Validating and refining a multi-dimensional scale for measuring AI literacy in education using the Rasch Model - Ying Dong; Wei Xu; Jiayan Huang; Kerr Yann (Humanities & Social Sciences Communications)
Abstract AI literacy in education is a multi-dimensional concept involving the understanding of AI technologies, critical appraisal of AI technologies, practical application, and AI ethics. Through the Rasch Model, this duplication study validated and revised the scales used in previous studies to measure AI literacy in education. Based on the literature, we developed a scale to measure AI literacy in education, including technological understanding, critical appraisal, practical application, and AI ethics, whose validity and reliability were examined using the Rasch Model. Based on the results of validity, we removed items whose infit/outfit mean square (MNSQ) or standardized mean square (ZSTD) values fell outside the acceptable range (0.6–1.4 for MNSQ; −2 to 2 for ZSTD). This enhances the validity and provides reliable results, enabling the scale to measure AI literacy in education effectively. Future research can conduct an in-depth examination of the Rasch Model for the construction of AI literacy in education, validating its cross-disciplinary generalizability, exploring cultural and demographic factors, and enhancing the generalizability and precision of the scale.