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

心理学 比例(比率) 验证性因素分析 透视图(图形) 应用心理学 探索性因素分析 桥(图论) 社会心理学 实证研究 高等教育 心理测量学 基础(证据) 探索性研究 情商 移情 可靠性(半导体) 医学教育 经验证据 评定量表 最佳实践
作者
Almaas Sultana,Nafilah Abdul Latheef,Nitha Siby,Zubair Ahmad
出处
期刊:SAGE Open [SAGE Publishing]
卷期号:15 (4) 被引量:5
标识
DOI:10.1177/21582440251378375
摘要

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.
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