Parenting Journal · Volume 1 · Issue 2 · Parenting and AI

Trust

AI advice, credibility, uncertainty and the difference between an answer and reliable evidence.

20 articles · Page 1 of 1

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Vol. 1 · Issue 2 · Trust · Mixed-methods caregiver vignette survey

Factors Shaping Trust and Satisfaction With AI Medical Chatbots: A Mixed Methods Vignette Survey of Caregivers Seeking Guidance on Pediatric Infectious Diseases

Does an AI answer that feels useful and reassuring deserve the same level of trust?

Pediatric infectious-disease scenarios; no children enrolledCC BY 4.0Generative AI / GPT-4o response evaluationDirect parenting evidence
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04

Vol. 1 · Issue 2 · Trust · Child experiment with an adult comparison group

Children’s susceptibility to content generated by artificial intelligence

How well can children distinguish human-created from AI-generated images and text?

Children aged 6–10 years; adult comparison aged 18–34CC BY 4.0Generative AI text/image stimuli / experimental taskChild-facing evidence
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05

Vol. 1 · Issue 2 · Trust · Cross-sectional parent–adolescent study

Emotional Intelligence and Adolescents’ Use of Artificial Intelligence: A Parent–Adolescent Study

How might parents and teenagers differ in what they trust AI to do?

Adolescents aged 13–17 yearsCC BY 4.0Mixed AI tools / self-reported family useDirect parenting evidence
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06

Vol. 1 · Issue 2 · Trust · Two-layer expert and pediatric-relative response evaluation

Evaluating large language models in pediatric fever management: a two-layer study

Do parents and clinicians judge the same AI health answer in the same way?

Pediatric fever scenarios; no child interventionCC BY 4.0Generative AI / expert-and-caregiver response evaluationDirect parenting evidence
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07

Vol. 1 · Issue 2 · Trust · Content and evidence-quality audit of manufacturer claims

Social robotics for children: an investigation of manufacturers’ claims

What evidence supports the promises made about a child’s social robot?

Commercial robots marketed for children; no child participantsCC BY 4.0Social robots / manufacturer-claims evidence auditFamily implications
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09

Vol. 1 · Issue 2 · Trust · Cross-sectional caregiver survey

Parents’ understanding and attitudes toward the application of AI in pediatric healthcare: a cross-sectional survey study

What do caregivers think AI can do, and who do they expect to remain accountable?

Children receiving pediatric hospital care; ages varyCC BY (version not stated)Mixed AI applications / LLM-informed surveyDirect parenting evidence
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10

Vol. 1 · Issue 2 · Trust · Qualitative parent and provider interview study

Between Hope and Skepticism: Parent and Provider Expectations of Artificial Intelligence as a Bridge to Human‐Centered Care for Children With Chronic Illness

What would make AI support feel trustworthy to a family managing a child’s chronic illness?

Children with chronic illness; ages varyCC BY 4.0Mixed AI concepts / anticipatory stakeholder studyDirect parenting evidence
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11

Vol. 1 · Issue 2 · Trust · Descriptive and correlational parent survey

Parents’ Perspectives on Artificial Intelligence–Supported Pediatric Healthcare Services: A Descriptive and Correlational Study

Why might a parent welcome one AI application and worry about another?

Parents of children; child ages varyCC BY 4.0Mixed pediatric AI applications / attitudes studyDirect parenting evidence
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12

Vol. 1 · Issue 2 · Trust · Conceptual literature review

Anthropomorphizing Technology: A Conceptual Review of Anthropomorphism Research and How it Relates to Children’s Engagements with Digital Voice Assistants

What does it mean when a child speaks to a voice assistant as though it were a person?

Children; developmental stages vary across reviewed researchCC BY 4.0Voice assistants / conceptual evidence reviewChild-facing evidence
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13

Vol. 1 · Issue 2 · Trust · Child–robot interaction experiment

Hurry Up, We Need to Find the Key! How Regulatory Focus Design Affects Children’s Trust in a Social Robot

Is a child’s liking for a robot the same as appropriate reliance on its suggestions?

Children aged 7–9 yearsCC BY 4.0Social robot / teleoperated experimental behaviorChild-facing evidence
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14

Vol. 1 · Issue 2 · Trust · Comparative bilingual information-quality study

Autism-Related Information on Websites and General-Purpose Artificial Intelligence Chatbots: Comparative, Bilingual Study

Can the wording and language of a question change the quality of AI information about autism?

Autism information for families; no child participantsCC BY 4.0Generative AI / bilingual information auditFamily implications
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15

Vol. 1 · Issue 2 · Trust · Multi-model reliability and readability benchmark

Evaluation of Five Large Language Models for Parental Education in Pediatric Anesthesia: Reliability and Readability Study

What makes AI-generated parent information understandable as well as technically accurate?

Pediatric anesthesia education; no parent or child sampleCC BY 4.0Generative AI / reliability-and-readability benchmarkFamily implications
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16

Vol. 1 · Issue 2 · Trust · Comparative expert-rated LLM benchmark

Benchmarking large language models for congenital cataract parent counseling: safety, readability, and knowledge translation of developmental and genetic information

How should parents judge long, structured AI explanations of complex pediatric information?

Congenital cataract counseling; no child participantsCC BY 4.0Generative AI / multi-model counseling benchmarkFamily implications
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17

Vol. 1 · Issue 2 · Trust · Multi-model expert-rated response benchmark

Cross-sectional comparative evaluation of five large language model–driven chatbots on an expert-curated 44-question set of parent-facing questions about pediatric vitamin D deficiency

Can a model perform well on average and still give an unsafe answer to a parent?

Pediatric vitamin-D questions; no children or parents enrolledCC BY 4.0Generative AI / multi-model safety benchmarkFamily implications
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18

Vol. 1 · Issue 2 · Trust · Expert-rated LLM response benchmark

Reliability of ChatGPT answers to common questions on developmental dysplasia of the hip as an information source for parents

What can an eight-question audit tell us about relying on AI after a child’s diagnosis?

Developmental hip dysplasia counseling; no child participantsCC BY 4.0Generative AI / LLM response benchmarkFamily implications
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Study designs and age labels describe the source. Read each study’s findings and limitations before applying them to your family.