Actionable companion · Motivation & engagement
Learning with agency: Integration of a Social Robot in a Pedagogical and Logopedic Intervention with Children: A Case Study
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): Children · stage unspecified. Verify the participant population and task before transferring the findings.
Original authors’ abstract, reproduced under the linked source license:
Abstract The effectiveness of social robots such as NAO in pedagogical therapies presents a challenge. There is abundant literature focused on therapies using robots with children with autism, but there is a gap to be filled in other educational different needs. This paper describes an experience of using a NAO as an assistant in a logopedic and pedagogical therapy with children with different needs. Even if the initial robot architecture is based on genericbehaviors, the loading and execution time for each specific requirement and the needs of each child in therapy, made it necessary to develop “Adaptive Behaviors”. These evolve into an adaptive architecture, appliedto the engineer–therapist–child interaction, requiring the engineer-programmer to be always present during the sessions. Benefits from the point of view of the therapist and the children and the acceptance of NAO in therapy are shown. A robot in speech-therapy sessions can play a positive role in several logopedic aspectsserving as a motivating factor for the children.Future works should be oriented in developing intelligent algorithms so as to eliminate the presence of the engineer-programmer in the sessions. Additional work proposals should consider deepening the psychological aspects of using humanoid robots in educational therapy. Keywords: NAO robot, child–robot interaction, logopedic therapy, speech therapy, pedagogical therapy, adaptive behavior design
What kind of evidence is this?
Embodied learning agents · Survey / observational study
Stage: Children · stage unspecified. 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
For the research we followed a qualitative exploratory case study approach. Case study approach is adopted when we want to understand more about a phenomenon in context where there is little control over the events [46,47]. This happens when studying children’s behaviors. The purpose is not to make a comparison to other cases, just to explore the phenomenon in context. The interventions were implemented using the NAO robot ‘EBA’ in a therapeutic center.
Limitations and uncertainty · source passage
To get an idea of usability and feasibility aspects, the number of participants provides worthwhile insights. Perhaps, future studies will compare similar sessions in other similar therapy centers in order to study if the use of these robots has better results when we focus on specific needs.
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.
- Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
- 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
- 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. Establish the participants’ ages, developmental needs and educational setting from the full paper before selecting an intervention. Use adult-mediated activity and preserve hands-on and peer learning. 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. Evaluate an age-appropriate independent task after the interaction. Do not infer a preschool or primary-school effect when the paper does not establish that population. 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
Verónica Egido-García; David Estévez; Ana Corrales-Paredes; María-José Terrón-López; Paloma-Julia Velasco-Quintana. Integration of a Social Robot in a Pedagogical and Logopedic Intervention with Children: A Case Study. Sensors (Basel, Switzerland) (2020-11-13). https://doi.org/10.3390/s20226483
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.