Feeling Economics: AI, Empathy, and the Future of Labor– A Systematic Review and Conceptual Model

Feeling Economics: AI, Empathy, and the Future of Labor– A Systematic Review and Conceptual Model | 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 Systematic Review Feeling Economics: AI, Empathy, and the Future of Labor– A Systematic Review and Conceptual Model Mohamed Ibrahim Abdelaziz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7971227/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 accelerating AI automation displaces routine intellectual and cognitive tasks, the labor market may be fun- damentally reshaping toward a ”feeling economy” that values human-centric skills like empathy, emotional intelligence (EI), and relational acuity. This systematic review synthesizes evi- dence on this transition, addressing critical gaps regarding its mechanisms, occupational shifts, and necessary policy responses. Following PRISMA guidelines, we systematically searched 13 databases (e.g., Google Scholar, OECD iLibrary, ILO) from 2010 to October 2025, screening 448 unique records to include 58 sources (peer-reviewed articles, institutional reports, and gray literature) appraised via MMAT and adapted checklists. Our mixed-methods synthesis reveals that AI displaces specific cognitive tasks but complements human EI, with no net employment decline yet observed; instead, demand is surging for care and relational roles. Thematic analysis identified key mechanisms, including task reorganization and feedback loops where AI- driven productivity boosts demand for human-centered skills. We present a novel conceptual model, visualized as a causal loop diagram, to illustrate these dynamics, showing reinforcing loops for EI upskilling and balancing loops for wage normalization. We conclude that AI is catalyzing a structural shift toward a ”feeling economy,” necessitating proactive, human-centric policies focused on EI upskilling and ethical AI regulation to ensure an equitable transition and mitigate inequality risks. Artificial intelligence automation emotional intelligence feeling economy labor market transitions future of work systematic review Figures Figure 1 Figure 2 Figure 3 I. INTRODUCTION The rise of artificial intelligence (AI) and machine learning technologies has sparked intense debate on labor market futures [ 5 ], [ 9 ]. While earlier automation waves targeted routine manual tasks, contemporary AI excels at non-routine cognitive work, such as data analysis, report generation, and decision-making [ 2 ]. This evolution raises profound questions about the future of human roles in an AI-dominated economy. The ”feeling economy” concept, introduced by Huang and Rust [ 12 ] – [ 14 ], posits that as AI masters analytical think- ing, economic value will pivot to uniquely human strengths: empathy, emotional intelligence (EI), and complex relational interactions. This review rigorously examines this posited shift through a systematic lens, integrating empirical evidence on AI displacement with theoretical frameworks on emotional labor [ 10 ], [ 11 ]. Drawing from labor economics [ 3 ], AI studies [ 24 ], and organizational psychology [ 22 ], we argue that AI will amplify demand for EI-centered jobs. However, the outcomes of this transition are not predetermined; they depend critically on mechanisms like task complementarity [ 1 ] and the design of policy interventions [ 19 ]. This review addresses a significant gap in the literature by providing an interdisciplinary synthesis and developing a conceptual model to guide stakeholders in education, industry, and policy [ 25 ]. This review makes three primary contributions. First, we provide a timely and comprehensive synthesis of empirical evidence, moving beyond speculation to assess observable labor market shifts. Second, we integrate disparate the- oretical fields (labor economics, AI studies, organizational psychology) to propose a novel conceptual model ( Fig. 2 ). Third, we develop testable scenarios and actionable policy recommendations grounded in this synthesis, providing a clear agenda for future research and public policy. II. METHODS This review follows the PRISMA 2020 guidelines [ 21 ]. The full protocol, summarized here, was registered (details in Appendix A) to ensure transparency and rigor. A. Objectives and Research Questions The primary objective is to assess evidence on how accelerating AI automation reshapes labor markets toward a ”feeling economy.” Our Primary Research Question (RQ) is: How will AI-driven automation of intellectual tasks change the demand for empathy- and EI-centered occupations over the next 10–25 years? Secondary RQs include: (a) What empirical links exist between AI and declines in traditional intellectual jobs? (b) How are EI and emotional labor defined and measured in this context? (c) Which occupations are likely to expand due to high empathy requirements? (d) What policy interventions can facilitate positive transitions? B. Eligibility Criteria We included empirical studies, theoretical papers, reviews, and policy reports (peer-reviewed, preprints, white papers) published from 2010 to October 1, 2025. Sources had to address both AI/automation of intellectual tasks and its implications for occupations involving emotional labor or EI. This covered research on job displacement, skill shifts, occupational forecasts, and the ”care” or ”feeling” economy. English-language sources were prioritized. We excluded non- systematic opinion pieces, speculative media, duplicates, and inaccessible sources. Table I Sample of Included Studies ID Author(s) Year Type Key Findings Codes 1 Huang et al. [ 13 ] 2019 Theoretical AI shifts to thinking; humans to AI automation, EI feeling roles. 2 Lane, Saint-Martin [ 17 ] 2021 Review No net job loss; task reorganiza- Labor changes tion. 3 Squicciarini, Nachtigall [ 23 ] 2021 Empirical Rising AI skills demand; comple- AI automation ments like creativity. 4 Broecke [ 4 ] 2023 Review AI tools efficiency; bias risks. Labor changes 5 ILO [ 16 ] 2025 Empirical AI displacement in regions; green Labor changes jobs. 6 Frey, Osborne [ 9 ] 2017 Empirical 47% of US jobs at high risk of AI automation automation. 