Students' Attitude Towards Artificial Intelligence and Its Predictive Factors

The general objective of this research is to investigate students' attitudes towards artificial intelligence (AI) in relation to a series of socio-demographic data such as age, gender, university year, general level of familiarity with technology, and the attitude of the social group. The purpose is to identify the characteristics of students that could serve as predictive factors for their attitude towards AI and to outline directions for strategies aimed at efficiently managing their relationship with AI products. The results, obtained through the application of the ATTARI-12 scale (Stein, et al., 2024) on a sample of 87 students from three faculties in Bucharest and processed using IBM SPSS Statistics v23, showed statistically significant correlations between the socio-demographic data (age, year of study, and social group attitude) and the attitude toward AI, and that these are also factors that predict students' attitudes towards AI by 82%. The results also highlight that the age group 25-34 has the most open attitude toward AI, and that there are significant differences in this attitude depending on the social group attitude. Although the research objective was achieved, due to the small number of subjects, this study represents a pilot study with associated limitations, such as the inability to generalise conclusions. Instead, the findings serve to guide strategies for the efficient management of AI-related interactions while adhering to ethical principles.

Authors

Institutions

Publication Details

Journal
BRAIN BROAD RESEARCH IN ARTIFICIAL INTELLIGENCE AND NEUROSCIENCE
Published
2025-04-06
DOI
https://doi.org/10.70594/brain/16.s1/22
Citations
2
Primary Topic
Education and Learning Interventions
Type
article
Field-Weighted Citation Impact
6.49
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Students' Attitude Towards Artificial Intelligence and Its Predictive Factors

Alina Costin, Roxana Maier, Ștefania Cristina Ghiocanu, Ioana Simion
2 citations
BRAIN BROAD RESEARCH IN ARTIFICIAL INTELLIGENCE AND NEUROSCIENCE
Education and Learning Interventions
6.49
article

Students' Attitude Towards Artificial Intelligence and Its Predictive Factors

Alina Costin, Roxana Maier, Ștefania Cristina Ghiocanu, Ioana Simion
article en
2 citations

Abstract

The general objective of this research is to investigate students' attitudes towards artificial intelligence (AI) in relation to a series of socio-demographic data such as age, gender, university year, general level of familiarity with technology, and the attitude of the social group. The purpose is to identify the characteristics of students that could serve as predictive factors for their attitude towards AI and to outline directions for strategies aimed at efficiently managing their relationship with AI products. The results, obtained through the application of the ATTARI-12 scale (Stein, et al., 2024) on a sample of 87 students from three faculties in Bucharest and processed using IBM SPSS Statistics v23, showed statistically significant correlations between the socio-demographic data (age, year of study, and social group attitude) and the attitude toward AI, and that these are also factors that predict students' attitudes towards AI by 82%. The results also highlight that the age group 25-34 has the most open attitude toward AI, and that there are significant differences in this attitude depending on the social group attitude. Although the research objective was achieved, due to the small number of subjects, this study represents a pilot study with associated limitations, such as the inability to generalise conclusions. Instead, the findings serve to guide strategies for the efficient management of AI-related interactions while adhering to ethical principles.

BRAIN BROAD RESEARCH IN ARTIFICIAL INTELLIGENCE AND NEUROSCIENCEVol. 16(1, Sup. 1)
Aurel Vlaicu University of Arad (RO), Bucharest University of Economic Studies (RO)
Openalex Percentile: Top 4%
Education and Learning Interventions
6.49
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.