Artificial intelligence (AI) is becoming routine infrastructure in higher vocational colleges, while labor education increasingly serves holistic talent cultivation through authentic tasks and practicum experiences. However, evaluating labor education is challenging because intended outcomes combine competence and occupational dispositions, and evidence is heterogeneous, process-oriented, and distributed across sites. This paper reframes labor education evaluation as a system design problem rather than a single-instrument measurement task. It proposes a layered conceptual model that separates constructs, evidence, analytics, decision procedures, and governance, and provides a minimal evidence taxonomy to support triangulated and longitudinal interpretation. Building on this foundation, the paper consolidates eight design principles and maps them to implementable system requirements and assurance considerations, emphasizing authentic-task anchoring, stake-sensitive human-in-the-loop decisions, traceability and explainability proportional to stakes, context-aware fairness, purpose-limited data practices, contestability, and continuous monitoring. The framework offers actionable guidance for designing AI-enhanced labor education evaluation systems and identifies directions for further research on construct operationalization, evidence integration, and governance-by-design.