In the advent of modern technologies used in educational systems, stakeholders in academic institutions are also scrutinizing the use of advanced technologies in special education supervision. Driven by the fact that advanced technologies, such as artificial intelligence (AI), have empirically improved the learning process of students and the monitoring of general systems, it is also highly likely that such technology can also prove worthy of adoption in special education. In this paper, a fuzzy best-worst method (BWM) is utilized to analyze the adoption factors aligned to the supervision process in special education. Key results revealed that AI tools must be selected mainly based on the engagement level it provides to the students as valuable insights can also be gained by supervisors which will be correspondingly used in intervention strategies. The rest of the adoption factors have also been ranked in order of priority for stakeholders to consider in selecting an AI technology to use for special education supervision.