生成式人工智能正日益被非正式地应用于空中交通管理（ATM）中的飞行计划生成、航迹解读和约束检查等任务。尽管这些工具能够减轻工作负荷并加速规划，但其非确定性输出在人机协同环境中带来了安全和运行风险。本文提出了 AI 信任与保障层（ATAL），这是一种模型无关的决策保障架构，用于评估 AI 生成的飞行计划输出是否具备足够的可靠性以投入实际运行。ATAL 结合了提示词变化下的语义稳定性、结构化输出的运行一致性以及针对领域规则的规范性约束验证，并将这些信号映射为供人类操作员参考的决策就绪等级（DRL）。一项受 ATM 启发的实验研究表明，该框架能够在影响飞行计划验证或执行之前，识别出不安全、不一致或具有误导性的输出。尽管在航空领域进行了演示，但该框架同样可迁移至其他需要在监管约束下进行人工监督的安全关键型决策支持领域。

Generative AI is increasingly being used informally in Air Traffic Management (ATM) for tasks such as flight plan generation, trajectory interpretation, and constraint checking. Although these tools can reduce workload and accelerate planning, their non-deterministic outputs create safety and operational risks in human-in-the-loop settings. This paper proposes the AI Trust and Assurance Layer (ATAL), a model-agnostic decision assurance architecture that evaluates whether AI-generated flight-planning outputs are sufficiently reliable for operational use. ATAL combines semantic stability under prompt variation, operational consistency of structured outputs, and normative constraint validation against domain rules, and maps these signals to a Decision Readiness Level (DRL) for human operators. An ATM-inspired experimental study shows how unsafe, inconsistent, or misleading outputs can be identified before influencing flight-plan validation or execution. Although demonstrated in aviation, the framework is also transferable to other safety-critical decision-support domains that require human oversight under regulatory constraints.