In this study, a scale was developed to determine the artificial intelligence literacy and usage competencies of pre-service teachers studying in special education departments of universities in Turkey. In creating the scales to determine the artificial intelligence literacy and usage levels of pre-service special education teachers, the related literature on artificial intelligence was examined, and the content and framework regarding which factors should be evaluated were created. First, subheadings for each measurement tool and an item pool for these subheadings were created, and then the target items were selected from the item pool by the research group. In this context, two five-point Likert-type measurement tools were drafted to awareness of pre-service special education teachers' artificial intelligence literacy levels, usage competencies, and ethical responsibilities. After the measurement tool items were drafted, the instruments were sent to 5 experts in the field of measurement and evaluation for their opinions to evaluate the appropriateness of the items. The measurement tools were reorganized within the scope of the experts' feedback, and a pilot study was conducted with 10 pre-service special education teachers before the tools were finalized. The research team reorganized the measurement tool within the pilot study's framework and sent it to the second group of experts. In order to determine the content validity of the measurement tool, the Lawshe (Lawshe, 1975) technique was used to evaluate the opinions of all experts together, and content validity was ensured by using the Lawshe (Lawshe, 1975) technique. The scales, which included 17 items at the Literacy level and 26 items at the Use Competencies level, were converted into Google Forms format and then sent to prospective special education teachers studying in special education departments of universities. In the study where the data are still being collected, Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) will be conducted using the Kaiser-Meyer-Olkin Sampling Adequacy Measure and Bartlett's Sphericity Test to determine the validity of the scale. Internal consistency and correlation analyses will determine the scale's reliability. In line with the data to be obtained, the place of pre-service teachers' artificial intelligence literacy levels and usage competencies in educational processes will be evaluated more comprehensively. In addition, the scale results may contribute to policy development processes for integrating artificial intelligence in special education. Future studies can deepen the knowledge in this field by examining how AI literacy is associated with teacher competencies, ethical awareness, and implementation skills in special education.

Keywords: Artificial intelligence, validity, reliability, teacher candidate, special education.