Constructing a Scale for Students’ Attitudes Toward AI-‎Enhanced Project-Based Learning ‏
ALHarthy, S., Al-Kharusi, H., Khalaf, M. (2025). Constructing a Scale for Students’ Attitudes Toward AI-‎Enhanced Project-Based Learning ‏. EKB Journal Management System, 9(49), 769-790. doi: 10.21608/jasep.2025.429663
Salim ALHarthy; Hussain Al-Kharusi; Mustafa Ali ‎‎ Khalaf. "Constructing a Scale for Students’ Attitudes Toward AI-‎Enhanced Project-Based Learning ‏". EKB Journal Management System, 9, 49, 2025, 769-790. doi: 10.21608/jasep.2025.429663
ALHarthy, S., Al-Kharusi, H., Khalaf, M. (2025). 'Constructing a Scale for Students’ Attitudes Toward AI-‎Enhanced Project-Based Learning ‏', EKB Journal Management System, 9(49), pp. 769-790. doi: 10.21608/jasep.2025.429663
ALHarthy, S., Al-Kharusi, H., Khalaf, M. Constructing a Scale for Students’ Attitudes Toward AI-‎Enhanced Project-Based Learning ‏. EKB Journal Management System, 2025; 9(49): 769-790. doi: 10.21608/jasep.2025.429663

Constructing a Scale for Students’ Attitudes Toward AI-‎Enhanced Project-Based Learning ‏

المجلة العربية للعلوم التربوية والنفسية
Volume 9, Issue 49, June 2025, Page 769-790  XML PDF (853.06 K)
Document Type: المقالة الأصلية
DOI: 10.21608/jasep.2025.429663
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Authors
Salim ALHarthy1; Hussain Al-Kharusi2; Mustafa Ali ‎‎ Khalaf3
1PhD Candidate in Measurement and Evaluation, Sultan Qaboos ‎University, Oman
2Professor in Measurement and Evaluation, Sultan Qaboos ‎University, Oman
3Associate Professor in Educational Psychology, Sultan Qaboos ‎University
Abstract
The study aimed to develop a Guttman scale and test its validity to measure students' attitudes toward AI-enhanced project-based learning in higher education. The scale went through a systematic process involving 300 university students (176 female, 124 male) from the Arts and Humanities, Applied Sciences and Engineering disciplines. The process began with an expert review (n=10) and content validation using (CVR) index. It initially resulted in a 7-item scale. The first phase of analysis showed strong psychometric properties, exceeding recommended thresholds, with Reproducibility (0.96) and Scalability (0.95) coefficients. However, exploratory factor analysis revealed structural complexities which required refinement. The final 5-item scale showed enhanced psychometric properties, with improved Reproducibility (0.98) and Scalability (0.97) coefficients. The scale demonstrated a clear unifactorial structure which accounted for 60.785% of the variance. Although the developed scale provides a psychometrically robust tool for measuring students’ attitudes toward AI-enhanced project-based learning, further validation across different populations is recommended for broader generalizability.
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