Towards Human-AI Synergy in Requirements Engineering: A Framework and Preliminary Study

AI can significantly improve requirements engineering efficiency: The paper identifies that AI-powered approaches using large language models and natural language processing can reduce the labor-intensive manual processes traditionally used to elicit, analyze, and validate requirements, addressing a major source of errors and complexity in software development.

Human oversight is essential, not optional: Rather than replacing human judgment, the proposed Human-AI RE Synergy Model (HARE-SM) emphasizes that AI should work with humans in collaborative workflows, suggesting that the most effective approach integrates AI-driven analysis alongside human decision-making rather than full automation.

Algorithmic bias and explainability are real concerns in RE: The paper explicitly identifies that AI use in requirements engineering introduces challenges including algorithmic bias, lack of explainability, and ethical concerns—highlighting that these aren't theoretical risks but practical issues that need to be actively addressed when deploying AI in this domain.

Transparency and bias mitigation must be built into AI-RE systems from the start: The framework prioritizes ethical AI use through transparency, explainability, and bias mitigation as core design principles, suggesting these safeguards should be integrated into AI systems for requirements engineering rather than added afterward.

This is still early-stage research requiring further development: The authors describe this as a "preliminary study" presenting a "conceptual framework and early-stage prototype," indicating that practical implementation guidance and validated best practices for human-AI collaboration in requirements engineering are still being developed.