Agentic education · Learning science

Actionable companion · Self-regulation & metacognition

Learning with agency: Digital transformation in education: psychological perspectives on learning, engagement, and outcomes: a systematic review and meta-analysis

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): Age / stage not established. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Abstract Background The process of digital transformation has developed new educational systems that use technology-based tools, including learning management systems, artificial intelligence, and gamified systems, as well as digital teaching methods. The studies show incomplete evidence about how this phenomenon affects learning engagement, motivation, cognitive processes, self-regulation skills, and institutional performance. Objective This meta-analysis aimed to synthesize global evidence on the psychological perspectives of digital transformation in education, focusing on learning engagement, motivation, cognitive/metacognitive processes, self-regulation, and institutional factors. Methodology A systematic meta-analysis was conducted to assess 42 studies that employed a random-effects model with the inverse variance method as their analysis approach. The odds ratios to calculate effect sizes, which included 95% confidence intervals. The subgroup analyses were about five different psychological and institutional domains. The assessment of heterogeneity used I2 statistics, while the publication bias assessment relied on funnel plots and Egger’s regression test. Results The results showed that digital transformation produced positive results in all areas which included learning engagement (OR = 1.79, 95% CI: 1.68–1.91), motivation, affective psychology (OR = 1.57, 95% CI: 1.21–2.03), cognitive and metacognitive psychology (OR = 1.79, 95% CI: 1.60–2.02) and self-regulation and learning psychology (OR = 1.66, 95% CI: 1.49–1.85) and institutional factors (OR = 1.71, 95% CI: 1.48–1.97). High motivation and self-regulation showed high domain heterogeneity, while the other domains maintained their consistency. The study showed no publication bias except for cognitive and metacognitive studies, which showed asymmetrical findings. Conclusion Digital transformation in education brings about significant advantages for psychological and institutional results, which include better learning engagement and cognitive growth. The different factors that affect motivation and self-regulation show how contextual and pedagogical elements create an impact. Keywords: cognitive psychology, digital transformation, educational technology, institutional factors, learning engagement, LMS, meta-analysis, motivation

What kind of evidence is this?

Supporting AI learning science · Evidence synthesis

Stage: Age / stage not established. 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 used 10 different methods, which included Structural Equation Modeling (SEM) as the second most common design after quantitative studies, which reached 10 times of usage (n = 10), and used Structural Equation Modeling (SEM) as their testing method to examine the relationship between digital learning variables and psychological outcomes. The study used a cross-sectional design and mixed-method design to achieve equal representation of two research methods, which produced descriptive results together with data that combined qualitative and quantitative elements. Experimental studies (n = 3) and quasi-experimental designs (n = 1) provided evidence about how digital interventions cause particular effects, while the four studies on scale development and validation established reliable and valid psychological measurement instruments. The research field used two cognitive modeling studies, one longitudinal study, one ecological study, and one learning analytics study to achieve advanced analytical methods, which also provided time-sensitive perspectives, thereby enhancing the research study’s methodological rigor and evidence base through their combined contributions (Figure 3).

Limitations and uncertainty · source passage

The initial database search identified 1,200 records, including PubMed (n = 223), Web of Science (n = 340), Embase (n = 222), Cochrane Library (n = 150), and Google Scholar (n = 265). All references of the selected articles and systematic reviews were further examined by hand. The search covered all the studies that have been published until the last day of the search preceding data analysis without any limitation to the publication date.

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.
  • 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. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. 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. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. 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

Pingping Hou. Digital transformation in education: psychological perspectives on learning, engagement, and outcomes: a systematic review and meta-analysis. Frontiers in Psychology (2026-09-16). https://doi.org/10.3389/fpsyg.2026.1856031

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.