Agentic education · Learning science

Actionable companion · Feedback & scaffolding

Learning with agency: Artificial intelligence learning environments and educational psychology (2016–2025): a systematic bibliometric synthesis and review

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:

The rapid integration of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) into educational environments has fundamentally transformed instructional design building upon earlier AI research in educational psychology. However, there is growing concern that computational innovation is outstripping pedagogical and psychological coherence. To evaluate the intersection of AI learning environments and educational psychology, this study provides a systematic bibliometric synthesis of peer-reviewed scholarship published between 2016 and 2025. Following PRISMA 2020 guidelines, we employed a dualpronged approach, applying multiple descriptive and analytical modeling techniques to analyze a synthetic corpus of 34 systematic review papers and an empirical corpus of 1,376 original research articles. Longitudinal results reveal an unprecedented “generative turn” and a shift in methodological maturity, moving from exploratory computational modeling to rigorous experimental and quantitative validation. Despite these advancements, our analysis identifies a persistent “theoretical gap.” While the synthetic literature advocates for deep psychological grounding, empirical studies remain overwhelmingly anchored in surface-level motivational outcomes, failing to adequately operationalize complex constructs such as metacognition, epistemic beliefs, and self-regulated learning (SRL), just to name a few. We conclude that for AI to foster effective, equitable instruction and learning, it must transcend being a mere content delivery mechanism and instead function as a “psychological catalyst.” By prioritizing human-centric architectures, cognitive scaffolding, identity, and critical AI literacy, this synthesis provides a structural roadmap for stakeholders to align rapid technological strides with the foundational science of learning.

What kind of evidence is this?

Supporting AI learning science · Bibliometric / mapping study

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

To evaluate the intersection of AI learning environments and educational psychology, this study provides a systematic bibliometric synthesis of peer-reviewed scholarship published between 2016 and 2025. Following PRISMA 2020 guidelines, we employed a dualpronged approach, applying multiple descriptive and analytical modeling techniques to analyze a synthetic corpus of 34 systematic review papers and an empirical corpus of 1,376 original research articles. All screening workflows, inclusion/exclusion rules and analytical protocols are fully described; full replication is achievable using the documented database search strings and procedures.

Limitations and uncertainty · source passage

The primary strength of this review is the multi-layered bibliometric approach, which cross-validated the “synthetic layer” of high-level scholarship against a massive “empirical layer” of 1,376 primary studies. The use of a custom R-based pipeline and LDA thematic modeling ensured a reproducible and objective analysis of paradigm shifts. However, several limitations must be noted. First, the rapid pace of the field means that studies published in late 2025 may not yet be fully reflected in citation impact metrics. Interpretations of bibliometric trends are constrained to this 2016–2025 corpus; broader cross-field epistemological generalizations are tempered by scope limitations. Second, the strict exclusion of vocational and administrative applications—while necessary to maintain the focus on educational psychology—may have omitted relevant cross-disciplinary insights into AI-driven skill acquisition. Finally, the reliance on English-language, peer-reviewed journals may introduce a geographical bias, potentially underrepresenting innovations from non-English speaking regions that are also leading in AI integration.

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.

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

Shujin Zhong; Jack D. Simons. Artificial intelligence learning environments and educational psychology (2016–2025): a systematic bibliometric synthesis and review. Frontiers in Psychology (2026-08-06). https://doi.org/10.3389/fpsyg.2026.1831006

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.