7 Autor, D. [ 3 ] 2015 Theoretical Task complementarity; automation Labor changes creates new tasks. 8 Goleman, D. [ 10 ] 1995 Theoretical Defines EI and its importance in EI life/work. 9 Hochschild, A. [ 11 ] 1983 Theoretical Introduces ’emotional labor’ con- EI cept. 10 WEF [ 25 ] 2023 Report Analytical/creative skills rising; AI AI automation creates new roles. 11 Acemoglu, Restrepo [ 1 ] 2018 Empirical Displacement and productivity ef- Labor changes fects of automation. 12 Brynjolfsson et al. [ 6 ] 2019 Theoretical Lag in productivity gains from AI AI automation adoption. 13 Damasio, A. [ 7 ] 2021 Theoretical Argues for the inseparability of EI emotion/reason. C. Information Sources and Search Strategy We conducted comprehensive searches of 13 academic and gray literature databases: Web of Science, Scopus, PubMed, PsycINFO, EconLit, IEEE Xplore, Google Scholar, arXiv, SSRN, RePEc, OECD iLibrary, ILO, and the World Bank. Search terms combined three concept clusters : AI Cluster : (”artificial intelligence” OR automation OR ”machine learning” OR ”large language model” OR LLM) Emotion Cluster : (”emotional intelligence” OR empathy OR ”emotional labor” OR ”feeling economy” OR ”care economy” OR ”social intelligence”) Labor Cluster : (job OR occupation OR workforce OR employment OR labor OR upskill OR reskill) A sample Boolean query (adapted for each database) was : (AI cluster) AND (Emotion cluster) AND (Labor cluster) . Full search logs are in Appendix B. D. Study Selection Two reviewers independently screened all records. In phase one, titles and abstracts were screened (n = 448). In phase two, full texts of potentially relevant papers were assessed (n = 312). Disagreements were resolved by consensus or a third reviewer. Reasons for exclusion at the full-text stage were recorded. The entire selection process is documented in the PRISMA flow diagram ( Fig. 1 ). E. Data Extraction and Quality Assessment Two reviewers independently extracted data into a stan- dardized template (id, title, authors, year, type, scope, key findings, quality score, codes). Conflicts were reconciled through discussion. Methodological quality was appraised using study- appropriate tools. Empirical studies were assessed using the Mixed Methods Appraisal Tool (MMAT). Conceptual papers and reviews were assessed via an adapted checklist for theoretical rigor. Gray literature was appraised using an adapted AACODS checklist (Authority, Accuracy, Coverage, Objectivity, Date, Significance). F. Data Synthesis We employed a mixed-methods synthesis. Quantitative : Descriptive analysis summarized study characteristics. Bibliometric analysis (publication trends, keyword co-occurrence) was conducted to map the literature. Qualitative : Thematic analysis (using NVivo) identified recurring mechanisms, occupational themes, and policy recommendations. Integrative : Findings were triangulated to build a causal loop diagram ( Fig. 2 ) and an occupational transition model ( Fig. 3 ) to visualize the dynamics of the ”feeling economy.” III. RESULTS A. Characteristics of Included Studies The 58 included sources comprised 32 peer-reviewed ar- ticles, 12 institutional reports (e.g., OECD, ILO), and 14 gray literature sources (e.g., preprints, think-tank reports). A truncated sample of key studies is presented in Table I. B. Bibliometric Analysis Publication trends show a significant spike in interest post−2018, with 40% of included sources published between 2021 and 2025. A co-citation network analysis (Appendix D ) identified Huang & Rust [ 13 ] as a central, highly cited node. Keyword co-occurrence analysis revealed strong clusters linking ”AI automation” with ”skills” and ”reskilling,” and a separate, emerging cluster linking ”emotional intelligence” with ”care economy” and ”healthcare.” C. Thematic Synthesis Qualitative thematic coding revealed three major themes : Mechanisms of Displacement and Complementarity : Sources confirmed AI links to intellectual task automa- tion (e.g., 33% exposure in Arab States [ 15 ] ). However, this often leads to task reorganization rather than net job loss [ 17 ], as AI complements human skills in creativity, judgment, and empathy [ 23 ]. The Role of EI and Emotional Labor : EI was consistently defined using the Goleman model (self- awareness, empathy, etc.) [ 10 ]. Emotional labor was defined as the management of feeling to create a public display [ 11 ]. Expanding Occupations and Interventions : A strong consensus identified the care economy (healthcare, edu- cation, social work) as a primary area for expansion [ 18 ]. Effective interventions focused on systemic upskilling [ 20 ] and ethical regulation like the EU AI Act [ 8 ]. D. Conceptual Models Based on the synthesis, we developed conceptual models to illustrate the dynamics. Figure 2 presents a causal loop diagram of the feedback mechanisms, and Fig. 3 models the occupational transition pathways. IV. DISCUSSION The findings strongly substantiate the ”feeling economy” hypothesis [ 13 ]. The systematic review confirms that while AI is displacing routine and non-routine cognitive tasks, it shows no sign of replicating genuine empathy or high-level social cognition [ 7 ]. This creates an ”empathy bottleneck,” driving demand for relational jobs. A. Interpretation of Findings The mechanisms identified—task complementarity [ 3 ] and productivity-driven demand loops—align with modern labor economics. The lack of net employment decline [ 17 ] suggests that, thus far, AI’s role has been one of task reorganization rather than wholesale job destruction. This supports the view of AI as a complement to human labor, augmenting human capabilities [ 23 ] while taking over automatable sub-tasks. The bibliometric clustering of ”emotional intelligence” with ”care economy” provides strong, data-driven evidence for this shift. B. Theoretical and Practical Implications Theoretically, our findings challenge purely displacement- focused models (e.g. , [ 9 ] ) by empirically supporting a more nuanced, task-based complementarity model [ 1 ]. Our causal loop model ( Fig. 2 ) visualizes these dynamics. Practically, the