Evaluating the Validity and Reliability of a Generative AI Literacy Scale among Pre-Service Mathematics Teachers Mailizar Mailizar, Mukhlis Hidayat, Dwi Fadhiliani, and Abdul Halim Mailizar @usk.ac.id Abstract This study aims to evaluate the validity and reliability of an instrument designed to measure generative AI literacy among pre-service mathematics teachers. The instrument, developed based on theoretical constructs of generative AI literacy, was administered to 30 pre-service mathematics teachers. Item-total correlation analysis revealed that all items demonstrated strong and statistically significant correlations with the total score (ranging from 0.52 to 0.84, p < 0.01), indicating good item validity. Exploratory factor analysis identified five main factors (eigenvalue > 1), supporting the multidimensionality of the instrument, with most items loading strongly on the first factor. The reliability analysis yielded a Cronbach^s Alpha value of 0.95, indicating good internal consistency. These results suggest that the instrument is both valid and reliable for assessing generative AI literacy in the context of mathematics teacher education. The validated instrument can be used to support further research and development in the integration of generative AI in mathematics education Mailizar Mailizar, Mukhlis Hidayat, Dwi Fadhiliani, Abdul Halim
Universitas Syiah Kuala
Abstract
This study aims to evaluate the validity and reliability of an instrument designed to measure generative AI literacy among pre-service mathematics teachers. The instrument, developed based on theoretical constructs of generative AI literacy, was administered to 30 pre-service mathematics teachers. Item-total correlation analysis revealed that all items demonstrated strong and statistically significant correlations with the total score (ranging from 0.52 to 0.84, p < 0.01), indicating good item validity. Exploratory factor analysis identified five main factors (eigenvalue > 1), supporting the multidimensionality of the instrument, with most items loading strongly on the first factor. The reliability analysis yielded a Cronbach^s Alpha value of 0.95, indicating good internal consistency. These results suggest that the instrument is both valid and reliable for assessing generative AI literacy in the context of mathematics teacher education. The validated instrument can be used to support further research and development in the integration of generative AI in mathematics education.
