AI-Augmented Model-Based Systems Engineering (MBSE)

AI-augmented model-based systems engineering integrates AI directly into engineering workflows to support the creation, maintenance, analysis, and review of system models. AI assistants work alongside language-aware toolchains, curated domain knowledge, repeatable workflows, and deterministic validation and analysis. This combination can reduce routine modeling effort, shorten the time between design changes and engineering feedback, expose inconsistencies earlier, and help keep models, analysis results, and documentation aligned.

This collection presents SEI research and demonstrations of this approach using SysML v2 and the Architecture Analysis and Design Language (AADL). Together, these efforts illustrate how AI assistants can generate and revise model content, retrieve relevant guidance, respond to tool diagnostics, and summarize results, while engineers remain responsible for requirements, assumptions, acceptance criteria, technical decisions, and assurance. By pairing AI capabilities with inspectable evidence and human oversight, AI-augmented MBSE can support faster, more consistent, and better-informed systems engineering.