Personality Traits and Attitudes Towards Artificial Intelligence Among University Students

This study explored the relationship between personality traits and attitudes toward artificial intelligence (AI), as well as the potential predictive effects of demographic factors, including gender, age, education, and prior knowledge of AI. The research was based on the Big Five personality model (using the Ten-Item Personality Inventory scale) and the ATTARI-12 scale to measure AI attitudes. Quantitative correlation research design was followed in the study. Data were collected from 198 participants (using simple random sampling). Correlation analysis was conducted to examine the relationship between personality traits and AI attitudes, and multiple regression analysis was used to assess the predictive effects of demographic variables. The results revealed a significant positive relationship between personality traits and attitudes toward AI, supporting the hypothesis that personality traits influence AI perceptions. However, the predictive effects of gender, age, education, and prior knowledge of AI were not significant. These findings suggest that individual differences, particularly personality traits, play a more critical role in shaping attitudes toward AI than demographic factors. The study contributes to the understanding of AI acceptance and offers implications for designing AI technologies and educational interventions that cater to different personality profiles. Future research should explore additional psychological factors and longitudinal approaches to further understand the dynamics of AI adoption.

Authors

Institutions

Publication Details

Journal
Social science review archives.
Published
2025-05-18
DOI
https://doi.org/10.70670/sra.v3i2.690
Citations
1
Primary Topic
Personality Traits and Psychology
Type
article
Field-Weighted Citation Impact
2.31
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Personality Traits and Attitudes Towards Artificial Intelligence Among University Students

Anila Sadaf Mubashir, Rida Kainaat, Wania Altaf
1 citations
Social science review archives.
Personality Traits and Psychology
2.31
article

Personality Traits and Attitudes Towards Artificial Intelligence Among University Students

Anila Sadaf Mubashir, Rida Kainaat, Wania Altaf
article en
1 citations

Abstract

This study explored the relationship between personality traits and attitudes toward artificial intelligence (AI), as well as the potential predictive effects of demographic factors, including gender, age, education, and prior knowledge of AI. The research was based on the Big Five personality model (using the Ten-Item Personality Inventory scale) and the ATTARI-12 scale to measure AI attitudes. Quantitative correlation research design was followed in the study. Data were collected from 198 participants (using simple random sampling). Correlation analysis was conducted to examine the relationship between personality traits and AI attitudes, and multiple regression analysis was used to assess the predictive effects of demographic variables. The results revealed a significant positive relationship between personality traits and attitudes toward AI, supporting the hypothesis that personality traits influence AI perceptions. However, the predictive effects of gender, age, education, and prior knowledge of AI were not significant. These findings suggest that individual differences, particularly personality traits, play a more critical role in shaping attitudes toward AI than demographic factors. The study contributes to the understanding of AI acceptance and offers implications for designing AI technologies and educational interventions that cater to different personality profiles. Future research should explore additional psychological factors and longitudinal approaches to further understand the dynamics of AI adoption.

Social science review archives.Vol. 3(2)
National University of Modern Languages (PK)
Openalex Percentile: Top 14%
Personality Traits and Psychology
2.31
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.