Disparities in Artificial Intelligence Service Adoption:Examining Gender, Age, and Education Gaps in the AI Era

The rapid proliferation of artificial intelligence services has raised critical questions about whether new forms of digital inequality are emerging. This study examines demographic disparities in AI service adoption, focusing on gender, age, and education as key predictors of the digital divide in the AI era. Analyzing data from a nationally representative survey of South Korean adults (N = 4,360), we tested hypotheses regarding main and interaction effects of demographic variables on AI service usage frequency and usage intention. Results revealed minimal gender gaps but substantial age and education divides. Age emerged as the strongest predictor, with seniors exhibiting significantly lower adoption rates. Notably, education moderated age effects differently for behavioral versus attitudinal outcomes, revealing complex mechanisms underlying demographic inequalities in AI service adoption.

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Publication Details

Journal
CityU Scholars
Published
2025-12-31
DOI
https://doi.org/10.23388/jdes.2025.3.2.006
Primary Topic
Technology Use by Older Adults
Type
article
Field-Weighted Citation Impact
0.00
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article

Disparities in Artificial Intelligence Service Adoption:Examining Gender, Age, and Education Gaps in the AI Era

Hyunyim Park, Ki Joon Kim
CityU Scholars
Technology Use by Older Adults
article

Disparities in Artificial Intelligence Service Adoption:Examining Gender, Age, and Education Gaps in the AI Era

Hyunyim Park, Ki Joon Kim
article en

Abstract

The rapid proliferation of artificial intelligence services has raised critical questions about whether new forms of digital inequality are emerging. This study examines demographic disparities in AI service adoption, focusing on gender, age, and education as key predictors of the digital divide in the AI era. Analyzing data from a nationally representative survey of South Korean adults (N = 4,360), we tested hypotheses regarding main and interaction effects of demographic variables on AI service usage frequency and usage intention. Results revealed minimal gender gaps but substantial age and education divides. Age emerged as the strongest predictor, with seniors exhibiting significantly lower adoption rates. Notably, education moderated age effects differently for behavioral versus attitudinal outcomes, revealing complex mechanisms underlying demographic inequalities in AI service adoption.

CityU Scholars
Hong Kong Polytechnic University (HK)
Quality Education
Openalex Percentile: Top 10%
Technology Use by Older Adults
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