Translating Open Source Lessons with AI (Without Breaking Them)
- 時間
- 2026-08-09 13:40 ~ 14:10
- 講者
- Kozo Nishida
- 位置
- TR410
- 共筆
議程簡介
Open source educational materials often come with their own “dialects” of Markdown. For example, The Carpentries Workbench extends Markdown with special directives, metadata blocks, and conventions that are essential for building lessons. These are not just formatting details—they are part of the executable structure of the content.
Today, AI tools make it easy to translate documentation quickly. You can generate a full draft in seconds. But if you’ve tried this on structured teaching materials, you may have seen what happens next: broken builds, modified syntax, or translations that ignore the intent of the lesson design. AI is powerful, but it doesn’t automatically understand which parts of a document must never be touched.
In this talk, I’ll share a practical approach to solving this problem using “agent skills” designed specifically for translating structured technical content. These skills act as guardrails: they protect non-translatable syntax while allowing AI to focus on natural language parts. The goal is not just speed, but safe and maintainable translation workflows.
I’ll present a real-world example from the Bioconductor community, where we applied this approach to translate Carpentries-style training materials. With these agent skills, we were able to significantly accelerate translation while keeping lessons fully buildable and consistent.
More importantly, this is not just a tooling story—it’s a community story. These translated materials are now being used in workshops across different regions, enabling participants to learn in their own languages. Translation becomes more than a task; it becomes an entry point for contributing to open source. You don’t need to start with code—you can start by helping your local community access knowledge.
At COSCUP, where community and openness are at the center, this talk aims to share both practical techniques and a perspective: AI can amplify community efforts, but only if we design workflows that respect how open source projects actually work.
If you care about documentation, localization, or making open source more inclusive, this talk is for you.
講者
Kozo Nishida
Kozo Nishida is a Technical Scientist at RIKEN, where he works at the intersection of life sciences and open source software. His work focuses on improving the interoperability, reproducibility, and accessibility of computational research, particularly in metabolomics and bioinformatics.
With over a decade of experience supporting scientific research environments, Kozo has contributed to the development and maintenance of research software and data standards. He is actively involved in international open science communities, including the Bioconductor community and global working groups around data standards such as mzTab-M. His recent work explores how to connect experimental data formats with downstream analysis and visualization tools to enable more seamless scientific workflows.
Kozo is also deeply engaged in community building and education. He has been involved in organizing events such as PyData meetups in Japan and community-driven workshops, and he contributes to initiatives that promote inclusive participation in open source. As part of his work with multilingual efforts in open science communities, he has supported the translation and localization of technical training materials, helping make them accessible to a broader audience.
In addition to his contributions to research software and community activities, Kozo is exploring the use of AI tools to enhance developer productivity and knowledge sharing. His recent efforts include designing agent-based workflows for translating structured technical documentation while preserving its integrity—bridging the gap between automation and human-centered open source practices.
Through his work, Kozo aims to lower barriers to participation in open science and to foster collaboration across languages, disciplines, and communities.