Integrating Artificial Intelligence in Engineering Education: A Work-in-Progress Systematic Review of Applications and Challenges

This Work-in-Progress (WIP) study systematically reviews the integration of Artificial Intelligence (AI) tools in engineering education.Through analysis of 216 studies, we examine AI's potential to enhance engagement, conceptual understanding, and skill development in foundational engineering courses.Using a theoretical framework that integrates Cognitive Load Theory (CLT), Self-Efficacy Theory (SET), and Situated Learning Theory (SLT), we analyze implementation strategies, outcomes, and barriers across diverse institutional contexts.Metaanalyses indicate that AI-enhanced active learning approaches can increase student performance by 0.47 standard deviations and reduce failure rates by up to 55% compared to traditional methods (Freeman et al., 2014).However, challenges including high implementation costs, insufficient faculty training, and inequitable access persist.This study provides evidence-based recommendations for sustainable AI integration in engineering education, supported by comprehensive case studies and detailed implementation frameworks.