Unfolding Generation Z Pre-Service Elementary Teachers Positive Attitude Toward Artificial Intelligence: Rasch Model Analysis - Meivawati, Eli; Ningthias, Dyah Puspitasari; Pravitasari Putri, Eka; Tari, Suci Rizkina; Qudratudarsi, Hilman; Yanti, Meili (Pusat Studi Penelitian dan Evaluasi Pembelajaran, 2025)
This study investigates the attitudes of pre-service elementary teachers in Indonesia toward artificial intelligence (AI) using Rasch model analysis. As future educators, their perceptions of AI are crucial for the successful integration of technology in educational practices. The research involved 244 participants from the Elementary Teacher Education program in West Sulawesi, Indonesia, selected based on inclusion criteria such as year of study and experience with AI applications in learning. The instrument, adapted with 12 items measuring positive attitudes toward AI, was validated through checks for reliability, item separation, fit statistics, and unidimensionality. Data were analyzed using WINSTEPS software to generate Wright maps and conduct Differential Item Functioning (DIF) analysis. Findings reveal that pre-service teachers generally demonstrate a moderate to high positive attitude toward AI, with higher-year students and those with more frequent AI usage exhibiting stronger positive attitudes. DIF analysis shows significant differences in item endorsement based on year of study and supported by one-way ANOVA results. These results suggest that greater exposure to AI correlates with more favorable attitudes. The study implies the need for structured AI integration in teacher education curricula to foster readiness and acceptance of AI in future teaching practices.