Navigating the AI Frontier: Developing Strategies to Harness Generative AI's Potential and Mitigate Risks in Higher Education

Abstract

  Research Question How can higher education institutions implement AI tools to enhance student productivity and learning outcomes while addressing faculty concerns about academic integrity, given the varying interpretations of AI's educational benefits among faculty members? Abstract As artificial intelligence becomes increasingly prevalent in higher education, engineering institutions must move beyond binary "allow or forbid" policies toward nuanced, evidence-based implementation strategies. Current approaches to AI integration in engineering education are often reactive rather than strategic, leading to missed opportunities for enhancing student learning while potentially compromising academic integrity. This study argues that the successful integration of AI in engineering education requires a tiered implementation approach, prioritizing applications where student needs and faculty perspectives align. Through a mixed-methods analysis combining student usage patterns with faculty evaluations, we demonstrate that certain AI applications, particularly those focused on learning process enhancement rather than content generation, can strengthen engineering education when properly implemented. The findings challenge the current ad-hoc approach to AI policy in higher education, proposing instead a structured framework that maximizes educational benefits while preserving academic rigor. This research provides institutions with an evidence-based pathway for moving from blanket AI policies toward targeted implementations that enhance engineering education.