Testing the capability of generative artificial intelligence for parent and caregiver information seeking
Bibliographic record
Abstract
Abstract Objective This study explored the quality of generative artificial intelligence (AI) responses to common parenting questions across diverse sources of digitally available information. Background The recent rise of generative AI, such as ChatGPT and other large language models (LLMs), which generate answers by synthesizing publicly available information, raises questions about the quality of digital responses and the effect on parenting and outcomes for children. Method We hypothesized that querying a professionally prepared parenting newsletter would have higher quality responses than an LLM. We explored this by running 11 tests with five common parenting and caregiving topics about young children across controlled and open data sources. We analyzed three Cs (correctness, clarity, and connection), reliability (artificiality, credibility, and citation quality), and readability to assess the quality of LLM responses. Results ChatGPT largely provided correct and clear answers although citations were frequently absent and inaccurate. LLM responses often lacked emphasis on parent–child connection and developmental context, and reading level difficulty increased steeply. Conclusion Generative AI offers reasonably good answers to general parenting questions. However, parents and caregivers need to contextualize the information. Implications Topical experts may help meet nuanced parenting needs with cultural relevance and plain language, but AI can be useful for summarizing open‐access content.
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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 | 0.087 | 0.444 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".