Agentic education · Learning science

Actionable companion · Feedback & scaffolding

Learning with agency: A qualitative systematic review on AI empowered self-regulated learning in 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.

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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 This systematic review explores the burgeoning intersection of Artificial Intelligence (AI) applications and self-regulated learning (SRL) in higher education. Aiming to synthesize empirical studies, we employed a qualitative approach to scrutinize AI’s role in supporting SRL processes. Through a meticulous selection process adhering to PRISMA guidelines, we identified 14 distinct studies that leveraged AI applications, including chatbots, adaptive feedback systems, serious games, and e-textbooks, to support student autonomy. Our findings reveal a nuanced landscape where AI demonstrates potential in facilitating SRL’s forethought, performance, and reflection phases, yet also highlights whether the agency is human-centered or AI-centered leading to variations in the SRL model. This review underscores the imperative for balanced AI integration, ensuring technological advantages are harnessed without undermining student self-efficacy. The implications suggest a future where AI is a thoughtfully woven thread in the SRL fabric of higher education, calling for further research to optimize this synergy. Subject terms: Education, Human behaviour

What kind of evidence is this?

Conversational & pedagogical agents · Evidence synthesis

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

The selection of studies to be included in this study followed the preferred reporting items for systematic reviews and meta-analysis (PRISMA) statement24. PRISMA provides specific reporting guidelines on the essential components to be reported in each section of the review study25. Six explicit, transparent, and replicable steps were conducted accordingly: (1) specification of eligibility criteria; (2) specification of information sources; (3) specification of search strategy; (4) study selection; (5) data extraction; (6) content analysis. To facilitate the review, a qualitative analysis tool, MAXQDA26, and Excel were used.

Limitations and uncertainty · source passage

Firstly, despite the burgeoning interest in leveraging AI to enhance educational outcomes, there is a notable paucity of research specifically exploring AI’s role in SRL, despite its status as a core skill for human learning. The limited number of studies is consistent with the challenges posed by the multifaceted nature of SRL, which demands sophisticated support mechanisms that are difficult to operationalize through AI technologies7. This gap underscores the nascent stage of integrating AI into this nuanced domain11. As each aspect of SRL emphasizes the multilayered characteristics of the learner based on their context12, the specific AI applications in SRL require further empirical exploration and validation1. Future research must broaden and deepen investigations into AI’s capacity to augment these intricate SRL processes and strategies, thereby advancing both theoretical and practical understanding in this field8.

Design the system before the pilot

Define whether the agent asks questions, gives hints, explains or assesses. Test hallucinated feedback, excessive praise, misleading confidence and responses when the learner is confused. A conversational interface alone does not establish autonomous agency.

  • 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. 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

Min Lan; Xiaofeng Zhou. A qualitative systematic review on AI empowered self-regulated learning in higher education. NPJ Science of Learning (2025-05-03). https://doi.org/10.1038/s41539-025-00319-0

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.