How can we revisit an AI learning routine together when school and family demands change?
Yiqun Zhang; Wuyuan Guo · 2026-03-13 · Frontiers in Psychology
First grade, with mothers recalling the preceding preschool periodCC BY 4.0Mixed AI learning apps as described by caregivers; not a verified trial of a particular LLMDirect parenting evidence
If our family helped design an AI care tool, which daily problem and relationship should it support first?
Silvia Filogna; Giovanni Arras; Tommaso Turchi; Giuseppe Prencipe; Elena Beani; Clara Bombonato; Francesca Fedeli; Gemma D’Alessandro; Antea Scrocco; Giuseppina Sgandurra · 2025-10-30 · Frontiers in Robotics and AI
Children 5–14 yearsCC BY 4.0Hypothetical AI rehabilitation assistant and design fiction; no deployed LLM or effectiveness trialDirect parenting evidence
What happens to our conversation when several family members share a voice assistant?
Anastasia K. Ostrowski; Jenny Fu; Vasiliki Zygouras; Hae Won Park; Cynthia Breazeal · 2022-01-14 · Frontiers in Robotics and AI
Participants 4–69 years; family groups included children and adultsCC BY 4.0Earlier commercial voice assistants and embodied social robot; not contemporary open-ended LLM chatDirect parenting evidence
What should a vaccination chatbot answer itself, and when should our family reach a person?
Danya Arif Siddiqi; Fatima Miraj; Humdiya Raza; Owais Ahmed Hussain; Mehr Munir; Vijay Kumar Dharma; Mubarak Taighoon Shah; Ali Habib; Subhash Chandir · 2023-11-08 · International Journal of Medical Informatics
Caregivers of children 0–24 months at recruitmentCC BY 4.0Earlier intent-classification/NLP chatbot with human handoff; not generative LLM adviceDirect parenting evidence
Which pieces of care coordination would we welcome an assistant helping with, and which decisions should it leave to us?
Ryan Tennant; Sana Allana; Kate Mercer; Catherine M Burns; Andre Kushniruk; Yaa Kumah-Crystal; Dillon Chrimes · 2022-06-30 · JMIR Human Factors
Child ages not reported; child-care subset within a mixed caregiving sampleCC BY 4.0Hypothetical speech/NLP voice-assistant support; no deployed LLM or measured care effectFamily implications
How can we bring an AI-generated rare-disease question into a productive conversation with our child’s care team?
Charlotte Blease; James Jones; Catherine E Blease; Anna Kharko; Carolina Garcia Sanchez; Kenneth D Mandl; Amy Price · 2026-07-24 · Journal of Participatory Medicine
Parents/guardians of children with rare diseases plus adult patients; child ages not specifiedCC BY 4.0Consumer generative AI/LLM tools reported by patients and parents; observational use, not clinical validationDirect parenting evidence
Where would personalized messages help our family’s care plan, and where is direct contact with the care team more useful?
Konstantia Zarkogianni; Evi Chatzidaki; Nektaria Polychronaki; Eleftherios Kalafatis; Nicolas C. Nicolaides; Antonis Voutetakis; Vassiliki Chioti; Rosa-Anna Kitani; Kostas Mitsis; Κonstantinos Perakis; Maria Athanasiou; Danae Antonopoulou; Panagiota Pervanidou; Christina Kanaka-Gantenbein; Konstantina Nikita · 2023-03-17 · Nutrients
Children 6–14 years; mean 10.5 yearsCC BY 4.0Narrow AI personalization/recommendation within a multicomponent care platform; not an LLMDirect parenting evidence
If an adolescent helps choose a health chatbot, how can their priorities shape its language, boundaries, and human support?
Laura Maenhout; Carmen Peuters; Greet Cardon; Sofie Compernolle; Geert Crombez; Ann DeSmet · 2021-11-11 · Frontiers in Public Health
Adolescents 12–15 yearsCC BY (version not stated)Earlier intent-based NLP conversational agent with authored responses; not generative AIChild-facing evidence
How can our family’s experience help a care service decide what information an AI tool should provide?
