Francisco Ortiz-Vidal

Dr. Francisco Ortiz-Vidal

Facultad de Ciencias Económicas y Administrativas, Universidad Católica de la Santísima Concepción, Concepción, Biobío, Chile
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orcid

Research Focus & Metrics

Academic Career Length

2Years (2025-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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14 April 2026
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FOFrancisco Ortiz-Vidal
Francisco Ortiz-Vidal
ABAlison Adriana Matus Bello
Alison Adriana Matus Bello
IPIsrael Figueroa Pedreros
Israel Figueroa Pedreros
CMCristian Medina-Rojas
Cristian Medina-Rojas
MBMáximo Burgos-Norambuena
Máximo Burgos-Norambuena
MCMarcelo Careaga-Butter
Marcelo Careaga-Butter
INPLASY202640048 Impacts of the use of Generative Artificial Intelligence in doctoral programmes: A systematic review based on knowledge and mobilisation management Keywords: Generative Artificial …

17 October 2025
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MUMabel Urrutia
Mabel Urrutia
AVAlonso Valderrama
Alonso Valderrama
SASusana Araya
Susana Araya
LALuciano Silva Aguayo
Luciano Silva Aguayo
FOFrancisco Ortiz-Vidal
Francisco Ortiz-Vidal
Pedro Salcedo LagosPedro Salcedo Lagos
Pedro Salcedo Lagos
INPLASY2025100064 Emotional Literacy in the Digital Era: Role of AI Tools for Social-emotional training in school populations Keywords: Artificial Intelligence, emotional competencies, social-emoti…


1 September 2026 in Journal of Intelligence
Verified
Journal Article
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MUMabel Urrutia
Mabel Urrutia
AVAlonso Valderrama
Alonso Valderrama
SASusana Araya
Susana Araya
LALuciano Silva Aguayo
Luciano Silva Aguayo
FOFrancisco Ortiz-Vidal
Francisco Ortiz-Vidal
Hipólito MarreroHipólito Marrero
Hipólito Marrero
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’ 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.