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

arXiv: https://arxiv.org/abs/2510.25016 · HTML: https://mlsanity.com/paper/2510.25016/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 28 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Adversarial Filters of Dataset Biases](https://mlsanity.com/paper/2002.04108/) (2020)
- [Fairness-guided Few-shot Prompting for Large Language Models](https://mlsanity.com/paper/2303.13217/) (2023)
- [Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs](https://mlsanity.com/paper/2310.13976/) (2023)
- [Evaluating Human-AI Collaboration: A Review and Methodological Framework](https://mlsanity.com/paper/2407.19098/) (2024)
- [Generative AI for Requirements Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2409.06741/) (2024)
- [Tailoring Requirements Engineering for Responsible AI](https://mlsanity.com/paper/2302.10816/) (2023)
- [Human AI Collaboration in Software Engineering: Lessons Learned from a Hands On Workshop](https://mlsanity.com/paper/2312.10620/) (2023)
- [RE-centric Recommendations for the Development of Trustworthy(er) Autonomous Systems](https://mlsanity.com/paper/2306.01774/) (2023)
- [Bridging the Research-Practice Gap in Requirements Engineering through Effective Teaching and Peer Learning](https://mlsanity.com/paper/1407.4186/) (2014)
- [AI Techniques for Software Requirements Prioritization](https://mlsanity.com/paper/2108.00832/) (2021)
- [Human-AI Guidelines in Practice: Leaky Abstractions as an Enabler in Collaborative Software Teams](https://mlsanity.com/paper/2207.01749/) (2022)
- [Using Language Models for Enhancing the Completeness of Natural-language Requirements](https://mlsanity.com/paper/2302.04792/) (2023)

## Abstract (arXiv)

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.