Exploring Students’ Attitudes Toward Artificial Intelligence (AI): Psychometric Validation of AI-Attitude Scale
<p dir="ltr">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, empi...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , |
| Published: |
2025
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | <p dir="ltr">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.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: Sage Open<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1177/21582440251378375" target="_blank">https://dx.doi.org/10.1177/21582440251378375</a></p> |
|---|