<p>Transforming engineering education in the AI era requires an evaluation of new instructional tools and a reconceptualization of the division of labor among teachers, students, and intelligent learning companion systems (ILCSs). This work explores how a retrieval-augmented generation intelligent learning companion can be embedded within a human–AI collaborative teaching model by using an analog circuit laboratory instruction as a case study. A controlled experiment compared traditional teacher-led guidance with system-supported instruction, focusing on three core dimensions: knowledge acquisition, learning effect (cognition, skill, and emotion), and flow experience (cognitive control, immersion and time transformation, loss of self-consciousness, and autotelic experience). The results indicate that while the system showed a limited impact on knowledge acquisition and emotion, it significantly enhanced skills, immersion and time transformation, and autotelic experience. These findings suggest that ILCSs serve as effective complements in practice-oriented engineering education, particularly in terms of providing personalized support and instant feedback strengthening hands-on learning and student engagement. Such companions cannot fully serve as a substitute for teacher-led conceptual scaffolding or emotional guidance. The study’s theoretical contribution lies in emphasizing the importance of role allocation in human–AI collaborative education and offers practical implications for the design of learner-centered, practice-oriented instructional models in intelligent education.</p>