Children’s Understanding and Use of Voice-Assistants: Opportunities and Challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract As voice-driven digital assistants become more popular and widely available, it is essential to understand how children think about and use these devices. Because voice-assistants (VAs) share characteristics with humans, such as interaction via natural language, they hold unique appeal to young children as both information sources and social partners. However, these shared characteristics with humans also potentially make it more difficult for children to understand how VAs work and to evaluate the information that they provide. Given the recent advent of VAs and rapid improvements in the technologies that they rely on, future research should focus on how VA use impacts children’s social cognition and learning, and how to design VAs that children can use safely and effectively. Recommendations are provided for how caregivers, educators, developers, and policymakers can support children’s use of VAs in ways that promote their social and cognitive development, while protecting them from potential dangers.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it