Exploring Students’ Attitudes Toward Artificial Intelligence (AI): Psychometric Validation of AI-Attitude Scale

As Artificial Intelligence (AI) becomes increasingly integrated into educational settings, students’ attitudes toward AI significantly influence their willingness to adopt and engage with these emerging technologies. Although interest in AI education is on the rise, empirical research exploring students’ attitudes especially from a gender perspective remains scarce. To bridge this gap, the present study developed and validated a scale grounded in UNESCO’s AI education guidelines to measure high school students’ attitudes toward AI. A total of 553 students from grades 10, 11, and 12 participated in a sequential, three-phase study that included exploratory and confirmatory factor analyses, followed by gender-based comparisons. The analysis confirmed a robust three-factor structure encompassing Determination, Exploration, and Collaboration. Results revealed notable gender differences: male students scored significantly higher in the Determination and Exploration dimensions, while no significant gender gap was observed in Collaboration. Effect size estimates indicated small to moderate practical significance, underscoring the subtle yet meaningful nature of these differences. These findings emphasize the importance of fostering inclusive, gender-responsive AI education practices that ensure equitable engagement for all learners. The validated scale offers a reliable tool for assessing students’ attitudes toward AI, while the proposed framework provides a foundation for designing targeted pedagogical interventions. By identifying specific areas of disparity and offering strategies to address them, this study contributes to both theoretical advancements and practical improvements in AI education, ultimately supporting the creation of equitable learning environments that empower every student to thrive in an AI-driven future.

Authors

Institutions

Publication Details

Journal
SAGE Open
Published
2025-10-01
DOI
https://doi.org/10.1177/21582440251378375
Citations
4
Primary Topic
Digital literacy in education
Type
article
Field-Weighted Citation Impact
12.98
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring Students’ Attitudes Toward Artificial Intelligence (AI): Psychometric Validation of AI-Attitude Scale

Nitha Siby, Almaas Sultana, Nafilah Abdul Latheef, Zubair Ahmad
4 citations
SAGE Open
Digital literacy in education
12.98
article

Exploring Students’ Attitudes Toward Artificial Intelligence (AI): Psychometric Validation of AI-Attitude Scale

Nitha Siby, Almaas Sultana, Nafilah Abdul Latheef, Zubair Ahmad
article en
4 citations

Abstract

As Artificial Intelligence (AI) becomes increasingly integrated into educational settings, students’ attitudes toward AI significantly influence their willingness to adopt and engage with these emerging technologies. Although interest in AI education is on the rise, empirical research exploring students’ attitudes especially from a gender perspective remains scarce. To bridge this gap, the present study developed and validated a scale grounded in UNESCO’s AI education guidelines to measure high school students’ attitudes toward AI. A total of 553 students from grades 10, 11, and 12 participated in a sequential, three-phase study that included exploratory and confirmatory factor analyses, followed by gender-based comparisons. The analysis confirmed a robust three-factor structure encompassing Determination, Exploration, and Collaboration. Results revealed notable gender differences: male students scored significantly higher in the Determination and Exploration dimensions, while no significant gender gap was observed in Collaboration. Effect size estimates indicated small to moderate practical significance, underscoring the subtle yet meaningful nature of these differences. These findings emphasize the importance of fostering inclusive, gender-responsive AI education practices that ensure equitable engagement for all learners. The validated scale offers a reliable tool for assessing students’ attitudes toward AI, while the proposed framework provides a foundation for designing targeted pedagogical interventions. By identifying specific areas of disparity and offering strategies to address them, this study contributes to both theoretical advancements and practical improvements in AI education, ultimately supporting the creation of equitable learning environments that empower every student to thrive in an AI-driven future.

SAGE OpenVol. 15(4)
Qatar University (QA)
Openalex Percentile: Top 2%
Digital literacy in education
12.98
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.