Parenting Journal · Volume 2 · Issue 5 · Parenting and AI II

Volume 2 · Issue 5 · Parenting and AI II

One hundred readings on parenting, children and AI. Explore the system type, study context and limits alongside each source.

100 articles · Page 1 of 5

Edition list · CSV ↓
01

Vol. 2 · Issue 5 · Trust · Qualitative parent interviews

A Process Framework of Family AI Use in Early Education and Care

What family values should guide when we ask AI for help?

Nursery and kindergarten children; exact ages not reportedCC BY 4.0Mixed consumer AI and LLM use; qualitative family-practice studyDirect parenting evidence
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02

Vol. 2 · Issue 5 · Trust · Cross-sectional caregiver and healthcare-worker survey

Self-reported understanding and attitudes of healthcare workers and caregivers toward artificial intelligence in pediatric healthcare in a children’s hospital in China: a cross-sectional study

Does feeling comfortable with an AI answer mean we understand its limits?

Pediatric care; child ages not specifiedCC BY 4.0General-purpose generative AI/LLM perceptionsDirect parenting evidence
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03

Vol. 2 · Issue 5 · Trust · National online parent/carer opinion survey

Parental and carer views on the use of AI in imaging for children: a national survey

What should a clinician explain before AI helps interpret my child’s scan?

Children and young people receiving imaging; adults aged 21–80 respondedCC BY 4.0Narrow diagnostic imaging AI scenariosDirect parenting evidence
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04

Vol. 2 · Issue 5 · Trust · Qualitative parent interviews

Exploring Parental Experiences of Childhood Ear Health Clinics and Their Acceptability of AI‐Based Diagnostic Tools: A Qualitative Study

Would an AI ear assessment solve the part of care that troubles our family?

Children attending otitis media services; child ages not specifiedCC BY 4.0Proposed narrow otitis media diagnostic AIDirect parenting evidence
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05

Vol. 2 · Issue 5 · Trust · Cross-sectional parent survey

Evaluation of parents’ attitudes and perceptions regarding the use of artificial intelligence in dental practice: a cross-sectional survey

What would make AI assistance at the dentist worthy of our confidence?

Pediatric dental patients; exact child ages not specifiedCC BY 4.0General dental AI perceptions; no specific model evaluatedDirect parenting evidence
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06

Vol. 2 · Issue 5 · Trust · Cross-sectional parent survey of a proposed AI coach

Parents’ Perspectives on Using Artificial Intelligence to Reduce Technology Interference During Early Childhood: Cross-sectional Online Survey

Would an AI reminder help me be more present with my young child?

Children aged five years or youngerCC BY 4.0Hypothetical AI coaching/monitoring designDirect parenting evidence
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07

Vol. 2 · Issue 5 · Trust · Qualitative design-fiction study with parents

Parental Acceptance of Children’s Storytelling Robots: A Projection of the Uncanny Valley of AI

Which parts of story time would we want an intelligent robot to share?

Preschool children aged 2–5 yearsCC BY 4.0Hypothetical social-robot design; no LLM or autonomous robot trialDirect parenting evidence
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08

Vol. 2 · Issue 5 · Trust · Prospective nonrandomized parent-use pain-assessment study

Parental Assessment of Postsurgical Pain in Infants at Home Using Artificial Intelligence–Enabled and Observer-Based Tools: Construct Validity and Clinical Utility Evaluation Study

How should an AI pain score fit with what I notice about my infant?

Infant boys up to 12 months; mean age 5.1 months among completersCC BY 4.0Narrow computer-vision facial pain assessment; not generative AIDirect parenting evidence
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09

Vol. 2 · Issue 5 · Trust · Dual-perspective caregiver/expert LLM response audit

Performance of 5 Large Language Models in Perioperative Consultation for Pediatric Hypospadias: Cross-Sectional Comparative Study

Can an answer with credible citations still be unsafe for my child?

Children undergoing hypospadias care; pediatric age range not specified hereCC BY 4.0Generative AI/LLMs; blinded answer-ranking and safety benchmarkDirect parenting evidence
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10

Vol. 2 · Issue 5 · Trust · Expert-rated caregiver-information LLM benchmark

Can Large Language Models Aid Caregivers of Pediatric Cancer Patients in Information Seeking? A Cross‐Sectional Investigation

How can I judge an AI explanation of my child’s cancer beyond whether it sounds reassuring?

