Actionable companion · Learning design & outcomes
Learning with agency: Social robots: a meta-analysis of learning outcomes
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.
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 Previous meta-analyses show that social robots aid learning but were often limited in scope or grouped diverse control conditions together. This meta-analysis examined learning outcomes, focusing on control condition type. We retrieved 146 studies (Google Scholar and reference searches) where a physical social robot was used for training cognitive skills, comprising 183 post-test effect sizes between the robot and the control group, and 372 pre-post effect sizes. Analysis of the 78 studies with control groups indicated that robots generally improved learning, most notably when compared to a no-training control group (d = 0.75). Comparing robots to human teachers yielded an overall positive effect (d = 0.31), although effect sizes varied widely. This variability was explained by the robot’s role: robots in a co-teaching capacity showed a strong positive effect (d = 0.88), while robots replacing the teacher showed no benefit (d = −0.06). LLM-based sentiment analysis indicated that papers from outside Europe received higher positivity scores when describing the robots. We conclude that the effect size is influenced by the robot implementation and the control condition chosen. Keywords: cognitive skills, educational robotics, human-robot interaction, learning, social robot
What kind of evidence is this?
Embodied learning agents · 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
Our literature search and reporting adhered to the PRISMA guidelines (Page et al., 2021), see supplementary material. Literature searches were conducted using Google Scholar (latest search date: 13 September 2024). Instead of fixed search strings, we used combinations of keywords related to the robot (e.g., “social robot”, “humanoid robot”, “robot tutor”) and the outcome (e.g., “learning”, “cognitive skills”, “education”, “post-test”). This approach was needed because of the diverse terminology in the field to achieve comprehensive retrieval of relevant studies. This keyword search was supplemented by a manual review of the reference lists of previous meta-analyses and a prior review on social robots (Johal, 2020) to assess whether there were further relevant studies for inclusion. Furthermore, we performed forward citation tracking for eligible studies and targeted searches for other relevant publications by the authors of these studies. Additional searches were conducted through ResearchGate and using Google Search. This iterative process continued until no new eligible studies were being identified.
Limitations and uncertainty · source passage
These 12 items, along with the automatically extracted text from the PDF of the paper, were submitted to the Gemini API. This process was repeated five times, with the 12 items presented in a different random order each time. Subsequently, we calculated a mean score per study for each item.
Design the system before the pilot
Compare the embodied interaction with an equivalent human-led or screen-based activity. Separate the effects of novelty and social presence from learning. Review physical access, sensory needs and adult supervision.
- Specify a measurable learning objective, a comparator and a task without tool assistance before adopting a system.
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. 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.
- 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. 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
J C F de Winter; D Dodou; F Moorlag; J Broekens. Social robots: a meta-analysis of learning outcomes. Frontiers in Robotics and AI (2026-03-23). https://doi.org/10.3389/frobt.2025.1735198
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.