Measuring ethical AI Use in higher education: Reliability and validity of the AI academic integrity scale for postgraduate students

Abstract

This study developed and validated a psychometrically sound instrument to assess ethical artificial intelligence (AI) use among postgraduate students, addressing the critical gap in reliable measurement tools for AI academic integrity in higher education. A quantitative design employed two Egyptian postgraduate student samples (N₁=629, N₂=884) for exploratory and confirmatory factor analyses. The AI Academic Integrity Scale was developed through a comprehensive literature review and subjected to rigorous psychometric validation, including reliability assessment using multiple coefficients. Results revealed a robust three-factor structure: Ethical Use of AI, Awareness of Misuse Risks, and Academic Writing Support, explaining 39.997% of the total variance. Confirmatory factor analysis demonstrated an excellent model fit (χ²/df=2.484, GFI=.974, CFI=.958, RMSEA=.041). The final 17-item instrument showed satisfactory reliability across subscales (McDonald's ω=.695-.708, Cronbach's α=.692-.707), with factor loadings ranging from .502 to .688. The AI Academic Integrity Scale is a reliable tool that identifies the ethical engagement of AI technologies in postgraduate education, offering a comprehensive assessment of both challenges and opportunities within academic contexts. The instrument assesses postgraduate students' ethical AI engagement, aiding policy development and targeted interventions, promoting responsible AI integration while maintaining academic integrity in AI-enhanced educational environments.

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