Revolutionizing Newcomers' Onboarding Process in OSS Communities: The Future AI Mentor

2025-05-07

AI Summary

Newcomers to open-source software (OSS) communities face significant onboarding challenges due to fragmented guidance and a lack of expert mentorship. Method: This study proposes the “AI Mentor” conceptual framework—the first holistic, systematic, and personalized AI-guidance paradigm spanning the entire newcomer journey. Through a fiction-based participatory design study with 19 newcomers, we elicited 32 demand-driven design strategies, which informed the development of OSSerCopilot, a prototype AI copilot. We conducted rigorous usability and technology acceptance evaluations. Contribution/Results: The work bridges critical theory–practice gaps in under-supported yet high-priority onboarding phases—particularly interest-driven project discovery—demonstrating AI’s feasibility as a continuous, end-to-end companion rather than a point tool. It delivers empirically grounded, reusable design principles and actionable insights, establishing a foundation for AI-augmented, sustainable OSS community growth.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Teamwork

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Onboarding newcomers is vital for the sustainability of open-source software (OSS) projects. To lower barriers and increase engagement, OSS projects have dedicated experts who provide guidance for newcomers. However, timely responses are often hindered by experts' busy schedules. The recent rapid advancements of AI in software engineering have brought opportunities to leverage AI as a substitute for expert mentoring. However, the potential role of AI as a comprehensive mentor throughout the entire onboarding process remains unexplored. To identify design strategies of this ``AI mentor'', we applied Design Fiction as a participatory method with 19 OSS newcomers. We investigated their current onboarding experience and elicited 32 design strategies for future AI mentor. Participants envisioned AI mentor being integrated into OSS platforms like GitHub, where it could offer assistance to newcomers, such as ``recommending projects based on personalized requirements'' and ``assessing and categorizing project issues by difficulty''. We also collected participants' perceptions of a prototype, named ``OSSerCopilot'', that implemented the envisioned strategies. They found the interface useful and user-friendly, showing a willingness to use it in the future, which suggests the design strategies are effective. Finally, in order to identify the gaps between our design strategies and current research, we conducted a comprehensive literature review, evaluating the extent of existing research support for this concept. We find that research is relatively scarce in certain areas where newcomers highly anticipate AI mentor assistance, such as ``discovering an interested project''. Our study has the potential to revolutionize the current newcomer-expert mentorship and provides valuable insights for researchers and tool designers aiming to develop and enhance AI mentor systems.

Problem

• AI mentor for OSS newcomer onboarding challenges
• Design strategies for AI mentor in OSS platforms
• Gaps in AI mentor research for project discovery

Innovation

• AI mentor integrated into OSS platforms
• Recommends projects based on personalized requirements
• Assesses and categorizes project issues by difficulty