Integrating AI Into Systems Engineering Education: From Concept to Classroom Implementation

This paper presents the implementation and evaluation of course-level AI integration across three core courses of a Systems Engineering (SE) specialization within a Master's program. Conducted as part of a faculty-wide initiative launched in 2024-25, the integration aimed to foster hybrid competencies essential for Industry 5.0. Generative and analytical AI tools were embedded as cognitive partnersaligned with the learning objectives of each course-to support structured inquiry, design exploration, and reflective practice. Drawing on student deliverables, surveys, and comparative assessments, the study demonstrates how scaffolded and context-sensitive AI use enhances both specialized SE competencies and general transferable skills. Findings suggest that aligning AI with disciplinary reasoning promotes deeper learning, increases student self-efficacy, and improves preparedness for AI-augmented professional environments. The paper concludes with instructional design principles to support effective, accountable, and meaningful AI integration in engineering education.