Sasha Litwin; Maya Mohabir; Ipek Sevim Kocak; Devin Singh; Susan Woods; Matthew Hudson; Sarah Greene; Laura Adams; Grace Cordovano · 2025-08-29 · Journal of Participatory Medicine
Children under 1–18 years; median seven yearsCC BY 4.0Narrow predictive machine learning and NLP in a deployed ED app; not a generative chatbotDirect parenting evidence
How could young people use AI to develop a shared community idea while keeping local knowledge and the final choice in human hands?
Eduardo De la Vega-Taboada; Sofia A Portillo; Lina Maria Gomez-Garcia; Ann Banchoff; Victoria Maria Bermudez; Diana M Chavez; Tate Isabella Sgaraglino; Eugenia Flores Millender; Olga L Sarmiento; Abby C King; Andrew Coristine; Uday Kiran Chilakalapalli; Suhartini Suhartini · 2026-04-29 · JMIR AI
Adolescents; mean age 14 years (SD 1.41)CC BY 4.0Facilitator-mediated LLM critique and generative imaging in youth citizen scienceChild-facing evidence
What should parents, children, and clinicians be able to change or stop when a robot is used during care?
Mary Ellen Foster; Jennifer N Stinson; Lauren Harris; Kate Kyuri Kim; Sasha Litwin; Patricia Candelaria; Summer Hudson; Julie Leung; Ronald P A Petrick; Alan Lindsay; Andrés Ramírez-Duque; David Harris Smith; Frauke Zeller; Samina Ali · 2025-10-30 · Frontiers in Robotics and AI
Pediatric emergency-department patients; age range not clearly specified in this design reportCC BY 4.0Hybrid AI-planned socially assistive robot with substantial human operator control; not an LLMDirect parenting evidence
What would my child want a robot to do to help people care for one another?
Núria Vallès-Peris; Cecilio Angulo; Miquel Domènech · 2018-05-12 · International Journal of Environmental Research and Public Health
Six-year-old first-gradersCC BY 4.0Programmed educational robot prototypes and imagined social robotics; no deployed generative AIChild-facing evidence
How would an automated pediatric advice service connect us to a clinician when a question exceeds the bot’s role?
Choo Min; Rong Xiu Cynthia Lim; Sek Wan Tan; Sashikumar Ganapathy · 2025-03-18 · Frontiers in Digital Health
Children served by a pediatric emergency department; chatbot users/caregivers not individually characterizedCC BY 4.0Earlier NLP plus rule-based clinical pathways and human escalation; generative AI was future workDirect parenting evidence
Which sensory and communication details would our family need to shape before trying an expressive robot?
Sandra Cano; Jaime Díaz-Arancibia; Jeferson Arango-López; Julia Elena Libreros; Matías García; Juan Pedro Bandera; Pedro Núñez Trujillo; Suna Bensch · 2023-06-02 · Sensors (Basel, Switzerland)
Parents described autistic children mainly 3–15 years; no child effectiveness sampleCC BY 4.0Expressive social-robot prototype; programmed demonstration, with unvalidated aspirations for autonomous emotional interactionDirect parenting evidence
How could a child, parent, and care team agree on what an AI partner supports and what information they share?
Mark A Neerincx; Willeke van Vught; Olivier Blanson Henkemans; Elettra Oleari; Joost Broekens; Rifca Peters; Frank Kaptein; Yiannis Demiris; Bernd Kiefer; Diego Fumagalli; Bert Bierman · 2019-11-15 · Frontiers in Robotics and AI
Children mainly 7–14 years with type 1 diabetesCC BY (version not stated)Hybrid narrow AI/knowledge-based social robot and avatar; not a generative LLM or automated diabetes treatmentDirect parenting evidence
What exactly would our family be asked to do in an AI-supported activity program, and which outcome has been tested?
Qianxia Jiang; Xiayu Summer Chen; Dev Patel; Keith Brazendale; Sualba Alejandro; Matthew Balcarras · 2026-07-24 · JMIR Pediatrics and Parenting
Child/adolescent eligibility 0–18 years; included studies ranged from infancy to adolescenceCC BY 4.0Mixed rule-based/AI chatbots and narrow or generative AI interventions; system-specific differences matterFamily implications
If a robot entered our home, which relationships and routines should its design protect or support?
Christina E Stimson · 2024-12-19 · Medical Humanities
Children 11–15 years with osteogenesis imperfecta, with siblings and parentsCC BY 4.0Participatory exploration of social robotics with overt teleoperation; not an autonomous AI or LLM trialDirect parenting evidence