The robot that stayed: understanding how children and families engage with a retired social robot
Bibliographic record
Abstract
Introduction: Social robots are increasingly introduced into homes as short-term educational or entertainment tools for children. However, their physical presence and social roles may persist long after their intended use has ended. This study explores how families continue to engage with a child-focused educational robot years after its original deployment. Methods: We conducted a retrospective follow-up study with 19 families who participated in a 2021 in-home deployment of a reading companion robot for preschool-aged children. In 2025, we revisited these families through in-depth interviews to investigate how the robot had been integrated, re-purposed, or preserved over time. Results: Despite the children outgrowing the robot's instructional content, 18 families had retained the robot. Families described transitions in its role-from an educational device to a symbolic household member-characterized by emotional attachment, care-taking behaviors, and affection. The robot was re-framed as a memory object, integrated into new routines, or passed on ceremonially, akin to a "retirement." Discussion: Our findings reveal three key themes explaining the robot's enduring presence: (1) emotional attachment and personification, (2) symbolic and nostalgic value, and (3) practical re-purposing within household routines. This study contributes to long-term human-robot interaction literature by extending domestication theory and emphasizing the importance of designing for the full life cycle of social robots-including end-of-life transitions. It underscores how social robots can become meaningful companions and enduring artifacts of family identity, long after their functional use has ended.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".