{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T21:23:31Z","timestamp":1790112211857,"version":"4.0.1"},"reference-count":17,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T00:00:00Z","timestamp":1771286400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Artificial Intelligence (AI) is rapidly reshaping pediatric care. While AI-driven clinical screening tools have demonstrated significant value in the early identification of neurodevelopmental risks (e.g., dyslexia, autism), a parallel trend of continuous, consumer-grade quantification of neurotypical children is emerging.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Problem<\/jats:title>\n                    <jats:p>This paper critically evaluates the \u201cQuantified Child\u201d paradigm\u2014defined as the use of consumer technologies for continuous physiological and behavioral tracking. We argue that unlike targeted clinical interventions, this pervasive surveillance approach carries systemic risks: it may induce \u201ctechnoference\u201d in parent-child interactions, amplify caregiver performance anxiety, and trigger iatrogenic risks through false-positive labeling.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Proposal<\/jats:title>\n                    <jats:p>Drawing on developmental science, we propose shifting to a \u201cSupporting the Caregiver\u201d paradigm for non-clinical settings. In this model, AI functions as an administrative assistant to automate family logistics and reduce cognitive load, rather than a digital intermediary for monitoring the child.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Mechanism<\/jats:title>\n                    <jats:p>We posit that reducing caregiver stress and saving effective time serve as crucial proximal mediators. By improving the caregiver's psychological well being, AI indirectly protects the quality of responsive parenting, which is the definitive driver of positive child developmental outcomes in early childhood.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A paradigm shift from direct child quantification to caregiver support offers a more robust and ethical technological pathway, ensuring that AI serves to enrich, rather than displace, the human connections essential for early development.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyg.2026.1682555","type":"journal-article","created":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T06:34:13Z","timestamp":1771310053000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["From \u201cquantifying the child\u201d to \u201csupporting the caregiver\u201d: a paradigm evaluation and ethical pathway selection for AI applications in child development"],"prefix":"10.3389","volume":"17","author":[{"given":"Yan","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Pediatrics, First Affiliated Hospital, Henan University of Chinese Medicine","place":["Zhengzhou, Henan, China"]},{"name":"School of Pediatrics, Henan University of Chinese Medicine","place":["Zhengzhou, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, First Affiliated Hospital, Henan University of Chinese Medicine","place":["Zhengzhou, Henan, China"]},{"name":"School of Pediatrics, Henan University of Chinese Medicine","place":["Zhengzhou, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mu-Mu","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, First Affiliated Hospital, Henan University of Chinese Medicine","place":["Zhengzhou, Henan, China"]},{"name":"School of Pediatrics, Henan University of Chinese Medicine","place":["Zhengzhou, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, First Affiliated Hospital, Henan University of Chinese Medicine","place":["Zhengzhou, Henan, China"]},{"name":"School of Pediatrics, Henan University of Chinese Medicine","place":["Zhengzhou, Henan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong-Tao","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Chongqing Traditional Chinese Medicine Hospital, The First Affiliated Hospital of Chongqing University of Chinese Medicine","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Endocrinology and Metabolism, Chongqing Traditional Chinese Medicine Hospital, The First Affiliated Hospital of Chongqing University of Chinese Medicine","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1097\/DBP.0000000000000668","article-title":"A machine learning strategy for autism screening in toddlers","volume":"40","author":"Achenie","year":"2019","journal-title":"J. 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