Aiming at the converging waves of Artificial Intelligence, the Internet of Things, and big data, the course Principles and Applications of Embedded Systems is confronted with rapidly evolving knowledge, high practical thresholds, and fragmented teaching resources. Under the new paradigm of AI-empowered smart classrooms, cultivating self-directed, high-level, innovative talent has become the primary mission of universities. We therefore propose an AI-enabled blended-teaching reform and exploration for embedded courses: an embedded-AI curriculum is created and an AI-empowered embedded-course system is constructed. Online-offline blended instructional designs are developed in which AI agents permeate the entire learning chain—pre-class diagnosis, in-class interaction, and post-class extension—dynamically generating "one thousand faces for one thousand students" learning paths and realizing true individualized instruction. Simultaneously, a multi-data-driven, diversified learning-assessment model is introduced to accurately profile knowledge and form an iterative, interpretable “AI+Embedded” teaching ecosystem, offering a new roadmap for transcending traditional blended-teaching models.