implications for education and policy are profound. Education systems must pivot from a curriculum focused purely on cognitive skills to a hybrid model emphasizing EI, systemic critical thinking, and human-AI collaboration [ 25 ]. Policymakers must create robust frameworks for lifelong learning and rapid reskilling to manage the transition and mitigate inequality. C. Limitations This review has limitations. First, the evidence base is skewed toward OECD countries; future work must examine these dynamics in the Global South. Second, potential publi- cation bias may exist, although our inclusion of gray literature attempts to mitigate this. Third, the ”feeling economy” is an emerging concept; longitudinal studies on new EI-focused job roles are not yet available. Finally, our conceptual models are based on synthesis and require empirical validation. V. CONCLUSION AI is automating intellectual work, but it is not making human skills obsolete. Instead, it is catalyzing a shift toward a ”feeling economy” where empathy, emotional intelligence , and relational skills are the new drivers of economic value. Our systematic review and conceptual models illustrate the mechanisms of this transition, highlighting a future where human-AI collaboration is standard. The primary challenge is not mass unemployment, but mass reskilling. This paper provides a robust, evidence-based call to action for proactive, human-centered policies focused on EI upskilling and ethical AI regulation to ensure this transition is equitable and positive. Declarations Author Contribution Mohamed Ibrahim Abdelaziz conceived the study, designed the systematic review methodology, conducted the literature search and data extraction, performed the mixed-methods synthesis, developed the conceptual and causal loop models, and wrote and revised the full manuscript. The author approves the final version and is fully accountable for the work. References Acemoglu D, Restrepo P (2018) Artificial intelligence, automation and work, NBER Working Paper, 24196 Acemoglu D, Restrepo P (2022) Tasks, automation, and the rise of U.S. wage inequality, Econometrica, vol. 90, no. 5, pp. 1975–2016 Autor DH (2015) Why are there still so many jobs? The history and future of workplace automation. J Economic Perspect 29(3):3–30 Broecke S (2023) Artificial intelligence and labour market matching, OECD Social, Employment and Migration Working Papers, 280 Brynjolfsson E, McAfee A (2018) The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company Brynjolfsson E, Rock D, Syverson C (2019) Artificial intelligence and the modern productivity paradox, NBER Working Paper, 24001 Damasio A (2021) Feeling & knowing: Making minds conscious. Pantheon European, Commission (2024) TheAIAct, [Online]. Available: https: //digital- strategy.ec.europa.eu/en/policies/regulatory-framework-ai Frey CB, Osborne MA (2017) The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, vol. 114, pp. 254–280 Goleman D (1995) Emotional intelligence: Why it can matter more than IQ. Bantam Books Hochschild AR (1983) The managed heart: Commercialization of human feeling. University of California Press Huang M-H, Rust RT (2018) Artificial intelligence in service. J Service Res 21(2):155–172 Huang M-H et al (2019) The feeling economy: Managing in the next generation of AI. Calif Manag Rev 61(4):43–65 Huang M-H, Rust RT (2021) A strategic framework for artificial intelligence in service. J Acad Mark Sci 49(1):30–50 ILO, Generative (2024) AI and jobs: A global analysis of potential effects on job quantity and quality, ILO Working Paper, 96 ILO, World Employment and Social Outlook (2025) Trends 2025. International Labour Organization Lane M, Saint-Martin A (2021) The impact of AI on the labour market: What do we know so far? OECD Social, Employment and Migration Working Papers, 256 OECD (2021) The future of the care economy. OECD Publishing OECD, Outlook OECDE (2023) : Artificial intelligence and the labour market. OECD Publishing, 2023 OECD, OECD Skills Outlook (2023) : Skills for a resilient green and digital future. OECD Publishing, 2023 Page MJ et al (2021) The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 372:n71 Salovey P, Mayer JD (1990) Emotional intelligence. Imagination Cognition Personality 9(3):185–211 Squicciarini M, Nachtigall M (2021) Demand for AI skills in jobs: Evidence from online job postings, OECD Science, Technology and Industry Working Papers, 2021/03 Tegmark M (2017) Life 3.0: Being human in the age of artificial intelligence. Knopf World Economic Forum, The future of jobs report 2023. World Eco- nomic Forum, 2023. Additional Declarations No competing interests reported. Supplementary Files AppendixA.docx 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. 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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-7971227","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":541259642,"identity":"48b76d5b-b647-48b5-814c-1765489f16af","order_by":0,"name":"Mohamed Ibrahim Abdelaziz","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Mohamed","middleName":"Ibrahim","lastName":"Abdelaziz","suffix":""}],"badges":[],"createdAt":"2025-10-28 17:09:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7971227/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7971227/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95602799,"identity":"1bc0d3cc-30d3-4741-91fc-6f3368ca1cc0","added_by":"auto","created_at":"2025-11-11 06:14:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86970,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA 2020 Flow Diagram of Study Selection. This diagram illustrates the flow of information through the different phases of the systematic review, from initial identification to final inclusion in the synthesis.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7971227/v1/43bde7b5bbab4e9429cf2693.png"},{"id":95602802,"identity":"1b6d4931-a165-4816-b734-cdada5556c09","added_by":"auto","created_at":"2025-11-11 06:14:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37251,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Model: Causal Loop Diagram of AI Automation and EI Demand. R1 (Reinforcing) shows AI productivity boosting EI demand. B1 (Balancing) models wage normalization. B2 (Balancing) models policy/social friction slowing adoption due to displacement.