Artificial Intelligence is increasingly introduced into systems engineering activities, particularly within requirements engineering, where quality assessment and validation remain heavily dependent on expert judgment. While recent AI tools demonstrate promising capabilities in analyzing and generating requirements, their role within formal systems engineering processes-and their alignment with established INCOSE criteria-remains insufficiently understood. This paper investigates the extent to which AI-based tools can support systems engineers in evaluating requirement quality, without replacing professional expertise. The research adopts a structured systems engineering methodology to compare AI-assisted requirement evaluation with human expert assessment. A controlled study was conducted in which system requirements were evaluated against established INCOSE ``good requirement'' criteria by both experienced systems engineers and an AI-based assessment tool. The evaluation focused on consistency, completeness, clarity, and testability, examining not only accuracy but also the decision logic underlying each assessment. Results indicate that AI tools can provide consistent and rapid preliminary assessments, particularly for syntactic and structural quality attributes. However, expert judgment remains essential for contextual interpretation, ambiguity resolution, and trade-off reasoning. Rather than positioning AI as a replacement for systems engineers, the findings support its role as a decision-support mechanism within the RE lifecycle. From a systems engineering perspective, this study contributes empirical evidence on how AI can be integrated into RE workflows while preserving traceability, accountability, and engineering consistency.

翻译：人工智能正日益融入系统工程活动中，尤其在需求工程领域，其质量评估与验证仍高度依赖专家判断。尽管近期AI工具在需求分析与生成方面展现出可期能力，但在正式系统工程流程中的角色——以及其与INCOSE既定标准的契合度——仍缺乏充分认知。本文探究了AI工具在无需替代专业经验的前提下，能在多大程度上支持系统工程师评估需求质量。研究采用结构化系统工程方法，将AI辅助需求评估与人类专家评估进行对比。通过控制实验，由经验丰富的系统工程师与AI评估工具分别依据INCOSE“优质需求”准则对系统需求进行评价。评估聚焦于一致性、完整性、清晰性和可测试性，不仅考察准确率，还剖析了每项评估背后的决策逻辑。结果表明，AI工具能够提供一致且快速的初步评估，尤其在句法和结构质量属性方面。然而，专家判断在上下文解读、歧义消解及权衡推理中仍不可或缺。研究结论并非将AI定位为系统工程师的替代品，而是支持其作为需求工程生命周期中的决策支持机制。从系统工程视角出发，本研究提供了将AI集成至需求工程工作流的实证证据，同时确保可追溯性、问责机制与工程一致性。