Recent technological advancements have significantly impacted various sectors, including education. Among these, Artificial Intelligence (AI) stands out as a transformative force, redefining both industry practices and academic disciplines. Incorporating AI into engineering education is essential to equip students with the skills needed to navigate the complexities of the modern, technology-driven job market. This study seeks to uncover and analyze the obstacles to incorporating AI into engineering curricula through a Fuzzy Interpretive Structural Modeling (ISM) method. A thorough review of existing literature, along with open ended surveys and semi-structured interviews with stake holders helped identify eight significant barriers: Curriculum Rigidity, Faculty Expertise, Resource Limitations, Resistance to Change, Interdisciplinary Collaboration, Student Preparedness, Industry Collaboration, and Ethical and Societal Concerns. The Fuzzy ISM method facilitated the creation of a Structural Self- Interaction Matrix (SSIM), an Initial Fuzzy Reachability Matrix (IFRM), and a Final Fuzzy Reachability Matrix (FFRM), which revealed the relationships and hierarchical structures among these
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