Artificial Intelligence Applications in Undergraduate Engineering Education: A Literature Review

Abstract

Artificial Intelligence (AI) is rapidly transforming undergraduate engineering education, offering new tools to personalize learning, improve feedback, and scale instruction. This literature review synthesizes recent developments across five key application areas: intelligent tutoring systems, adaptive learning platforms, AI-assisted assessment, virtual laboratories, and AI-driven career and communication support. These technologies have shown promise in enhancing student engagement, tailoring content delivery, and supporting data-informed teaching practices. However, their integration also raises concerns about ethical use, data privacy, algorithmic bias, and faculty preparedness. The review identifies six critical gaps in current research, including the lack of longitudinal studies, limited focus on higher-order thinking skills, and underrepresentation of certain engineering fields. It concludes by outlining strategic directions for responsible AI integration, emphasizing ethical design, inclusive access, and stronger support for educators. As AI continues to evolve, its thoughtful implementation holds the potential to significantly enrich engineering education and better prepare students for future challenges.