[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-418700-105":59,"doc-detail-418700-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","rethinking-pediatric-human-ai-interaction-for-building-safer-digital-health-ecosystems","Rethinking Pediatric Human-AI Interaction for Building Safer Digital Health Ecosystems","","Artificial intelligence (AI) systems embedded in everyday digital spaces often assume adult modes of reasoning, creating interpretive gaps for younger users. This editorial explains how narrative-style, epistemic automation can make probabilistic estimates seem more authoritative than intended, while technical opacity and model drift may be misread as stable clinical logic. It argues that clinicians should address AI-mediated information seeking and highlights design measures like explicit uncertainty, layered explanations, and age-responsive prompting to reduce misinterpretation. Treating the pediatric digital ecosystem as a distinct regulatory and design setting improves alignment between algorithm behavior, developmental cognition, and clinical practice.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/rethinking-pediatric-human-ai-interaction-for-building-safer-digital-health-ecosystems/418700/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/rethinking-pediatric-human-ai-interaction-for-building-safer-digital-health-ecosystems/418700.png","ImageObject",300,407,{"name":92,"@type":93},"Alex Sinclair","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-28",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why can pediatric users misinterpret AI outputs as authoritative clinical reasoning?","Question",{"text":112,"@type":113},"AI often converts probabilistic estimates into fluent narrative text, and adolescents may grant more authority than the evidence supports. Emotional tone and limited cues can reinforce this credibility inflation.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does technical opacity and model drift affect minors’ understanding?",{"text":117,"@type":113},"Limited transparency about model updates makes it harder for minors to distinguish computational changes from clinician-like reasoning. As systems evolve, the gap widens when users receive few signals about when or why outputs changed.",{"name":119,"@type":110,"acceptedAnswer":120},"What clinician and design strategies does the editorial recommend to reduce harm?",{"text":121,"@type":113},"Clinicians should ask adolescents about symptom checkers, chatbots, wearable interpretations, and health apps to contextualize concerns. The editorial also recommends design features such as explicit uncertainty cues, layered explanations, and age-responsive prompting, alongside digital health literacy that teaches probabilistic reasoning and data provenance.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},418700,1791095239,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":14,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":24,"preview_page_count":4},1099523882182,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Review began 11/26/2025  \nReview ended 12/01/2025  \nPublished 12/03/2025  \n© Copyright 2025  \nAbbasian et al. This is an open access article distributed under the terms of the Creative Commons Attribution License CCBY 4.0. , which permits unrestricted use , distribution , and reproduction in any medium , provided the original author and source are credited.  \nDOI: 10.7759/cureus.98369  \nOpen Access Editorial  \nRethinking Pediatric Human-AI Interaction for Building Safer Digital Health Ecosystems  \nHana Abbasian 1, Imeth Illamperuma 2  \n1. Medical Education and Simulation, Center for Bioethics, Harvard Medical School, Boston, USA 2. Medicine, McMaster University, Hamilton, CAN  \nCorresponding author: Hana Abbasian, [hana_abbasian@hms.harvard.edu](hana_abbasian@hms.harvard.edu)  \nAbstract  \nArtificial intelligence (AI) systems used in everyday digital spaces often rely on design assumptions shaped by adult patterns of reasoning, which creates specific interpretive gaps for younger users. This editorial examines how narrative-style outputs produced through epistemic automation can make probabilistic estimates appear more authoritative than intended for some adolescents. It also considers how technical opacity and model drift introduce shifts in system behavior that minors may misread as stable clinical logic, since there are few cues that distinguish computational changes from expert reasoning. When adolescents independently consult conversational agents or symptom-oriented tools, these interactions can influence clinical encounters without being systematically discussed. Therefore, this editorial outlines practical ways for clinicians to ask about AI-mediated information seeking and describes developmental design features, such as explicit uncertainty cues, layered explanations, and age-responsive prompting, that can reduce misinterpretation. Treating the pediatric digital ecosystem as a distinct design and regulatory setting allows for more precise alignment between algorithmic behavior, developmental cognition, and clinical practice.  \nCategories: Public Health, Quality Improvement, Pediatrics  \nKeywords: artificial intelligence (ai), digital health technologies, health policy and advocacy, machine learning tools, pediatric ethics  \nEditorial  \nThe integration of artificial intelligence (AI) into pediatric life is accelerating in ways that exceed the conceptual tools clinicians use to evaluate risk. Children now routinely interface with algorithmic systems that function as informal health advisors [1] . These include generative conversational agents, predictive symptom models, adaptive learning platforms, and biomarker-inferring phone sensors [1,2] . Most of these systems were trained on datasets and design assumptions that reflect adult cognition [2] . This produces a structural mismatch between how the technology behaves and how a developing user interprets that behavior.  \nA central problem is the growing presence of epistemic automation, meaning the automation of judgment and explanation [3] . Many AI tools present probabilistic estimates as fluent narrative text. Emerging evidence shows heightened susceptibility to credibility inflation among younger users, where AI outputs are granted more authority than their underlying evidence justifies [2,4] . This is partly due to the emotional tone conveyed by the system, which young users may interpret as implicit social reassurance [4] . Pediatric care must articulate how these psychological mechanisms influence help-seeking behavior.  \nTechnical opacity further complicates these challenges [3] . Many health-adjacent AI tools provide limited transparency around model updates and model drift, including how outputs change as the system retrains over time [2,3] . Minors are uniquely vulnerable to misinterpreting such outputs because they cannot distinguish between a system’s computational limits and a clinician’s reasoning process [4] . This interpretive gap widens as systems evolve","cbCaivDPM0yp2qCk","https://ap.wps.com/l/cbCaivDPM0yp2qCk","pdf",87567,"English","# Abstract\n# Editorial\n## Epistemic automation and credibility inflation\n## Technical opacity and model drift\n## Limits of parental mediation and adolescent autonomy\n## Practical clinician questions and digital health literacy\n## Regulatory and design safeguards for pediatric ecosystems","[{\"question\":\"Why can pediatric users misinterpret AI outputs as authoritative clinical reasoning?\",\"answer\":\"AI often converts probabilistic estimates into fluent narrative text, and adolescents may grant more authority than the evidence supports. Emotional tone and limited cues can reinforce this credibility inflation.\"},{\"question\":\"How does technical opacity and model drift affect minors’ understanding?\",\"answer\":\"Limited transparency about model updates makes it harder for minors to distinguish computational changes from clinician-like reasoning. As systems evolve, the gap widens when users receive few signals about when or why outputs changed.\"},{\"question\":\"What clinician and design strategies does the editorial recommend to reduce harm?\",\"answer\":\"Clinicians should ask adolescents about symptom checkers, chatbots, wearable interpretations, and health apps to contextualize concerns. The editorial also recommends design features such as explicit uncertainty cues, layered explanations, and age-responsive prompting, alongside digital health literacy that teaches probabilistic reasoning and data provenance.\"}]","Rethinking Pediatric Human-AI Interaction for Building Safer Digital Health Ecosystems | PDF",1790614027]