Actionable companion · Self-regulation & metacognition
Learning with agency: Exploring How AI Literacy and Self-Regulated Learning Relate to Student Writing Performance and Well-Being in Generative AI-Supported Higher Education
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:
Abstract The integration of generative artificial intelligence (GAI) into higher education is transforming students’ learning processes, academic performance, and psychological well-being. Despite the increasing adoption of GAI tools, the mechanisms through which students’ AI literacy and self-regulated learning (SRL) relate to their academic and emotional experiences remain underexplored. This study investigates how AI literacy and SRL are associated with writing performance and digital well-being among university students in GAI-supported higher learning contexts. A survey was administered to 257 students from universities in China, and structural equation modeling was used to examine the hypothesized relationships. Results show that both AI literacy and SRL significantly and positively predict students’ writing performance, with SRL having a stronger effect. Moreover, AI literacy shows a positive association with GAI-driven well-being, with writing performance serving as a partial mediator in this relationship. These findings suggest that fostering both technological competencies and effective learning strategies may support students’ academic outcomes while supporting their psychological well-being in AI-enriched educational environments. By integrating AI literacy and SRL into a unified model, this study contributes to the growing body of research on GAI-driven well-being in higher education and offers practical implications for cultivating balanced and sustainable learning experiences in the age of GAI. Keywords: generative artificial intelligence, AI literacy, self-regulated learning, well-being, writing performance, higher education
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
Participants in this study were undergraduate and graduate students from a high-level comprehensive university located in northern China. A total of 275 students initially participated in the survey. After excluding incomplete responses, 257 valid questionnaires were retained for analysis. Among the valid respondents, 89 (34.6%) were male and 168 (65.3%) were female. In terms of academic level, 12 participants (4.6%) were freshmen, 47 (18.2%) sophomores, 74 (28.7%) juniors, and 51 (19.8%) seniors. The sample also included 43 (16.7%) first-year graduate students, 24 (9.3%) second-year graduate students, and 6 (2.3%) third-year graduate students. In terms of nationality, 224 students (87.1%) were from China, while 33 (12.8%) were international students. Participants came from a variety of academic disciplines, including humanities and arts (29.6%), social sciences (50.6%), natural sciences (10.9%), and engineering sciences (8.9%), as shown in Table 1.
Limitations and uncertainty · source passage
In response to the growing integration of GAI into higher education, this study examined how AI literacy and SRL relate to students’ writing performance and GAI-driven digital well-being in academic writing contexts. Drawing upon a sample of 257 university students in China and employing SEM, this study identified the significant associations among AI literacy, SRL, writing performance, and GAI-driven well-being. Writing performance was found to function as a potential statistical mediator between learner competencies and well-being, suggesting a central role in the overall pattern of relationships in GAI-supported academic contexts.
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
Jiajia Shi; Weitong Liu; Ke Hu. Exploring How AI Literacy and Self-Regulated Learning Relate to Student Writing Performance and Well-Being in Generative AI-Supported Higher Education. Behavioral Sciences (2025-05-20). https://doi.org/10.3390/bs15050705
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.