All 100 Trust20 Respect20 Independence20 Collaboration20 Kindness20 01 Vol. 3 · Issue 1 · Trust · Qualitative parent information-seeking study
How do parents decide which AI answers to use, question or check elsewhere?
Mustafa EROL; Ahmet EROL · 2026-06-15 · European Journal of Pediatrics
Children aged 0–3 years CC BY 4.0 Generative AI / LLM use Direct parenting evidence
Read & explore ↗ 02 Vol. 3 · Issue 1 · Trust · Mixed-methods caregiver vignette survey
Does an AI answer that feels useful and reassuring deserve the same level of trust?
Run Huang; Joseph Cecil; Marjorie Freedman; Souti Chattopadhyay; Qiao Jin · 2026-07-14 · Journal of Medical Internet Research
Pediatric infectious-disease scenarios; no children enrolled CC BY 4.0 Generative AI / GPT-4o response evaluation Direct parenting evidence
Read & explore ↗ 03 Vol. 3 · Issue 1 · Trust · Two child selective-trust experiments
When a digital assistant and a person disagree, what guides a young child’s trust?
Amanda S. Haber; Sona C. Kumar; Melia Swenson; Kara Bode; Elizabeth Ruel · 2026-04-27 · Behavioral Sciences
Children aged 4–6 years CC BY 4.0 Voice assistant / experimentally supplied explanations Child-facing evidence
Read & explore ↗ 04 Vol. 3 · Issue 1 · Trust · Child experiment with an adult comparison group
How well can children distinguish human-created from AI-generated images and text?
Allison Langer; Steven Martinez; Peter J Marshall; Jason Chein · 2026-03-18 · Technology in society
Children aged 6–10 years; adult comparison aged 18–34 CC BY 4.0 Generative AI text/image stimuli / experimental task Child-facing evidence
Read & explore ↗ 05 Vol. 3 · Issue 1 · Trust · Cross-sectional parent–adolescent study
How might parents and teenagers differ in what they trust AI to do?
Marco Andrea Piombo; Sabina La Grutta; Maria Stella Epifanio; Gaetano Di Napoli; Cinzia Novara; Jiaming Fang · 2025-08-21 · Behavioral Sciences
Adolescents aged 13–17 years CC BY 4.0 Mixed AI tools / self-reported family use Direct parenting evidence
Read & explore ↗ 06 Vol. 3 · Issue 1 · Trust · Two-layer expert and pediatric-relative response evaluation
Do parents and clinicians judge the same AI health answer in the same way?
Guijun Yang; Hejun Jiang; Shuhua Yuan; Mingyu Tang; Jing Zhang; Jilei Lin; Jiande Chen; Jiajun Yuan; Liebin Zhao; Yong Yin · 2025-09-03 · Frontiers in Digital Health
Pediatric fever scenarios; no child intervention CC BY 4.0 Generative AI / expert-and-caregiver response evaluation Direct parenting evidence
Read & explore ↗ 07 Vol. 3 · Issue 1 · Trust · Content and evidence-quality audit of manufacturer claims
What evidence supports the promises made about a child’s social robot?
Jill A. Dosso; Anna Riminchan; Julie M. Robillard · 2023-12-19 · Frontiers in Robotics and AI
Commercial robots marketed for children; no child participants CC BY 4.0 Social robots / manufacturer-claims evidence audit Family implications
Read & explore ↗ 08 Vol. 3 · Issue 1 · Trust · Comparative pediatric-emergency response audit
Why is a convincing chatbot answer an inadequate safety test for an urgent child-health question?
Seerat Kular; Vikas Kumar; Alexander Muacevic; John R Adler · 2025-08-01 · Cureus
Pediatric emergency scenarios; no families enrolled CC BY 4.0 Generative AI / emergency-information audit Family implications
Read & explore ↗ 09 Vol. 3 · Issue 1 · Trust · Cross-sectional caregiver survey
What do caregivers think AI can do, and who do they expect to remain accountable?
Yan-Dan Huang; Shu-Long Zeng; Jie Lin; Zhi-Peng Mao; Qi-Liang Zhang; Gao-Zhong Feng; Qiong-Xia Ou · 2025-08-20 · Frontiers in Public Health
Children receiving pediatric hospital care; ages vary CC BY (version not stated) Mixed AI applications / LLM-informed survey Direct parenting evidence
Read & explore ↗ 10 Vol. 3 · Issue 1 · Trust · Qualitative parent and provider interview study
What would make AI support feel trustworthy to a family managing a child’s chronic illness?
