AI4SE and SE4AI Exploration: A Decade Looking Back and Forward

A decade-long research initiative tracking the intersection of AI and Systems Engineering has identified five critical research gaps and three evolutionary phases in the field. The study, which grew from a landmark 2020 INCOSE publication, analyzed over 2,600 papers using human-AI collaborative review to guide practitioners on AI adoption, assurance, and workforce transformation in engineering.

The convergence of artificial intelligence and systems engineering represents a maturing field that has evolved dramatically since the March 2020 INCOSE INSIGHT special issue sparked widespread research interest. The publication attracted such significant attention—becoming the most downloaded issue in the journal's history—that it catalyzed a formal research community now drawing 250+ annual workshop participants. This growth trajectory reflects genuine industry demand for frameworks reconciling AI capabilities with traditional SE rigor.

The research community's approach to mapping this landscape through three phases (foundational, applied, and LLM inflection) mirrors broader technological development patterns. The foundational phase established theoretical groundwork, the applied phase demonstrated real-world implementation feasibility, and the LLM inflection point marks the current era where large language models fundamentally reshape both AI capabilities and SE methodologies. This temporal framing helps practitioners understand where their organizations fit within industry maturation curves.

The study's hybrid methodology—combining human domain expertise with six distinct AI models to review 1,712 INCOSE articles and 889 SERC publications—establishes a novel precedent for collaborative knowledge synthesis. Rather than relying solely on human judgment or automated analysis, this approach creates mutual validation mechanisms that strengthen research credibility. The identification of five critical gaps provides actionable intelligence for organizations planning AI integration strategies, particularly regarding assurance mechanisms and workforce development.

Looking forward, the AI4SE/SE4AI Explorer web application democratizes access to these findings, enabling practitioners to benchmark their own relevance assessments against the study's human and AI raters. This transparency supports evidence-based decision-making across technical and organizational domains, potentially accelerating responsible AI adoption in systems engineering contexts.

• →A decade-long study identified three evolutionary phases in AI and Systems Engineering integration, with LLM technologies creating a current inflection point.
• →Analysis of 2,600+ publications revealed five critical research gaps requiring focused attention for effective AI adoption in SE contexts.
• →Hybrid human-AI collaborative methodology demonstrates scalable approaches for validating research relevance across complex technical literature.
• →The released AI4SE/SE4AI Explorer tool enables practitioners to benchmark their judgment against both human and AI raters.
• →Workforce transformation emerges as a critical gap, indicating organizations need guidance on human-AI collaboration models, not just technical integration.