The rapid growth of generative artificial intelligence (GenAI) has created both new opportunities and problems for instructional change in higher education. Systems Engineering, a key methodology course for “engineering management” postgraduates, aims to develop students’ abilities to understand and solve complex engineering problems using systematic thinking. However, persistent obstacles in teaching practice have long existed, including abstract theoretical concepts, methodologies with limited transferability to real-world circumstances, insufficient customized assistance, boring teaching forms, and strict evaluation practices. Taking the course of Systems Engineering as the research object, this study analyzes the course’s characteristics and teaching pain points. An overall framework is proposed, which centers on “human-machine collaboration”, with “pre-class, in-class, post-class” as the main line, and encompassing the dimensions of “teacher, student, and course assessment”. Based on this approach, we explain how GenAI might be integrated into the teaching of Systems Engineering from each of the three perspectives of teacher, student, and course assessment. This investigation is expected to serve as a reference for teaching reform in engineering management courses, aid in the development of compound engineering talents with systematic thinking ability, and contribute to the development of “new business” talents in the digital economy.