AI-Assisted Requirements Engineering: An Empirical Evaluation Relative to Expert Judgment

TL;DR

This study empirically evaluates AI tools' support role in requirements engineering, comparing their assessments with human experts to understand their effectiveness and limitations.

Contribution

It provides empirical evidence on integrating AI into requirements engineering workflows without replacing human judgment.

Findings

AI provides consistent, rapid preliminary assessments for syntactic and structural quality.

Expert judgment remains crucial for contextual interpretation and ambiguity resolution.

AI supports decision-making but does not replace human expertise in requirements evaluation.

Abstract

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.