Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: Can AI Robot Teaching Improve Children’s Performance in Housework? A Quasi-Experimental 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.

Read original paperSearch paper agentCC BY 4.0

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): Early childhood · preschool, Children · stage unspecified. Verify the participant population and task before transferring the findings.

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

Abstract Background: The primary objective of this study is to explore the effects of using Artificial Intelligence (AI) robot-assisted instruction in preschool activities on young children’s performance in housework. Methods: A quasi-experimental design was employed, with an experimental group and two control groups, to observe changes in children’s performance in housework before and after AI robot-assisted teaching. The study sample consisted of preschoolers from metropolitan Taipei, with 65 children in the experimental group, 53 in control group 1, and 75 in control group 2. Children participating in the research project required consent from the school, teachers, and parents. The children in the experimental group received AI robot-assisted instruction on household tasks for two days a week over three consecutive weeks, with each session lasting approximately 20–30 min. Control group 1 did not receive any experimental treatment, while control group 2 underwent the same learning schedule and content as the experimental group but was instructed by teachers instead of AI robots. The data were analyzed based on pre- and post-test surveys of parents observing their children’s performance in household tasks. Results: This study found that both AI robot-assisted teaching and teacher-led instruction enhanced children’s household skills, with the AI robot-assisted method showing slightly better results. These findings suggest that both technological tools and teacher guidance can effectively improve children’s housework performance, while artificial intelligence robots may provide young children with motivation and curiosity to learn due to their appearance and interactive design, which deserves further analysis. Conclusion: As technology rapidly advances, young children are exposed to various technological devices at an early age. Integrating technological media into future preschool teaching is inevitable, and leveraging tools like AI robots to support teaching, reduce teacher burden, and diversify instructional methods could be a direction worth considering. Keywords: AI robots, children, housework performance, quasi-experimental study

What kind of evidence is this?

Embodied learning agents · Evidence synthesis

Stage: Early childhood · preschool; 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

This study adopted a quasi-experimental design, a method used when the random assignment of participants into experimental and control groups is not feasible within the research setting, thereby necessitating the use of existing groups (e.g., classes) for the experiment [31]. The experimental setting of this study was in preschools, where sampling was required to be conducted at the class level due to the preschools’ regulations. As a result, the ideal of “complete randomization” in sample assignment could not be achieved. However, to accommodate these practical constraints and better control the experiment’s content and external validity, this study employs a “nonequivalent pre-test–post-test control group design” to explore the differences in changes in household task abilities between the experimental group and two control groups before and after the intervention. The experimental design model of this study is presented in Table 1.

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.

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. Use a brief adult-mediated story, turn-taking or language activity. An educator controls every interaction; the agent never becomes an unsupervised companion. Preserve hands-on play and human conversation. 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. Measure independent naming, explanation or turn-taking in a human-led activity after the tool is removed. Record frustration, overstimulation and the amount of adult help. 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

Ching-Fen Lee; Shain-May Tang. Can AI Robot Teaching Improve Children’s Performance in Housework? A Quasi-Experimental Study. Children (2024-10-30). https://doi.org/10.3390/children11111330

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.