Pediatric cancer; no children or caregivers enrolled as usersCC BY 4.0Generative AI/LLM information and search toolsFamily implications
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11

Vol. 2 · Issue 5 · Trust · Cross-language and cross-geographic LLM content evaluation

Assessing AI-Generated Autism Information for Healthcare Use: A Cross-Linguistic and Cross-Geographic Evaluation of ChatGPT, Gemini, and Copilot

Is an AI answer equally reliable in the language our family actually uses?

Autism information for families of children roughly 2–18 years; no family users enrolledCC BY 4.0Generative AI/LLM multilingual answer benchmarkFamily implications
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12

Vol. 2 · Issue 5 · Trust · Expert-rated family-question LLM benchmark

The effectiveness of artificial intelligence models in addressing the concerns of families of children with cerebral palsy: a comparative analysis of ChatGPT, Gemini, and DeepSeek

Which questions about cerebral palsy require more than a fluent general answer?

Children with cerebral palsy; no families enrolledCC BY 4.0Generative AI/LLM clinical-information benchmarkFamily implications
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13

Vol. 2 · Issue 5 · Trust · Guideline-concordance LLM response audit

Can ChatGPT Guide Parents on Tympanostomy Tube Insertion?

How would I verify an AI answer about ear tubes against the clinician’s recommendation?

Children considered for tympanostomy tubes; no child or parent participantsCC BY 4.0Generative AI/LLM guideline-concordance benchmarkFamily implications
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14

Vol. 2 · Issue 5 · Trust · In-silico pediatric emergency-scenario safety audit

“ChatGPT, Can You Help Me Save My Child’s Life?” - Diagnostic Accuracy and Supportive Capabilities to Lay Rescuers by ChatGPT in Prehospital Basic Life Support and Paediatric Advanced Life Support Cases – An In-silico Analysis

Could an AI name the problem correctly while giving my family unsafe next steps?

Twenty pediatric emergency and two basic-life-support vignettesCC BY 4.0Generative AI/LLM emergency-advice safety benchmarkFamily implications
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15

Vol. 2 · Issue 5 · Trust · Expert-rated pediatric dental LLM benchmark

Brush, byte, and bot: quality comparison of artificial intelligence-generated pediatric dental advice across ChatGPT, Gemini, and Copilot

What evidence would make an AI dental answer useful for our actual decision?

Pediatric dental concerns; no enrolled parent or child usersCC BY 4.0Generative AI/LLM expert-rated answer benchmarkFamily implications
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16

Vol. 2 · Issue 5 · Trust · Single-model pediatric rare-disease information audit

Performance of ChatGPT-4o in Providing Information on Pediatric Inborn Errors of Immunity: A Cross-Sectional Evaluation

How should I weigh a mostly good AI explanation when our child has a rare condition?

Pediatric inborn errors of immunity; no family participantsCC BY 4.0Generative AI/LLM rare-disease information benchmarkFamily implications
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17

Vol. 2 · Issue 5 · Trust · Expert-rated parent-education LLM benchmark

Freely accessible large language models for parent education in pediatric immune thrombocytopenia: an expert-rated cross-sectional study of safety, readability, and guideline concordance

Does the best overall score identify the safest AI explanation for a parent?

Childhood immune thrombocytopenia; no enrolled parent usersCC BY 4.0Generative AI/LLM parent-information safety benchmarkFamily implications
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18

Vol. 2 · Issue 5 · Trust · Development and internal expert evaluation of a family-support AI agent

Development of a generative AI agent for family support in implementing family-based treatment for children and adolescents with anorexia nervosa

What evidence would a specialized AI family-support tool need before I relied on it?

Children and adolescents with anorexia nervosa; families not enrolledCC BY 4.0Domain-specific generative AI/LLM with retrieval and guardrailsFamily implications
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19

Vol. 2 · Issue 5 · Trust · Clinician focus groups using hypothetical family-care vignettes

‘Let’s work alongside it’: healthcare professionals’ perspectives of generative AI in supporting care at home in paediatric rheumatology

Which parts of my child’s home-care plan could AI explain, and which need the team?

Pediatric rheumatology care at home; no child or parent participantsCC BY 4.0Hypothetical generative AI/LLM care-guidance designsFamily implications
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20

Vol. 2 · Issue 5 · Trust · Normative legal and accountability framework

From “assistant” to “autonomous”: legal liability and ethical traceability frameworks for generative AI in clinical misdiagnosis scenarios, with a special focus on paediatrics

Who should be accountable when an AI-supported decision harms a child?

Neonates through adolescents are an explicit application groupCC BY 4.0Generative AI/LLM accountability theory; hypothetical audit infrastructureFamily 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.