AI4SE and SE4AI Exploration: A Decade Looking Back and Forward

2026-06-17 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Research Methodology & Innovation · Depth: Expert, quick

Summary

The article "AI4SE and SE4AI Exploration: A Decade Looking Back and Forward" reviews the evolution of Artificial Intelligence (AI) and Systems Engineering (SE) over the past decade, categorizing its progress into foundational, applied, and LLM inflection phases. It highlights areas of community consensus and persistent research gaps. A comprehensive literature review, involving both human experts and six AI models, analyzed 1,712 INCOSE INSIGHT articles and 889 SERC publications to assess relevance. This analysis pinpointed five critical research gaps and provides guidance for practitioners on AI adoption, assurance, and workforce transformation within SE. The authors also share the agreement data and an AI4SE/SE4AI Explorer web application, allowing readers to compare their own relevance judgments with those of human and AI raters. The March 2020 INCOSE INSIGHT special issue on AI and SE was the most downloaded in its history, fostering a community that now attracts over 250 annual workshop registrants.

Key takeaway

For Systems Engineers and AI Researchers navigating AI adoption and assurance, this analysis provides critical insights into the field's evolution and current gaps. You should utilize the AI4SE/SE4AI Explorer web application to benchmark your own relevance judgments against human and AI raters. Focus your efforts on addressing the five identified research gaps to advance AI integration and workforce transformation within Systems Engineering.

Key insights

The article reviews AI and SE evolution, identifying research gaps and offering practitioner guidance based on a human-AI literature review.

Principles

• Tracing AI/SE evolution reveals foundational, applied, and LLM inflection phases.
• Human-AI collaboration enhances literature review accuracy.
• Identifying research gaps guides future AI/SE development.

Method

A literature review assessed 1,712 INCOSE INSIGHT and 889 SERC articles using human expertise and six AI models to identify research gaps.

In practice

• Use the AI4SE/SE4AI Explorer to compare relevance judgments.
• Consult guidance for AI adoption and assurance in SE.
• Address identified research gaps in AI/SE projects.

Topics

• AI4SE
• SE4AI
• Systems Engineering
• AI Adoption
• Research Gaps
• LLM Inflection

Best for: AI Scientist, Research Scientist, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.