The future of Requirements Engineering (RE) is increasingly driven by artificial intelligence (AI), reshaping how we elicit, analyze, and validate requirements. Traditional RE is based on labor-intensive manual processes prone to errors and complexity. AI-powered approaches, specifically large language models (LLMs), natural language processing (NLP), and generative AI, offer transformative solutions and reduce inefficiencies. However, the use of AI in RE also brings challenges like algorithmic bias, lack of explainability, and ethical concerns related to automation. To address these issues, this study introduces the Human-AI RE Synergy Model (HARE-SM), a conceptual framework that integrates AI-driven analysis with human oversight to improve requirements elicitation, analysis, and validation. The model emphasizes ethical AI use through transparency, explainability, and bias mitigation. We outline a multi-phase research methodology focused on preparing RE datasets, fine-tuning AI models, and designing collaborative human-AI workflows. This preliminary study presents the conceptual framework and early-stage prototype implementation, establishing a research agenda and practical design direction for applying intelligent data science techniques to semi-structured and unstructured RE data in collaborative environments.

翻译：需求工程的未来日益受到人工智能的驱动，重塑了我们如何获取、分析和验证需求。传统的需求工程基于劳动密集型的手动流程，易受错误和复杂性的影响。以人工智能为驱动的方法，特别是大语言模型、自然语言处理和生成式人工智能，提供了变革性的解决方案并减少了低效问题。然而，人工智能在需求工程中的应用也带来了挑战，如算法偏见、缺乏可解释性以及与自动化相关的伦理问题。为解决这些问题，本研究提出了人机需求工程协同模型，这是一个将人工智能驱动分析与人类监督相结合的概念框架，旨在改进需求获取、分析和验证。该模型通过透明度、可解释性和偏见缓解强调人工智能的伦理使用。我们概述了一个多阶段研究方法，专注于准备需求工程数据集、微调人工智能模型以及设计协作式人机工作流程。这项初步研究提出了概念框架和早期原型实现，为在协作环境中将智能数据科学技术应用于半结构化和非结构化需求工程数据确立了研究议程和实际设计方向。