Atefeh Shamsi; Mahboobeh Namnabati; Asghar Ehteshami; Hamed Zandi Esfahani · 2026-06-03 · Health Expectations : An International Journal of Public Participation in Health Care and Health Policy
Children with chronic illness; ages vary CC BY 4.0 Mixed AI concepts / anticipatory stakeholder study Direct parenting evidence
Read & explore ↗ 11 Vol. 3 · Issue 1 · Trust · Descriptive and correlational parent survey
Why might a parent welcome one AI application and worry about another?
Bengisu Akoglu; Aysegul Simsek; Saba Noor · 2026-05-30 · Journal of Nursing Management
Parents of children; child ages vary CC BY 4.0 Mixed pediatric AI applications / attitudes study Direct parenting evidence
Read & explore ↗ 12 Vol. 3 · Issue 1 · Trust · Conceptual literature review
What does it mean when a child speaks to a voice assistant as though it were a person?
Janik Festerling; Iram Siraj · 2021-11-23 · Integrative Psychological & Behavioral Science
Children; developmental stages vary across reviewed research CC BY 4.0 Voice assistants / conceptual evidence review Child-facing evidence
Read & explore ↗ 13 Vol. 3 · Issue 1 · Trust · Child–robot interaction experiment
Is a child’s liking for a robot the same as appropriate reliance on its suggestions?
Natalia Calvo-Barajas; Maha Elgarf; Giulia Perugia; Ana Paiva; Christopher Peters; Ginevra Castellano · 2021-07-08 · Frontiers in Robotics and AI
Children aged 7–9 years CC BY 4.0 Social robot / teleoperated experimental behavior Child-facing evidence
Read & explore ↗ 14 Vol. 3 · Issue 1 · Trust · Comparative bilingual information-quality study
Can the wording and language of a question change the quality of AI information about autism?
Valentin Nădășan; Ciprian-Rareș Păroiu; Alexandra Neguțescu; Elena-Gabriela Strete · 2026-07-13 · JMIR Formative Research
Autism information for families; no child participants CC BY 4.0 Generative AI / bilingual information audit Family implications
Read & explore ↗ 15 Vol. 3 · Issue 1 · Trust · Multi-model reliability and readability benchmark
What makes AI-generated parent information understandable as well as technically accurate?
Fulin Pu; Jishuang Hong; Xiaoying Wei; Yanling Chen; Andrew Coristine · 2026-06-18 · JMIR Medical Informatics
Pediatric anesthesia education; no parent or child sample CC BY 4.0 Generative AI / reliability-and-readability benchmark Family implications
Read & explore ↗ 16 Vol. 3 · Issue 1 · Trust · Comparative expert-rated LLM benchmark
How should parents judge long, structured AI explanations of complex pediatric information?
Ligang Jiang; Yihan Zhu; Chunyan Song; Xinya Hu; Xianming Fan; Weihua Yang; Yune Zhao · 2026-03-27 · Frontiers in Cell and Developmental Biology
Congenital cataract counseling; no child participants CC BY 4.0 Generative AI / multi-model counseling benchmark Family implications
Read & explore ↗ 17 Vol. 3 · Issue 1 · Trust · Multi-model expert-rated response benchmark
Can a model perform well on average and still give an unsafe answer to a parent?
Ping Shi; Tian Zhou; Qiao Nie; Baicheng Tao; Xiaolu Li; Mei Zhu · 2026-09-09 · Frontiers in Pediatrics
Pediatric vitamin-D questions; no children or parents enrolled CC BY 4.0 Generative AI / multi-model safety benchmark Family implications
Read & explore ↗ 18 Vol. 3 · Issue 1 · Trust · Expert-rated LLM response benchmark
What can an eight-question audit tell us about relying on AI after a child’s diagnosis?
Klemens Vertesich; Joachim Ortmayr; Reinhard Windhager; Madeleine Willegger · 2025-12-15 · Frontiers in Pediatrics
Developmental hip dysplasia counseling; no child participants CC BY 4.0 Generative AI / LLM response benchmark Family implications
Read & explore ↗ 19 Vol. 3 · Issue 1 · Trust · Comparative expert-rated LLM benchmark
How do we distinguish a clear AI explanation from a clinically complete one?
Ahmed Hassan Kamal · 2025-05-27 · Healthcare
Childhood knee-alignment questions; no family participants CC BY 4.0 Generative AI / clinical-information benchmark Family implications
Read & explore ↗ 20 Vol. 3 · Issue 1 · Trust · Expert-rated LLM response audit
How complete are AI answers to parent-facing dental-trauma questions?
Ruhi Sanvordekar; Elaine S Barretto; Dinesh F Swamy; Aswathy Sudarsanan · 2026-06-18 · Cureus
Pediatric dental-trauma scenarios; no families enrolled CC BY 4.0 Generative AI / parent-question response audit Family implications
Read & explore ↗ Study designs and age labels describe the source. Read each study’s findings and limitations before applying them to your family.