Agentic education · Learning science

Actionable companion · Accessibility & inclusion

Learning with agency: Charting the Development of Robot-Assisted Social–Emotional Learning: Mapping Its Intellectual Foundations, Thematic Foci, and Evolution

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 Social and emotional learning (SEL) has become increasingly central to educational policy and lifelong development, while advances in robotics have opened new possibilities for supporting socio-emotional competencies through human–robot interaction. Despite the rapid growth of robot-assisted SEL research, this field remains fragmented, with limited understanding of its intellectual structure, thematic foci, and evolution. To address this gap, this study conducted a scientometric analysis of 241 publications indexed in Web of Science using bibliometric methods. Results indicate a steady growth trajectory, with research concentrated in a small number of core countries driving international collaboration. Influential publications and co-citation patterns reveal a strong foundation in autism-related interventions and child-centered social skill development. Thematic mapping shows that motor themes are dominated by soft skills, autism, and interaction design, while emotion recognition and affective computing form technically mature but specialized streams. Foundational concepts such as human–robot interaction and artificial intelligence remain central yet theoretically evolving. Emerging links between robotics, STEM, and project-based learning suggest ongoing pedagogical expansion. This study maps the intellectual and thematic structure of robot-assisted SEL, showing how robots are emerging as mediational agents in hybrid learning systems while revealing uneven integration and misalignments between technological capabilities and pedagogical foundations. Keywords: robots, social–emotional learning, scientometric, human–robot interaction

What kind of evidence is this?

Embodied learning agents · Design requires full-text appraisal

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.

Limitations and uncertainty · source passage

First, this study relies on a single bibliographic database, which, despite its high quality and rigorous indexing standards, may underrepresent relevant non-English publications and emerging interdisciplinary work. This selection may therefore introduce coverage bias and shape the observed research landscape. Future studies should incorporate multiple databases (e.g., Scopus, IEEE Xplore) and grey literature to enhance representativeness and reduce potential bias. Second, bibliometric analyses depend on author-provided keywords and citation structures, which may not fully capture theoretical nuance, methodological diversity, or the quality of empirical evidence. As a result, thematic clustering reflects structural and conceptual patterns rather than evaluating intervention effectiveness or theoretical coherence. Future research should complement bibliometric approaches with systematic reviews or meta-analyses to critically assess research design, outcome measures, and effect sizes. Third, the quantitative science-mapping approach adopted here captures macro-level trends but cannot account for micro-level interaction processes or contextual implementation dynamics. Consequently, it does not reveal how robot-assisted SEL operates in authentic educational settings. Future research should employ qualitative or mixed-method designs to examine teacher mediation, student engagement, and contextual variability in real-world classroom environments.

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.

  • Co-design accommodations with learners and specialists. Check access and error burden across groups without inferring subgroup effectiveness from small or unreported samples.

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

Wenjia Cui; Kejun Zhang; Zaipeng Zhang; Haoran Cui; Cixian Lv; Taghreed Ali Alsudais; Xinghua Wang. Charting the Development of Robot-Assisted Social–Emotional Learning: Mapping Its Intellectual Foundations, Thematic Foci, and Evolution. Behavioral Sciences (2026-05-11). https://doi.org/10.3390/bs16050746

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.