Developing the AIlessphobia in education scale and examining its psychometric characteristics

The purpose of this research is to create a reliable and valid scale to assess AIlessphobia in Education (the fear of being without Artificial Intelligence in education) among university students. In three phases, a sample of 1378 undergraduate students from different faculties at a public university participated in the reliability and validity investigations of the scale during the academic year 2023–2024. Expert comments were obtained to assess the scale's face validity and content validity. The first group sample (n = 420) underwent exploratory factor analysis (EFA), the second group sample (n = 510) underwent confirmatory factor analysis (CFA), and the third group sample (n = 448) underwent criterion-related validity testing. EFA revealed that the scale had a two-factor structure with 18 items that explained 56.23% of the total variance. The CFA analysis verified the scale's two-factor structure and produced good fit values (χ2/df = 2.25, CFI = .99; TLI = .99; NFI = .98; IFI = .99; SRMR = .049; RMSEA = 0.050 [0.42–0.57]). The first factor's analysis showed acceptable values for Guttman's lambda (λ = 0.930–0.948), McDonald's omega (ω = 0.923–0.929), and Cronbach's alpha (α = 0.925–0.935). Similarly, the second factor's analysis also showed acceptable values for these measures (λ = 0.851–0.880, ω = 0.850–0.879, α = 0.847–0.877). Overall, the entire scale demonstrated acceptable values for Cronbach's alpha (0.925–0.935), McDonald's omega (0.922–0.942), and Guttman's lambda (0.940–0.942). Additionally, the scale exhibited a positive and statistically significant correlation with the Fırat Netlessphobia Scale, indicating satisfactory criterion validity. Cross-gender invariance analysis was also performed, showing that gender invariance was achieved. The results indicate that this scale is valid and reliable for university students. In conclusion, the scale fills a critical gap in educational research by providing a reliable tool to measure students' fears and anxieties about the absence of Artificial Intelligence (AI) in their learning experiences. By accurately assessing this unique form of anxiety, educators and policymakers can develop targeted interventions to better understand and mitigate students' fears and support the integration of AI in education, thereby enhancing its constructive contribution to learning.