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7971227/v1/6da252763bbce35a1e269941.png"},{"id":95656151,"identity":"b7f8497b-1ad2-4ca8-bba6-c2aa6606bb4a","added_by":"auto","created_at":"2025-11-11 16:17:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":46836,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Model: Occupational Transition from Cognitive to Relational Roles. Illustrates the skill shift from automatable cognitive tasks toward roles requiring high EI, facilitated by a reskilling pipeline.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7971227/v1/c9edceaed5fc6e08e45ccdb2.png"},{"id":95660181,"identity":"ad326e9e-390c-4332-914e-3bff8c6123f8","added_by":"auto","created_at":"2025-11-11 16:31:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":584767,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7971227/v1/0fa57971-57c1-459e-ab83-8602fca193c4.pdf"},{"id":95602800,"identity":"c0248913-7f37-4cb2-b0a2-47aabab99eb5","added_by":"auto","created_at":"2025-11-11 06:14:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16889,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.docx","url":"https://assets-eu.researchsquare.com/files/rs-7971227/v1/9898fb2a951041f6c639caef.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Feeling Economics: AI, Empathy, and the Future of Labor– A Systematic Review and Conceptual Model","fulltext":[{"header":"I. INTRODUCTION","content":"\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThe rise of artificial intelligence (AI) and machine learning technologies has sparked intense debate on labor market futures\u003c/span\u003e [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eWhile earlier automation waves targeted routine manual tasks, contemporary AI excels at non-routine cognitive work, such as data analysis, report generation, and decision-making\u003c/span\u003e [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThis evolution raises profound questions about the future of human roles in an AI-dominated economy. The \u0026rdquo;feeling economy\u0026rdquo; concept, introduced by Huang and Rust\u003c/span\u003e [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e\u0026ndash;\u003c/span\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eposits that as AI masters analytical think- ing, economic value will pivot to uniquely human strengths: empathy, emotional intelligence (EI), and complex relational interactions. This review rigorously examines this posited shift through a systematic lens, integrating empirical evidence on AI displacement with theoretical frameworks on emotional labor\u003c/span\u003e [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eDrawing from labor economics\u003c/span\u003e [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eAI studies\u003c/span\u003e [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eand organizational psychology\u003c/span\u003e [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003ewe argue that AI will amplify demand for EI-centered jobs. However, the outcomes of this transition are not predetermined; they depend critically on\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003emechanisms like task complementarity\u003c/span\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eand the design of policy interventions\u003c/span\u003e [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThis review addresses a significant gap in the literature by providing an interdisciplinary synthesis and developing a conceptual model to guide stakeholders in education, industry, and policy\u003c/span\u003e [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eThis review makes three primary contributions. First, we provide a timely and comprehensive synthesis of empirical evidence, moving beyond speculation to assess observable labor market shifts. Second, we integrate disparate the- oretical fields (labor economics, AI studies, organizational psychology) to propose a novel conceptual model (\u003c/span\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e). Third, we develop testable scenarios and actionable policy recommendations grounded in this synthesis, providing a clear agenda for future research and public policy.\u003c/span\u003e\u003c/p\u003e"},{"header":"II.\tMETHODS","content":"\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eThis review follows the PRISMA 2020 guidelines\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. \u003cspan class=\"SmallCaps\"\u003eThe full protocol, summarized here, was registered (details in Appendix A) to ensure transparency and rigor.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eA. Objectives and Research Questions\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eThe primary objective is to assess evidence on how accelerating AI automation reshapes labor markets toward a \u0026rdquo;feeling economy.\u0026rdquo; Our Primary Research Question (RQ) is: How will AI-driven automation of intellectual tasks change the demand for empathy- and EI-centered occupations over the next 10\u0026ndash;25 years? Secondary RQs include: (a) What empirical links exist between AI and declines in traditional intellectual jobs? (b) How are EI and emotional labor defined and measured in this context? (c) Which occupations are likely to expand due to high empathy requirements? (d) What policy interventions can facilitate positive transitions?\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eB. Eligibility Criteria\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eWe included empirical studies, theoretical papers, reviews, and policy reports (peer-reviewed, preprints, white papers) published from 2010 to October 1, 2025. Sources had to address both AI/automation of intellectual tasks and its implications for occupations involving emotional labor or EI. This covered research on job displacement, skill shifts, occupational forecasts, and the \u0026rdquo;care\u0026rdquo; or \u0026rdquo;feeling\u0026rdquo; economy. English-language sources were prioritized. We excluded non- systematic opinion pieces, speculative media, duplicates, and inaccessible sources.