Teachers’ Readiness to Integrate Artificial Intelligence into Classroom Practice: Development and Psychometric Validation of the TRi-AI Scale Using Factor Analysis, Measurement Invariance, and Network Psychometrics

The increasing integration of Artificial Intelligence (AI) into classroom practice has created the need for valid instruments to assess teachers’ readiness for AI integration. The present study developed and psychometrically validated the Teachers’ Readiness to integrate Artificial Intelligence (TRi-AI) scale, conceptualizing readiness as a multidimensional construct comprising Cognitive Conditions, Affective Conditions, Commitment, Self-efficacy, Ethical Considerations, and Worries. A total of 645 in-service primary and secondary school teachers participated in the study. The findings from Exploratory and Confirmatory Factor Analyses supported the hypothesized six-factor structure. The final scale comprised 36 items, χ2(579) = 1098.950, p < 0.001, CFI = 0.943, TLI = 0.938, RMSEA = 0.037, 90% CI [0.034, 0.041], and SRMR = 0.044. Reliability, convergent validity, discriminant validity, and evidence of practical measurement invariance across gender substantiated the psychometric adequacy of the instrument. Additionally, Exploratory Graph Analysis, Item Stability Analysis, and Network Comparison Test provided complementary psychometric evidence for the robustness of the proposed framework. The TRi-AI scale is proposed as a reliable and valid multidimensional instrument that can support both future research and educational practice.