Barriers to Integrating AI in Curriculum for Enhanced Engineering Education: A Fuzzy ISM Approach

Abstract

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 barriers. Further exploration with MICMAC categorizes the barriers according to their influence (driving power) and their susceptibility (dependence). The findings indicated that Curriculum Rigidity and Student Preparedness are both highly influential and dependent, whereas Ethical and Societal Concerns are relatively isolated. This study provides a structured framework for identifying and overcoming the challenges of integrating AI into engineering education, offering critical insights for both educators and decision-makers. By strategically prioritizing and tackling these barriers, educational institutions can improve their AI curricula, thus better equipping students for future challenges. The research emphasizes the importance of continually revising and assessing the AI integration process to stay aligned with evolving technological trends.