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eTable I\u0026nbsp;\u003cspan class=\"SmallCaps\"\u003eSample of Included Studies\u003c/span\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eID\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eAuthor(s)\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eYear\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eType\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eKey Findings\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eCodes\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eHuang et al.\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e2019\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eTheoretical\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eAI shifts to thinking; humans to\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eAI automation, EI\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003efeeling roles.\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eLane, Saint-Martin\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e2021\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eReview\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eNo net job loss; task reorganiza-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eLabor changes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003etion.\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eSquicciarini, Nachtigall\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e2021\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eEmpirical\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eRising AI skills demand; comple-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eAI automation\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003ements like creativity.\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e4\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eBroecke\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e2023\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eReview\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eAI tools efficiency; bias risks.\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eLabor changes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eILO\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e2025\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eEmpirical\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eAI displacement in regions; green\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eLabor changes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003ejobs.\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e6\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eFrey, Osborne\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e2017\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eEmpirical\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e47% of US jobs at high risk of\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eAI automation\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eautomation.\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e7\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eAutor, D.\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e2015\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eTheoretical\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eTask complementarity; automation\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eLabor changes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003ecreates new tasks.\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eGoleman, D.\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e1995\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eTheoretical\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eDefines EI and its importance in\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eEI\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003elife/work.\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e9\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eHochschild, A.\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e1983\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eTheoretical\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eIntroduces \u0026rsquo;emotional labor\u0026rsquo; con-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eEI\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003ecept.\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e10\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eWEF\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e2023\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eReport\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eAnalytical/creative skills rising; AI\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eAI automation\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003ecreates new roles.\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e11\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eAcemoglu, Restrepo\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e2018\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eEmpirical\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eDisplacement and productivity ef-\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eLabor changes\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003efects of automation.\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e12\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eBrynjolfsson et al.\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e2019\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eTheoretical\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eLag in productivity gains from AI\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eAI automation\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eadoption.\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e13\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eDamasio, A.