# Human AI Collaboration in Software Engineering: Lessons Learned from a Hands On Workshop

arXiv: https://arxiv.org/abs/2312.10620 · HTML: https://mlsanity.com/paper/2312.10620/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2023.

## Built on by (most cited first, 7 of 7)

- [Advancing GenAI Assisted Programming--A Comparative Study on Prompt Efficiency and Code Quality Between GPT-4 and GLM-4](https://mlsanity.com/paper/2402.12782/) (2024)
- [From Junior to Senior: Allocating Agency and Navigating Professional Growth in Agentic AI-Mediated Software Engineering](https://mlsanity.com/paper/2602.00496/) (2026)
- [Enhancing Code LLM Training with Programmer Attention](https://mlsanity.com/paper/2503.14936/) (2025)
- [Impact and Implications of Generative AI for Enterprise Architects in Agile Environments: A Systematic Literature Review](https://mlsanity.com/paper/2510.22003/) (2025)
- [Towards Human-AI Synergy in Requirements Engineering: A Framework and Preliminary Study](https://mlsanity.com/paper/2510.25016/) (2025)
- [AI for Requirements Engineering: Industry adoption and Practitioner perspectives](https://mlsanity.com/paper/2511.01324/) (2025)
- [PRAISE: A Reproducible Workflow for Non-Programmer Scientists to Implement Published AI Models](https://mlsanity.com/paper/2610.16980/) (2026)

## Abstract (arXiv)

This paper investigates the dynamics of human AI collaboration in software engineering, focusing on the use of ChatGPT. Through a thematic analysis of a hands on workshop in which 22 professional software engineers collaborated for three hours with ChatGPT, we explore the transition of AI from a mere tool to a collaborative partner. The study identifies key themes such as the evolving nature of human AI interaction, the capabilities of AI in software engineering tasks, and the challenges and limitations of integrating AI in this domain. The findings show that while AI, particularly ChatGPT, improves the efficiency of code generation and optimization, human oversight remains crucial, especially in areas requiring complex problem solving and security considerations. This research contributes to the theoretical understanding of human AI collaboration in software engineering and provides practical insights for effectively integrating AI tools into development processes. It highlights the need for clear role allocation, effective communication, and balanced AI human collaboration to realize the full potential of AI in software engineering.