Research on the Construction of Evaluation Mechanism for Packaging Design Course Driven by AIGC Content Generation

As artificial intelligence-generated content (AIGC) becomes increasingly integrated into design education, traditional evaluation models in packaging design courses reveal significant limitations, particularly in assessing creative processes, iterative development, and human-AI collaboration. This study proposes a comprehensive evaluation mechanism tailored to AIGC-driven workflows, incorporating formative assessment principles across five key dimensions: crea-tive expression, technical execution, AIGC collaboration, process participation, and reflective evaluation. A multi-agent framework involving teachers, AI systems, and students is introduced to ensure transparency, fairness, and alignment with intelligent design practices. This mechanism emphasizes a dynamic, learner-centered approach over static, outcome-based assessments. It not only captures the evolving nature of design tasks but also supports critical thinking and adaptability in working with generative tools. The proposed model holds both theoretical and practical significance, offering a pathway toward more relevant and future-oriented design education in an era shaped by emerging technologies and collaborative intelligence. Furthermore, the increasing use of generative AI in packaging design challenges educators to redefine academic integrity, originality, and authorship within collaborative environments.