Parent and Educator Concerns on the Pedagogical Use of AI-Equipped Social Robots
Bibliographic record
Abstract
Social robots equipped with conversational artificial intelligence are becoming increasingly common in educational settings. However, the long-term consequences of such uses remain relatively unknown due to their novelty. To ensure children's safe use of social robots, and proper adoption of the technology, it is crucial to scrutinize potential concerns regarding their usage. This exploration will provide insights to inform the design and development of this technology. Thus, this study investigated parents' and educators' perceptions of social robot use by children in the home and school settings. Our main objectives are to; 1) explore whether the types and/or levels of concern are tied to the role that individuals take (i.e., parents vs. educators); 2) explore if the levels of concern vary based on the gender and age of the potential child user; and 3) compile a catalogue of parents' and educators' concerns, both from the literature and those that are overlooked, surrounding children's use of SRs for learning. To address those inquiries, a cross-national online survey study was conducted with parents and educator participants (N = 396). Overall, participants indicated high levels of concern but recognized the potential in responsibly applying such technology for educational purposes.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".