AI–Family Integration Index (AFII): Benchmarking a New Global Readiness for AI as Family

AI–Family Integration Index (AFII): Benchmarking a New Global Readiness for AI as Family | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article AI–Family Integration Index (AFII): Benchmarking a New Global Readiness for AI as Family Prashant Mahajan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6577279/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract As Artificial Intelligence (AI) systems increasingly enter caregiving, educational, and emotionally sensitive domains, there is an urgent need to assess national readiness beyond traditional metrics like R&D, infrastructure, and digital output. While indices such as the Stanford AI Index and Oxford AI Readiness Index highlight technical prowess, they overlook relational dimensions including emotional safety, caregiving ethics, and symbolic trust. Simultaneously, many national AI policies articulate ethical aspirations but lack real-world implementation in family-centered environments. This study identifies two underexplored gaps: (1) the disconnect between policy intent and real-time practice in AI–Family Integration (AFI), and (2) the misalignment between conventional AI indices and emotionally grounded readiness metrics. In response, we introduce the AI–Family Integration Index (AFII)—a ten-dimensional global benchmarking tool designed to evaluate national preparedness for emotionally intelligent and caregiving-focused AI. The AFII framework assesses dimensions such as Emotional Authority & Safety Design, Youth-AI Exposure & Emotional Literacy, Family Structure & Emotional Labor Equity, Consent Frameworks, Symbolic Trust, and Cultural Receptivity. Each country was scored on a 0–10 scale using a mixed-method analysis of secondary data, policy reviews, and narrative synthesis. Equal weighting was applied to reflect conceptual parity and methodological fairness, echoing practices in the Human Development Index. To enhance interpretability, the AFII integrates real-world narratives—such as Singapore’s grief support robotics and Japan’s culturally attuned companion AI—to ground abstract indicators in everyday caregiving realities. The index was applied to thirteen countries, including top performers in the Stanford AI Index (2024), to surface relational asymmetries. Findings reveal significant contrasts between technological capacity and emotional readiness. While Singapore (9.6), South Korea (8.8), and Japan (8.7) top the AFII, countries like China (7.6) and the United States (7.4)—technological leaders—rank lower due to shortfalls in emotional literacy and symbolic legitimacy. Lower-ranking countries such as India (6.0), Brazil (5.2), and South Africa (4.8) illustrate emergent potential but require investment in emotionally inclusive AI ecosystems. A key insight is the policy–practice gap: nations often emphasize ethics rhetorically but lack caregiving-responsive implementation. The study introduces the AFII Governance Gap Lens as a diagnostic framework to map this disjunction. Additionally, comparison with the Stanford AI Index reveals a symbolic asymmetry: countries leading in AI power do not necessarily lead in relational integration. For policymakers, the AFII offers a scalable and ethically grounded tool for assessing AI maturity in emotionally charged settings. It reframes AI readiness beyond technocratic capacity toward relational trust, caregiving ethics, and cultural resonance—essential criteria for integrating AI into the most intimate and emotionally complex areas of human life. Artificial Intelligence and Machine Learning AI–Family Integration (AFI) Relational and Emotional AI Caregiving AI Technology - Ethical Governance Symbolic Trust Cultural Adaptation AI Policy–Practice Gap Inclusive AI Design Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6577279","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451020109,"identity":"4435c796-62b6-418f-9d8b-56a4a35d5fce","order_by":0,"name":"Prashant Mahajan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIie2SsQrCMBCGrxTaJeAaqbSvUHHoovgqJ0JdOgR8gW59hQg+TEugXbq4CYIorjpIlwodTBVREaKjQ77hh4T7yB0XAI3mT0lvaccy/NcbpYIySVvn+78pcFNoG49nVARJssou9dYLFqd9xVgDnSQ1BFMovbJkguC8v9zMBg6XjdESQXCFQmmEAhAN7oSWSdpZ1gCCqBTviFmNOObd/K54XxVqpylBnHBq3RX/q0IiWRDilJPQlLMMSL+cxGrFLg7neogjbudGxRrXdQshKpUiV/hchdkeAYxYKciPsntTNBqNRvPBFerWRlqLwWPYAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-5761-5757","institution":"R. 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While indices such as the Stanford AI Index and Oxford AI Readiness Index highlight technical prowess, they overlook relational dimensions including emotional safety, caregiving ethics, and symbolic trust. Simultaneously, many national AI policies articulate ethical aspirations but lack real-world implementation in family-centered environments.\u003c/p\u003e \u003cp\u003eThis study identifies two underexplored gaps: (1) the disconnect between policy intent and real-time practice in AI\u0026ndash;Family Integration (AFI), and (2) the misalignment between conventional AI indices and emotionally grounded readiness metrics. In response, we introduce the AI\u0026ndash;Family Integration Index (AFII)\u0026mdash;a ten-dimensional global benchmarking tool designed to evaluate national preparedness for emotionally intelligent and caregiving-focused AI.\u003c/p\u003e \u003cp\u003eThe AFII framework assesses dimensions such as Emotional Authority \u0026amp; Safety Design, Youth-AI Exposure \u0026amp; Emotional Literacy, Family Structure \u0026amp; Emotional Labor Equity, Consent Frameworks, Symbolic Trust, and Cultural Receptivity. Each country was scored on a 0\u0026ndash;10 scale using a mixed-method analysis of secondary data, policy reviews, and narrative synthesis. Equal weighting was applied to reflect conceptual parity and methodological fairness, echoing practices in the Human Development Index.\u003c/p\u003e \u003cp\u003eTo enhance interpretability, the AFII integrates real-world narratives\u0026mdash;such as Singapore\u0026rsquo;s grief support robotics and Japan\u0026rsquo;s culturally attuned companion AI\u0026mdash;to ground abstract indicators in everyday caregiving realities. The index was applied to thirteen countries, including top performers in the Stanford AI Index (2024), to surface relational asymmetries.\u003c/p\u003e \u003cp\u003eFindings reveal significant contrasts between technological capacity and emotional readiness. While Singapore (9.6), South Korea (8.8), and Japan (8.7) top the AFII, countries like China (7.6) and the United States (7.4)\u0026mdash;technological leaders\u0026mdash;rank lower due to shortfalls in emotional literacy and symbolic legitimacy. Lower-ranking countries such as India (6.0), Brazil (5.2), and South Africa (4.8) illustrate emergent potential but require investment in emotionally inclusive AI ecosystems.\u003c/p\u003e \u003cp\u003eA key insight is the policy\u0026ndash;practice gap: nations often emphasize ethics rhetorically but lack caregiving-responsive implementation. The study introduces the AFII Governance Gap Lens as a diagnostic framework to map this disjunction. Additionally, comparison with the Stanford AI Index reveals a symbolic asymmetry: countries leading in AI power do not necessarily lead in relational integration.\u003c/p\u003e \u003cp\u003eFor policymakers, the AFII offers a scalable and ethically grounded tool for assessing AI maturity in emotionally charged settings. It reframes AI readiness beyond technocratic capacity toward relational trust, caregiving ethics, and cultural resonance\u0026mdash;essential criteria for integrating AI into the most intimate and emotionally complex areas of human life.\u003c/p\u003e","manuscriptTitle":"AI–Family Integration Index (AFII): Benchmarking a New Global Readiness for AI as Family","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 05:29:09","doi":"10.21203/rs.3.rs-6577279/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c036a949-a145-4c38-8ade-2df7298ad891","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47985272,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2025-05-06T05:29:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-06 05:29:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6577279","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6577279","identity":"rs-6577279","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}