A total of 719 questionnaires were distributed online via the Wenjuanxing platform, and 681 were distributed offline as paper questionnaires. The proportion of online versus offline distribution was determined according to each stratum’s proportion in the total sample, thereby ensuring that the mixed-mode administration did not introduce bias in the representation of different demographic groups. After excluding invalid responses (e.g., those with excessive missing data, patterned responding, or completion times below two standard deviations of the mean), 1,198 valid questionnaires were retained, yielding an effective response rate of 85.6% Jacob (n.d.).
Limitations and uncertainty · source passage
This study has several limitations. First, the cross-sectional design does not allow for strict causal inferences regarding the directional relationships among the variables. Although the hypothesized model is theoretically grounded and empirically supported by the data, alternative causal directions cannot be entirely ruled out. For instance, it is plausible that students with higher learning engagement may be more motivated to develop their AI literacy or that reciprocal relationships exist among the constructs. Future research should adopt longitudinal designs or experimental interventions—such as AI literacy training programs with pretest–posttest assessments—to further examine causal directions and provide stronger evidence for the temporal precedence of AI literacy over psychological capital and learning engagement.
Design the system before the pilot
Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.
- Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
A 90-day district pilot
- Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
- Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
- Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
- Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.
The board decision brief
Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.
Attribution and reuse
Ning Wang. The impact of artificial intelligence literacy on learning engagement among university students: the mediating role of psychological capital and the moderating role of professional identity. Frontiers in Psychology (2026-09-11). https://doi.org/10.3389/fpsyg.2026.1892204
Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.