AI-based Safety Training Systems for Hispanic Workers in Construction

Hispanic workers represent about one third of the total workforce of the construction industry in the United States. Nevertheless, this demographic has disproportionately higher rates of fatalities and injuries. Each year, over 300 Hispanic workers lose their lives due to construction related incidents. Various efforts have been made to prevent casualties among this population, of which safety training has been found to be the most effective. However, traditional safety training methods have not been found to be adequate, particularly failing to address barriers such as language differences, cultural factors, immigration concerns, and issues related to construction operation skills. A potential solution to effectively address these challenges is to utilize advanced technologies such as Artificial Intelligence (AI) to improve safety training and outcomes for Hispanic workers. The objective of this prospective research is to develop AI-based safety training systems for Hispanic workers which provides personalized and adaptive training for better knowledge retention and engagement through the enhancement of four core aspects such as communication, culture, workers’ rights, and construction operational safety. By addressing these critical challenges, this study designs the foundation for healthier and safer workplace for Hispanic laborers. In the broader context, the proposed intelligent training systems create a platform with comprehensive safety programs that connect vulnerable groups of workers across the U.S. construction industry.

Authors

Publication Details

Journal
CIB Conferences
Published
2025-06-19
DOI
https://doi.org/10.7771/3067-4883.1865
Citations
1
Primary Topic
Occupational Health and Safety Research
Type
article
Field-Weighted Citation Impact
1.54
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI-based Safety Training Systems for Hispanic Workers in Construction

Chien–Ho Ko, Gulzhanat Akhanova
1 citations
CIB Conferences
Occupational Health and Safety Research
1.54
article

AI-based Safety Training Systems for Hispanic Workers in Construction

Chien–Ho Ko, Gulzhanat Akhanova
article en
1 citations

Abstract

Hispanic workers represent about one third of the total workforce of the construction industry in the United States. Nevertheless, this demographic has disproportionately higher rates of fatalities and injuries. Each year, over 300 Hispanic workers lose their lives due to construction related incidents. Various efforts have been made to prevent casualties among this population, of which safety training has been found to be the most effective. However, traditional safety training methods have not been found to be adequate, particularly failing to address barriers such as language differences, cultural factors, immigration concerns, and issues related to construction operation skills. A potential solution to effectively address these challenges is to utilize advanced technologies such as Artificial Intelligence (AI) to improve safety training and outcomes for Hispanic workers. The objective of this prospective research is to develop AI-based safety training systems for Hispanic workers which provides personalized and adaptive training for better knowledge retention and engagement through the enhancement of four core aspects such as communication, culture, workers’ rights, and construction operational safety. By addressing these critical challenges, this study designs the foundation for healthier and safer workplace for Hispanic laborers. In the broader context, the proposed intelligent training systems create a platform with comprehensive safety programs that connect vulnerable groups of workers across the U.S. construction industry.

CIB ConferencesVol. 1(1)
Decent work and economic growth
Openalex Percentile: Top 18%
Occupational Health and Safety Research
1.54
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.