Agentic education · Learning science

Actionable companion · Feedback & scaffolding

Learning with agency: Student engagement and self-regulated learning across standard and AI-enhanced flipped learning: a within-subjects longitudinal study

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:

Introduction Although AI is increasingly being integrated into flipped classrooms, little is known about how student engagement and self-regulated learning (SRL) develop across extended implementations, particularly in underrepresented higher education contexts. This study examined developmental trajectories of student engagement and SRL across a sequential implementation comprising a standard flipped phase followed by a progressively scaffolded AI-enhanced phase, representing one of the earliest longitudinal investigations of AI-enhanced flipped learning in a Central Asian higher education context. Methods Using a within-subjects longitudinal design, 211 undergraduate students at a public university in Mongolia were followed across four measurement occasions (Weeks 1, 6, 11, and 15) within a 16-week course, with quantitative data collected across 15 weeks of instruction. Engagement and SRL were assessed across a standard flipped phase (Weeks 1–6) and a progressively scaffolded AI-enhanced phase (Weeks 7–15). Metacognitive self-regulation was examined as a potential mediator of the longitudinal relationship between agentic engagement and academic achievement using a cross-lagged panel model (Mplus 8.3, ML estimator, 1,000 bootstrap samples). Results Linear mixed-model analyses revealed significant trajectories for all engagement and SRL subscales (all p ≤ 0.004, η2p = 0.021–0.111). All six SRL subscales remained stable during the standard flipped phase before increasing significantly during the AI-enhanced phase. Agentic engagement demonstrated the strongest engagement trajectory (η2p = 0.103), whereas metacognitive self-regulation showed the largest SRL effect (η2p = 0.111), with the greatest gains observed during the W11-W15 interval. Cross-lagged analyses identified a significant longitudinal association between agentic engagement at Week 1 and metacognitive self-regulation at Week 6 (β = 0.187, p = 0.005), whereas the indirect effect on academic achievement was not significant (β = 0.002, 95% BC CI [−0.028, 0.035]). Discussion The findings describe distinct longitudinal trajectories of engagement and SRL across sequential instructional phases and support a developmental interpretation in which standard flipped instruction and progressively scaffolded AI integration may play distinct roles in supporting student engagement and SRL. This interpretation warrants evaluation in future controlled longitudinal studies.

What kind of evidence is this?

Supporting AI learning science · Design requires full-text appraisal

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 within-subjects longitudinal design with four quantitative measurement occasions across 15 weeks of instruction: Week 1 (baseline), Week 6 (end of the standard flipped phase), Week 11 (midway through the AI-enhanced phase), and Week 15 (end of the AI-enhanced phase). The final week of the semester (Week 16) was reserved for reflective essay collection. The sequential design enabled the characterization of longitudinal changes in student engagement and self-regulated learning across successive instructional phases but was not intended to isolate the causal effects of AI-enhanced instruction. Table 1 presents the study design and measurement schedule.

Limitations and uncertainty · source passage

Several limitations should be acknowledged. First, the within-subjects design without a parallel control group prevents causal attribution. Although the longitudinal transition from standard to AI-enhanced instruction provides stronger evidence than cross-sectional designs, future randomized or quasi-experimental studies are needed to determine whether progressively scaffolded AI support contributes to SRL development.

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.

  • Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
  • 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.
  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

  1. 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.
  2. 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.
  3. 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.
  4. 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

Chamintsetseg Myagmarkhorloo; Bat-Uchral Ganzorigt; Anita Habók. Student engagement and self-regulated learning across standard and AI-enhanced flipped learning: a within-subjects longitudinal study. Frontiers in Psychology (2026-08-19). https://doi.org/10.3389/fpsyg.2026.1936442

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.