The aim of the present study is to investigate the pattern of using artificial intelligence in evaluating curricula designed and developed in the higher education system. This study was conducted with a qualitative approach and using a data-driven method. The statistical population included all experts in the fields of curriculum planning, educational technology, and computer engineering who had experience in writing and scientific research in the field of artificial intelligence. 19 people were selected to participate in the study using a purposive and criterion-based sampling method. The data collection method was a semi-structured interview, and open, axial, and selective coding methods were used to analyze the data. In order to validate and ensure the validity and reliability of the research findings, Lincoln and Guba's four criteria were used. The results of the research indicated twenty central concepts, which were organized according to the paradigmatic model of Strauss and Corbin (2008) in the form of the central phenomenon of the research (data-driven analysis based on artificial intelligence in the design and development of higher education curricula), causal conditions (weakness of standards, traditional evaluation, complexity and volume of evaluation data and the need for internationalization of curriculum design and development), strategies, contextual factors, intervening factors and consequences. Finally, based on the results of the research, it is suggested that by providing a platform in the aforementioned dimensions, attention should be paid to the application of artificial intelligence in curriculum planning processes in higher education.