\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003e2021\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eTheoretical\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eArgues for the inseparability of\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eEI\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eemotion/reason.\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eC. Information Sources and Search Strategy\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eWe conducted comprehensive searches of 13 academic and gray literature databases: Web of Science, Scopus, PubMed, PsycINFO, EconLit, IEEE Xplore, Google Scholar, arXiv, SSRN, RePEc, OECD iLibrary, ILO, and the World Bank.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eSearch terms combined three concept clusters\u003c/span\u003e:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cspan class=\"BoldSmallCaps\"\u003eAI Cluster\u003c/span\u003e: \u003cspan class=\"SmallCaps\"\u003e(\u0026rdquo;artificial intelligence\u0026rdquo; OR automation OR \u0026rdquo;machine learning\u0026rdquo; OR \u0026rdquo;large language model\u0026rdquo; OR LLM)\u003c/span\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cspan class=\"BoldSmallCaps\"\u003eEmotion Cluster\u003c/span\u003e: \u003cspan class=\"SmallCaps\"\u003e(\u0026rdquo;emotional intelligence\u0026rdquo; OR empathy OR \u0026rdquo;emotional labor\u0026rdquo; OR \u0026rdquo;feeling economy\u0026rdquo; OR \u0026rdquo;care economy\u0026rdquo; OR \u0026rdquo;social intelligence\u0026rdquo;)\u003c/span\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cspan class=\"BoldSmallCaps\"\u003eLabor Cluster\u003c/span\u003e: \u003cspan class=\"SmallCaps\"\u003e(job OR occupation OR workforce OR employment OR labor OR upskill OR reskill)\u003c/span\u003e\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eA sample Boolean query (adapted for each database) was\u003c/span\u003e: \u003cstrong\u003e(AI cluster) AND (Emotion cluster) AND (Labor cluster)\u003c/strong\u003e. \u003cspan class=\"SmallCaps\"\u003eFull search logs are in Appendix B.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eD. Study Selection\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eTwo reviewers independently screened all records. In phase one, titles and abstracts were screened (n\u0026thinsp;=\u0026thinsp;448). In phase two, full texts of potentially relevant papers were assessed (n\u0026thinsp;=\u0026thinsp;312). Disagreements were resolved by consensus or a third reviewer. Reasons for exclusion at the full-text stage were recorded. The entire selection process is documented in the PRISMA flow diagram (\u003c/span\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cspan class=\"SmallCaps\"\u003e).\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eE. Data Extraction and Quality Assessment\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eTwo reviewers independently extracted data into a stan- dardized template (id, title, authors, year, type, scope, key\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003efindings, quality score, codes). Conflicts were reconciled through discussion.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eMethodological quality was appraised using study- appropriate tools. Empirical studies were assessed using the Mixed Methods Appraisal Tool (MMAT). Conceptual papers and reviews were assessed via an adapted checklist for theoretical rigor. Gray literature was appraised using an adapted AACODS checklist (Authority, Accuracy, Coverage, Objectivity, Date, Significance).\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eF. Data Synthesis\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eWe employed a mixed-methods synthesis.\u003c/span\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cspan class=\"BoldSmallCaps\"\u003eQuantitative\u003c/span\u003e: \u003cspan class=\"SmallCaps\"\u003eDescriptive analysis summarized study characteristics. Bibliometric analysis (publication trends, keyword co-occurrence) was conducted to map the literature.\u003c/span\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cspan class=\"BoldSmallCaps\"\u003eQualitative\u003c/span\u003e: \u003cspan class=\"SmallCaps\"\u003eThematic analysis (using NVivo) identified recurring mechanisms, occupational themes, and policy recommendations.\u003c/span\u003e\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cspan class=\"BoldSmallCaps\"\u003eIntegrative\u003c/span\u003e: \u003cspan class=\"SmallCaps\"\u003eFindings were triangulated to build a causal loop diagram (\u003c/span\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cspan class=\"SmallCaps\"\u003e) and an occupational transition model (\u003c/span\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cspan class=\"SmallCaps\"\u003e) to visualize the dynamics of the \u0026rdquo;feeling economy.\u0026rdquo;\u003c/span\u003e\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e"},{"header":"III. RESULTS","content":"\u003cp\u003eA. Characteristics of Included Studies\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eThe 58 included sources comprised 32 peer-reviewed ar- ticles, 12 institutional reports (e.g., OECD, ILO), and 14 gray literature sources (e.g., preprints, think-tank reports). A truncated sample of key studies is presented in Table I.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eB. Bibliometric Analysis\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003ePublication trends show a significant spike in interest post\u0026minus;2018, with 40% of included sources published between 2021 and 2025. A co-citation network analysis (Appendix\u003c/span\u003e D\u003cspan class=\"SmallCaps\"\u003e) identified Huang \u0026amp; Rust\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e] \u003cspan class=\"SmallCaps\"\u003eas a central, highly cited node. Keyword co-occurrence analysis revealed strong clusters linking \u0026rdquo;AI automation\u0026rdquo; with \u0026rdquo;skills\u0026rdquo; and \u0026rdquo;reskilling,\u0026rdquo; and a separate, emerging cluster linking \u0026rdquo;emotional intelligence\u0026rdquo; with \u0026rdquo;care economy\u0026rdquo; and \u0026rdquo;healthcare.