Integrating Artificial Intelligence Into the Industrial Engineering Discipline

Abstract

This chapter explores the integration of Artificial Intelligence (AI) into Industrial Engineering (IE) education, focusing on foundational concepts, applications, research innovations, and ethics. By embedding AI literacy into curricula using tools like Python, TensorFlow, and simulation software, it fosters skills in predictive analytics, optimization, and AI-driven decision-making. Practical applications are demonstrated through case studies in logistics, healthcare, and sustainable manufacturing, highlighting AI's synergy with Multi-Criteria Decision-Making (MCDM) methods such as AHP and TOPSIS for dynamic system optimization. Adaptive learning tools and AI tutors enhance personalized education, while ethical challenges—algorithmic bias, data privacy, and access gaps—call for governance and transparency. Emerging trends like explainable AI (XAI), digital twins, and human-AI collaboration promise to redefine IE education, equipping students to address global industrial challenges. This synthesis of theory and practice cultivates engineers who balance innovation with ethical responsibility in a rapidly evolving technological landscape.