An Empirical Study of GenAI Adoption in Open-Source Game Development: Tools, Tasks, and Developer Challenges

2025-07-23

AI Summary

Empirical studies on generative AI (GenAI) adoption in open-source game development—particularly regarding developer discourse, adoption patterns, and integration practices—remain scarce, leaving critical gaps in understanding real-world human-AI collaboration. Method: This study conducts the first systematic empirical investigation of GenAI usage in GitHub-hosted open-source game projects. We construct a curated dataset of AI-related repositories and apply hierarchical sampling, open card sorting, and thematic analysis to code and annotate issue discussions. Contribution/Results: Findings reveal that GenAI is predominantly applied to content generation and design ideation, yet developers face significant challenges in tool integration and express concerns about code quality and reliability. GenAI-associated tasks exhibit distinct distributions in task types, tool selection, and challenge profiles compared to traditional AI or non-AI tasks. This work bridges a key empirical gap in understanding GenAI-augmented software development in authentic settings, providing actionable insights for GenAI tool design, workflow integration, and community support mechanisms.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Philosophy and Ethics of AI: Artificial General Intelligence

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

The growing capabilities of generative AI (GenAI) have begun to reshape how games are designed and developed, offering new tools for content creation, gameplay simulation, and design ideation. While prior research has explored traditional uses of AI in games, such as controlling agents or generating procedural content. There is limited empirical understanding of how GenAI is adopted by developers in real-world contexts, especially within the open-source community. This study aims to explore how GenAI technologies are discussed, adopted, and integrated into open-source game development by analyzing issue discussions on GitHub. We investigate the tools, tasks, and challenges associated with GenAI by comparing GenAI-related issues to those involving traditional AI (TradAI) and NonAI topics. Our goal is to uncover how GenAI differs from other approaches in terms of usage patterns, developer concerns, and integration practices. To address this objective, we construct a dataset of open-source game repositories that discuss AI-related topics. We apply open card sorting and thematic analysis to a stratified sample of GitHub issues, labelling each by type and content. These annotations enable comparative analysis across GenAI, TradAI, and NonAI groups, and provide insight into how GenAI is shaping the workflows and pain points of open-source game developers.

Problem

• Study GenAI adoption in open-source game development
• Compare GenAI, TradAI, and NonAI tools and challenges
• Analyze GitHub issues to understand developer workflows

Innovation

• Analyzing GitHub issues for GenAI adoption
• Comparing GenAI, TradAI, and NonAI usage patterns
• Applying thematic analysis to developer challenges