\u0026rdquo;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eC. Thematic Synthesis\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eQualitative thematic coding revealed three major themes\u003c/span\u003e:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cspan class=\"BoldSmallCaps\"\u003eMechanisms of Displacement and Complementarity\u003c/span\u003e: \u003cspan class=\"SmallCaps\"\u003eSources confirmed AI links to intellectual task automa- tion (e.g., 33% exposure in Arab States\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003cspan class=\"SmallCaps\"\u003e). However, this often leads to task reorganization rather than net job loss\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e], \u003cspan class=\"SmallCaps\"\u003eas AI complements human skills in creativity, judgment, and empathy\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cspan class=\"BoldSmallCaps\"\u003eThe Role of EI and Emotional Labor\u003c/span\u003e: \u003cspan class=\"SmallCaps\"\u003eEI was consistently defined using the Goleman model (self- awareness, empathy, etc.)\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. \u003cspan class=\"SmallCaps\"\u003eEmotional labor was defined as the management of feeling to create a public display\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cspan class=\"BoldSmallCaps\"\u003eExpanding Occupations and Interventions\u003c/span\u003e: \u003cspan class=\"SmallCaps\"\u003eA strong consensus identified the care economy (healthcare, edu- cation, social work) as a primary area for expansion\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. \u003cspan class=\"SmallCaps\"\u003eEffective interventions focused on systemic upskilling\u003c/span\u003e\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e] \u003cspan class=\"SmallCaps\"\u003eand ethical regulation like the EU AI Act\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eD. Conceptual Models\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eBased on the synthesis, we developed conceptual models to illustrate the dynamics.\u003c/span\u003e Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cspan class=\"SmallCaps\"\u003epresents a causal loop diagram of the feedback mechanisms, and\u003c/span\u003e Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cspan class=\"SmallCaps\"\u003emodels the occupational transition pathways.\u003c/span\u003e\u003c/p\u003e"},{"header":"IV. DISCUSSION","content":"\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eThe findings strongly substantiate the \u0026rdquo;feeling economy\u0026rdquo; hypothesis\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. \u003cspan class=\"SmallCaps\"\u003eThe systematic review confirms that while\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eAI is displacing routine and non-routine cognitive tasks, it shows no sign of replicating genuine empathy or high-level social cognition\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. \u003cspan class=\"SmallCaps\"\u003eThis creates an \u0026rdquo;empathy bottleneck,\u0026rdquo; driving demand for relational jobs.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eA. Interpretation of Findings\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eThe mechanisms identified\u0026mdash;task complementarity\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e] \u003cspan class=\"SmallCaps\"\u003eand productivity-driven demand loops\u0026mdash;align with modern labor economics. The lack of net employment decline\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e] \u003cspan class=\"SmallCaps\"\u003esuggests that, thus far, AI\u0026rsquo;s role has been one of task reorganization rather than wholesale job destruction. This supports the view of AI as a complement to human labor, augmenting human capabilities\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] \u003cspan class=\"SmallCaps\"\u003ewhile taking over automatable sub-tasks. The bibliometric clustering of \u0026rdquo;emotional intelligence\u0026rdquo; with \u0026rdquo;care economy\u0026rdquo; provides strong, data-driven evidence for this shift.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eB. Theoretical and Practical Implications\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eTheoretically, our findings challenge purely displacement- focused models (e.g.\u003c/span\u003e, [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003cspan class=\"SmallCaps\"\u003e) by empirically supporting a more nuanced, task-based complementarity model\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. \u003cspan class=\"SmallCaps\"\u003eOur causal loop model (\u003c/span\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cspan class=\"SmallCaps\"\u003e) visualizes these dynamics. Practically, the\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eimplications for education and policy are profound. Education systems must pivot from a curriculum focused purely on cognitive skills to a hybrid model emphasizing EI, systemic critical thinking, and human-AI collaboration\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. \u003cspan class=\"SmallCaps\"\u003ePolicymakers must create robust frameworks for lifelong learning and rapid reskilling to manage the transition and mitigate inequality.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eC. Limitations\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"SmallCaps\"\u003eThis review has limitations. First, the evidence base is skewed toward OECD countries; future work must examine these dynamics in the Global South. Second, potential publi- cation bias may exist, although our inclusion of gray literature attempts to mitigate this. Third, the \u0026rdquo;feeling economy\u0026rdquo; is an emerging concept; longitudinal studies on new EI-focused job roles are not yet available. Finally, our conceptual models are based on synthesis and require empirical validation.