Actionable companion · Cognitive load & attention

Learning with agency: Impact of educational agents on student’s learning outcomes: a 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): Higher education. Verify the participant population and task before transferring the findings.

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

Introduction With the deep integration of artificial intelligence technology in the field of education, educational agents as an intelligent teaching tool possessing interactive and personalised characteristics have drawn increasing attention for their impact on learning outcomes. Methods This study employs a meta-analysis methodology to systematically synthesise 52 empirical investigations published in internationally authoritative journals between 2015 and 2025. It examines the overall effect of educational agents on student learning outcomes, their specific manifestations at cognitive and non-cognitive levels, and the influence of moderating variables such as types of agents, subjects, sample size, and academic level. Results Findings indicate that educational agents exert a significant positive influence on student learning outcomes. Regarding cognitive abilities, they demonstrate moderate to substantial enhancement effects on creative thinking, academic performance, and communication skills, while their impact on spatial ability and problem-solving skills falls below statistical significance. Regarding non-cognitive abilities, learning motivation and learning attitude showed significant enhancement, whereas the effects on learning engagement and learning interest were smaller and non-significant. Moderation analyses indicated that the impact of educational agents was particularly pronounced among chatbots, universities, small-scale settings, and engineering technology disciplines. Discussion This study reveals limitations in educational agents’ cultivation of complex abilities and personalised adaptation, providing empirical evidence for their precise application and optimised design.

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

This study strictly adhered to the PRISMA guidelines for literature retrieval and selection (Moher et al., 2009), and followed the fundamental meta-analysis workflow proposed by Borenstein et al. (2021): literature search, literature screening, literature coding, calculation of the average effect size, calculation of the effect size for moderating variables, and comprehensive discussion of the research findings.

Limitations and uncertainty · source passage

Introduction With the deep integration of artificial intelligence technology in the field of education, educational agents as an intelligent teaching tool possessing interactive and personalised characteristics have drawn increasing attention for their impact on learning outcomes. Methods This study employs a meta-analysis methodology to systematically synthesise 52 empirical investigations published in internationally authoritative journals between 2015 and 2025. It examines the overall effect of educational agents on student learning outcomes, their specific manifestations at cognitive and non-cognitive levels, and the influence of moderating variables such as types of agents, subjects, sample size, and academic level. Results Findings indicate that educational agents exert a significant positive influence on student learning outcomes. Regarding cognitive abilities, they demonstrate moderate to substantial enhancement effects on creative thinking, academic performance, and communication skills, while their impact on spatial ability and problem-solving skills falls below statistical significance. Regarding non-cognitive abilities, learning motivation and learning attitude showed significant enhancement, whereas the effects on learning engagement and learning interest were smaller and non-significant. Moderation analyses indicated that the impact of educational agents was particularly pronounced among chatbots, universities, small-scale settings, and engineering technology disciplines. Discussion This study reveals limitations in educational agents’ cultivation of complex abilities and personalised adaptation, providing empirical evidence for their precise application and optimised design.

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.

Limit simultaneous instructions and unnecessary dialogue. Compare task performance and a suitable mental-effort measure; easier-feeling work is not automatically better learning.
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

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

Xi Xu; Xin Cao; Qian Wu. Impact of educational agents on student’s learning outcomes: a meta-analysis. Frontiers in Psychology (2026-02-24). https://doi.org/10.3389/fpsyg.2026.1707196

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.