Do Generation Z Pre-Service ESL Teachers Perceive Artificial Intelligence Negatively? Rasch Analysis
Abstract
This study investigates the negative attitudes of pre-service English as a Second Language (ESL) teachers toward the integration of Artificial Intelligence (AI) in education. Using a quantitative cross-sectional survey design, data were collected from 363 undergraduate students enrolled in teacher education programs. The participants completed the Negative Attitudes Toward Artificial Intelligence (NATAI) scale which was validated through expert review. Rasch model analysis was employed to examine item fit, reliability, and unidimensionality. The instrument demonstrated high internal consistency (Cronbach’s Alpha = 0.84), strong person and item reliability (0.80 and 0.98, respectively), and solid construct validity. The Wright Map revealed a moderate to high concern among students, particularly about AI's emotional and ethical implications. Differential Item Functioning (DIF) analysis based on year of study and gender showed minimal variation across groups, with third-year students expressing slightly stronger ethical concerns. A one-way ANOVA and independent t-test confirmed no significant difference in attitudes based on the year of study, suggesting uniform skepticism across cohorts. These findings imply a need for teacher education curricula to address AI literacy and integrate balanced perspectives to prepare future educators for AI-enhanced classrooms.
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DOI: https://doi.org/10.59698/afeksi.v6i4.502
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