\u003c/span\u003e\u003c/p\u003e"},{"header":"V. CONCLUSION","content":"\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eAI is automating intellectual work, but it is not making human skills obsolete. Instead, it is catalyzing a shift toward a \u0026rdquo;feeling economy\u0026rdquo; where empathy, emotional intelligence\u003c/span\u003e,\u003c/p\u003e\u003cp\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eand relational skills are the new drivers of economic value. Our systematic review and conceptual models illustrate the mechanisms of this transition, highlighting a future where human-AI collaboration is standard. The primary challenge is not mass unemployment, but mass reskilling. This paper provides a robust, evidence-based call to action for proactive, human-centered policies focused on EI upskilling and ethical AI regulation to ensure this transition is equitable and positive.\u003c/span\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMohamed Ibrahim Abdelaziz conceived the study, designed the systematic review methodology, conducted the literature search and data extraction, performed the mixed-methods synthesis, developed the conceptual and causal loop models, and wrote and revised the full manuscript. The author approves the final version and is fully accountable for the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcemoglu D, Restrepo P (2018) Artificial intelligence, automation and work, NBER Working Paper, 24196\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAcemoglu D, Restrepo P (2022) Tasks, automation, and the rise of U.S. wage inequality, Econometrica, vol. 90, no. 5, pp. 1975\u0026ndash;2016\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAutor DH (2015) Why are there still so many jobs? The history and future of workplace automation. J Economic Perspect 29(3):3\u0026ndash;30\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBroecke S (2023) Artificial intelligence and labour market matching, OECD Social, Employment and Migration Working Papers, 280\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrynjolfsson E, McAfee A (2018) The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton \u0026amp; Company\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrynjolfsson E, Rock D, Syverson C (2019) Artificial intelligence and the modern productivity paradox, NBER Working Paper, 24001\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDamasio A (2021) Feeling \u0026amp; knowing: Making minds conscious. Pantheon\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEuropean, Commission (2024) TheAIAct, [Online]. Available: https: //digital-\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003estrategy.ec.europa.eu/en/policies/regulatory-framework-ai\u003c/span\u003e\u003cspan address=\"http://strategy.ec.europa.eu/en/policies/regulatory-framework-ai\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrey CB, Osborne MA (2017) The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, vol. 114, pp. 254\u0026ndash;280\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoleman D (1995) Emotional intelligence: Why it can matter more than IQ. Bantam Books\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHochschild AR (1983) The managed heart: Commercialization of human feeling. 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Imagination Cognition Personality 9(3):185\u0026ndash;211\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSquicciarini M, Nachtigall M (2021) Demand for AI skills in jobs: Evidence from online job postings, OECD Science, Technology and Industry Working Papers, 2021/03\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTegmark M (2017) Life 3.0: Being human in the age of artificial intelligence. Knopf\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Economic Forum, The future of jobs report 2023. World Eco- nomic Forum, 2023.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Artificial intelligence, automation, emotional intelligence, feeling economy, labor market transitions, future of work, systematic review","lastPublishedDoi":"10.21203/rs.3.rs-7971227/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7971227/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs accelerating AI automation displaces routine intellectual and cognitive tasks, the labor market may be fun- damentally reshaping toward a \u0026rdquo;feeling economy\u0026rdquo; that values human-centric skills like empathy, emotional intelligence (EI), and relational acuity. This systematic review synthesizes evi- dence on this transition, addressing critical gaps regarding its mechanisms, occupational shifts, and necessary policy responses. Following PRISMA guidelines, we systematically searched 13 databases (e.g., Google Scholar, OECD iLibrary, ILO) from 2010 to October 2025, screening 448 unique records to include 58 sources (peer-reviewed articles, institutional reports, and gray literature) appraised via MMAT and adapted checklists. Our mixed-methods synthesis reveals that AI displaces specific cognitive tasks but complements human EI, with no net employment decline yet observed; instead, demand is surging for care and relational roles. Thematic analysis identified key mechanisms, including task reorganization and feedback loops where AI- driven productivity boosts demand for human-centered skills. We present a novel conceptual model, visualized as a causal loop diagram, to illustrate these dynamics, showing reinforcing loops for EI upskilling and balancing loops for wage normalization. We conclude that AI is catalyzing a structural shift toward a \u0026rdquo;feeling economy,\u0026rdquo; necessitating proactive, human-centric policies focused on EI upskilling and ethical AI regulation to ensure an equitable transition and mitigate inequality risks.\u003c/p\u003e","manuscriptTitle":"Feeling Economics: AI, Empathy, and the Future of Labor– A Systematic Review and Conceptual Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-11 06:14:37","doi":"10.21203/rs.3.rs-7971227/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":"b3ff8411-75a2-4e6e-97fd-30bedc902b5d","owner":[],"postedDate":"November 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-11T06:14:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-11 06:14:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7971227","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7971227","identity":"rs-7971227","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}