Volume 16, Issue 6 And S7 (Artificial Intelligence- In Press 2026)
J Research Health 2026, 16(6 And S7): 1-1 |
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Ethics code: 26.11.2025-184993
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Momeni Shabani S, Darabi F. Development and Psychometric Evaluation of the AI-Based Academic Support Questionnaire for University Students. , J Research Health. 2026; 16 (6 and S7) :1-1
URL: http://jrh.gmu.ac.ir/article-1-3087-en.html
URL: http://jrh.gmu.ac.ir/article-1-3087-en.html
1- Psychological Counseling and Leadership Group, Department of Educational Sciences, Faculty of Education, Istanbul Kültür University, Istanbul, Turkey
2- Department of Public Health, Asadabad School of Medical Sciences, Asadabad, Iran ,fatemedarabi43@yahoo.com
2- Department of Public Health, Asadabad School of Medical Sciences, Asadabad, Iran ,
Abstract: (11 Views)
Background: With the rapid integration of artificial intelligence (AI) into higher education, students increasingly rely on AI-driven tools (e.g., intelligent tutoring systems and large language model–based chatbots) for learning support. However, brief and psychometrically sound instruments for assessing perceived AI-based academic support remain limited. This study aimed to develop and evaluate the psychometric properties of a concise questionnaire designed to measure perceived AI-based academic support among university students.
Methods: This methodological psychometric study generated an initial 20-item pool based on literature review and a three-dimensional conceptual framework (motivational, cognitive/conceptual, and metacognitive support). After expert review, nine items were retained. Content validity was assessed using CVR (Lawshe) and CVI. Face validity was evaluated using qualitative review and Item Impact Score. Reliability was assessed using Cronbach’s alpha coefficients. Construct validity was examined via exploratory factor analysis (EFA), including KMO and Bartlett’s test, using principal component extraction with Varimax rotation. In addition, item-level analysis (corrected item–total correlations and alpha if item deleted), descriptive statistics (skewness/kurtosis), and communalities were computed to provide a complete psychometric report.
Results: Content validity was excellent (CVR = 1.00; S-CVI/Ave = 1.00). Internal consistency was strong for the total scale (Cronbach’s α = 0.881) and subscales (motivational α = 0.981; cognitive/conceptual α = 0.944; metacognitive α = 0.801). Item distributions were approximately normal (|skewness| < 0.12; kurtosis ≈ −1.06 to −0.95). Sampling adequacy was acceptable (KMO = 0.843), and Bartlett’s test was significant (χ² = 2667.37, df = 36, p < 0.001). EFA supported a three-factor structure explaining 86.08%of variance, with strong primary loadings and minimal cross-loadings. All corrected item–total correlations exceeded 0.45, and removing any item did not meaningfully improve alpha.
Conclusion: The nine-item AI-Based Academic Support Questionnaire demonstrated strong evidence of content validity, internal consistency, and construct validity. The instrument may be used for educational research and for evaluating AI-enhanced learning interventions in higher education.
Methods: This methodological psychometric study generated an initial 20-item pool based on literature review and a three-dimensional conceptual framework (motivational, cognitive/conceptual, and metacognitive support). After expert review, nine items were retained. Content validity was assessed using CVR (Lawshe) and CVI. Face validity was evaluated using qualitative review and Item Impact Score. Reliability was assessed using Cronbach’s alpha coefficients. Construct validity was examined via exploratory factor analysis (EFA), including KMO and Bartlett’s test, using principal component extraction with Varimax rotation. In addition, item-level analysis (corrected item–total correlations and alpha if item deleted), descriptive statistics (skewness/kurtosis), and communalities were computed to provide a complete psychometric report.
Results: Content validity was excellent (CVR = 1.00; S-CVI/Ave = 1.00). Internal consistency was strong for the total scale (Cronbach’s α = 0.881) and subscales (motivational α = 0.981; cognitive/conceptual α = 0.944; metacognitive α = 0.801). Item distributions were approximately normal (|skewness| < 0.12; kurtosis ≈ −1.06 to −0.95). Sampling adequacy was acceptable (KMO = 0.843), and Bartlett’s test was significant (χ² = 2667.37, df = 36, p < 0.001). EFA supported a three-factor structure explaining 86.08%of variance, with strong primary loadings and minimal cross-loadings. All corrected item–total correlations exceeded 0.45, and removing any item did not meaningfully improve alpha.
Conclusion: The nine-item AI-Based Academic Support Questionnaire demonstrated strong evidence of content validity, internal consistency, and construct validity. The instrument may be used for educational research and for evaluating AI-enhanced learning interventions in higher education.
Keywords: artificial intelligence, academic support, psychometrics, validity, reliability, university students, exploratory factor analysis
Type of Study: Orginal Article |
Subject:
● Artificial Intelligence
Received: 2026/05/17 | Accepted: 2026/09/16 | Published: 2026/11/11
Received: 2026/05/17 | Accepted: 2026/09/16 | Published: 2026/11/11
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