Actionable companion · Self-regulation & metacognition

Learning with agency: Fostering engagement in the digital age: the mediating role of self-efficacy and self-regulation between enjoyment and learner engagement in AI-assisted EFL writing

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.

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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 Introduction The development of artificial intelligence (AI) has created new opportunities for AI-supported foreign language teaching and applications. This study investigates the interrelationships among foreign language enjoyment (FLE), writing self-efficacy, self-regulated learning (SRL) strategies, and learner engagement within the context of AI-assisted English as a foreign language (EFL) writing. Methods A cross-sectional survey design was employed, involving 535 Chinese university students with prior experience in AI-assisted writing. Participants completed adapted and validated scales measuring FLE, writing self-efficacy, SRL strategies, and learner engagement. Structural equation modeling (SEM) was used to test a hypothesized partial mediation model linking these constructs. Results The results supported a well-fitting partial mediation model. FLE was found to be a direct and significant positive factor of learner engagement. Furthermore, the analysis confirmed that self-efficacy and SRL strategies act as sequential mediators in the relationship between FLE and engagement. Specifically, FLE is positively associated with learners' self-efficacy, which in turn promotes the use of SRL strategies, ultimately leading to deeper cognitive, behavioral, emotional, and agentic engagement in the writing task. Discussion The study extends Control-Value Theory, Broaden-and-Build Theory, Social Cognitive Theory, and Self-Regulated Learning Theory to the novel domain of AI-assisted language learning, highlighting the critical affective and cognitive pathways that foster engaged learning. Pedagogical implications are discussed, emphasizing the importance of designing enjoyable, confidence-building, and strategy-rich AI-assisted writing environments. Keywords: AI-assisted writing, enjoyment, learner engagement, self-efficacy, self-regulated learning strategies

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

This study employed a convenience sampling strategy to recruit participants from universities in China. Recruitment announcements were disseminated via popular social media platforms (WeChat, QQ, and RedNote) between January and March 2026. To ensure the relevance of the data to the research focus on AI-assisted writing, specific inclusion criteria were established: (1) current enrollment in a university-level English course; (2) completion of at least six years of formal English education; and (3) demonstrable prior experience in using AI tools (e.g., ChatGPT, Doubao) for English writing tasks.

Limitations and uncertainty · source passage

Three limitations should be acknowledged. First, due to its cross-sectional design, this study cannot establish causal relationships among the examined variables. Future research could adopt longitudinal or experimental designs to better infer causality. Second, the sample was exclusively drawn from university students in mainland China, which constrains the cross-cultural applicability of the findings. Subsequent investigations could expand the geographical and cultural coverage, thereby strengthening the generalizability of the proposed model. Third, the sample was drawn primarily from undergraduate students at a single key university in China, which limits the generalizability of the findings. Due to potential differences in cultural attitudes toward artificial intelligence, educational policies, and language learning experiences across diverse populations, the relationships among variables may vary. Therefore, future research should include more diverse populations from different geographical regions and educational stages—such as students from other regions and countries, as well as those at different educational levels, such as primary and secondary school students, graduate students, and vocational college students. Overall, future research should adopt longitudinal or experimental designs to verify causality and dynamic changes over time, include diverse cultural samples to test the stability of the model across different regions and educational contexts, use mixed methods that combine qualitative inquiry and objective behavioral data to improve measurement validity, and conduct in-depth analyses of AI tool types and usage patterns to explore their differential impacts on learner 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.

Use plan–attempt–check–reflect prompts and compare confidence with actual performance. Keep learner choice and reflection visible.
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

Xiaojing Li; Tao Wang; Wei Wang; Lei Liu. Fostering engagement in the digital age: the mediating role of self-efficacy and self-regulation between enjoyment and learner engagement in AI-assisted EFL writing. Frontiers in Psychology (2026-06-15). https://doi.org/10.3389/fpsyg.2026.1848701

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.