{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T04:16:47Z","timestamp":1788236207789,"version":"build-2803163510"},"reference-count":71,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Anid\/Fondecyt Exploraci\u00f3n","award":["13220040"],"award-info":[{"award-number":["13220040"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Intell."],"abstract":"<jats:p>Developing emotional competencies has been identified as a key component of personal well-being, school coexistence, and significant learning during childhood and adolescence. The aim of this study was to map and categorise the quantitative scientific evidence available between 2019 and 2024 regarding the use of artificial intelligence technologies and algorithms applied to emotional education in school-age populations. This scoping review followed the PRISMA Extension for Scoping Reviews (PRISMA-ScR) and searched seven databases (Web of Science, Scopus, PubMed, PsycNet, ERIC, IEEE Xplore and the ACM Digital Library). The reviewed studies were published between the years 2019 and 2024. Eighteen studies met the inclusion criteria required for the final analysis. The results show that the most trained competence was machine-based emotional recognition using convolutional neural networks (CNNs). The goal of using them was to provide teacher support, specifically during initial or diagnostic assessments of students\u2019 competencies. However, there remains a gap in its integration into the teaching and training of complex human skills, such as empathy. More longitudinal research is required, as well as more robust experimental designs that go beyond basic emotion models.<\/jats:p>","DOI":"10.3390\/jintelligence14090191","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T03:19:26Z","timestamp":1788232766000},"page":"191","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Decoding Social Cues: A Scoping Review of AI-Supported Emotional Competencies and Their Impact on School-Aged Populations"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8475-1383","authenticated-orcid":false,"given":"Mabel","family":"Urrutia","sequence":"first","affiliation":[{"name":"Facultad de Educaci\u00f3n, Universidad de Concepci\u00f3n, Concepci\u00f3n 4030000, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alonso","family":"Valderrama","sequence":"additional","affiliation":[{"name":"Facultad 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