Actionable companion · Self-regulation & metacognition
From AI evidence to responsible practice: Artificial Intelligence Literacy and the Responsible Use of Generative AI Among University Students in Peru: The Mediating Role of Self-Regulated Learning and the Moderating Effect of Technostress
AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not findings 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 participants, intervention and comparator before applying this study in a new setting.
Original authors’ abstract, reproduced under the linked source license:
University students’ adoption of generative artificial intelligence has become nearly universal, yet institutional guidance has not kept pace, leaving responsible use dependent on students’ own self-regulation. Drawing on social cognitive theory, this study examined whether self-regulated learning accounts for the association between AI literacy and self-reported responsible use of generative AI, and how technostress relates to that association. A cross-sectional survey of 820 Peruvian undergraduates from public and private universities was analyzed using covariance-based structural equation modeling; a two-stage moderated mediation model with latent interactions was estimated using latent moderated structural equations. Measurement invariance held across gender and university type, and the available diagnostics did not indicate a dominant common-method factor. AI literacy predicted self-regulated learning (β = 0.626) and responsible use (β = 0.243), and self-regulated learning predicted responsible use (β = 0.445). The indirect effect was significant, β = 0.278, 95% CI [0.226, 0.337], accounting for 53.4% of the total effect. Technostress showed no significant moderation of the first stage or the direct path, but the second-stage path was significantly weaker at higher levels of technostress (b2 = −0.211, p < 0.001): the conditional indirect effect was 0.344 at low and 0.111 at high technostress and was not statistically distinguishable from zero above the estimated region-of-significance boundary of +1.40 SD. Technostress was not associated with weaker self-regulation, but with a weaker association between self-regulation and its behavioural expression. Because the design is cross-sectional, all estimates are reported as associations.
What kind of evidence is this?
Supporting AI learning science · Survey / observational study
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 study adopted a quantitative approach with a non-experimental, cross-sectional design aimed at explanatory analysis. The research question driving this study is not descriptive. The aim was not to determine the level of artificial intelligence literacy among Peruvian students, but rather to estimate the magnitude and direction of a hypothesised mechanism linking that literacy to the responsible use of generative AI through self-regulated learning, and to determine at which levels of technostress that association is weaker. A question formulated in terms of process and conditions requires a model that estimates several equations simultaneously and separates true variance from measurement error. Because the data were collected at a single point in time, every coefficient reported below is an association consistent with the hypothesised process, not an estimate of a causal effect. Mediation and moderation are used here as statistical specifications, and the terms mediator, indirect effect and conditional effect carry no temporal or causal claim; the design-based limits of cross-sectional mediation are set out in Section 4.7. A series of independent regressions does not achieve this.
Limitations and uncertainty · source passage
The cross-sectional design precludes making causal claims; in particular, estimating mediation based on data from a single time point may skew the coefficients compared to those that would be obtained with longitudinal data, in directions and magnitudes that are not always predictable (Maxwell & Cole, 2007; Maxwell et al., 2011). The reported relationships should be interpreted as structural associations consistent with the model, not as established temporal sequences. This qualification applies to the mediated and the conditional estimates alike: a cross-sectional indirect effect is not evidence that self-regulation intervenes between literacy and behaviour over time, and a cross-sectional conditional indirect effect is not evidence that technostress alters that process as it unfolds. Both are associations whose magnitude depends on the level of a third variable measured simultaneously. A relevant future line of research would be a repeated-measures design that estimates mediation with a time lag between literacy, self-regulation, and behavior. The design is also compatible with directions of association other than the one modelled. Students with stronger self-regulatory habits may become more AI literate rather than AI literacy, giving rise to self-regulation, and students who already verify and disclose may develop both competences as a consequence of that practice. Nothing in these data adjudicates between the modelled ordering and its alternatives. The model is offered as one specification consistent with social cognitive theory, and the ordering it assumes remains an assumption of the specification rather than a finding of the study.
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.
- Use plan–attempt–check–reflect prompts and compare confidence with actual performance. Keep learner choice and reflection visible.
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
Sandra Lizzette León Luyo; Heyner Yuliano Marquez-Yauri; Christian David Corrales Otazú; Sarita Jessica Apaza Miranda; César Pol Arévalo-Aranda; Marcos Marcelo Flores Castillo; Luisa Angélica Orejuela Guerrero; César Augusto Guzmán Valle; Marco Agustín Arbulú Ballesteros; Ana Elizabeth Paredes Morales. Artificial Intelligence Literacy and the Responsible Use of Generative AI Among University Students in Peru: The Mediating Role of Self-Regulated Learning and the Moderating Effect of Technostress. European Journal of Investigation in Health, Psychology and Education (2026-09-17). https://doi.org/10.3390/ejihpe16090143
Companion prepared 2026-11-01. 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.