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From AI Coursework to Research—Building a Unified Education and Research Environment | Inha University
2025.11.25.
As AI education and research expand across universities, the first challenge is often not how to teach or research AI, but how to provide a stable hands-on environment everyone can actually use. When student PC environments vary and researchers need different libraries and compute resources, both classes and research can stall well before any real model development begins.
In AI coursework, errors often appear during the setup of Python, CUDA, and PyTorch, and students without access to high-performance GPUs face limits even in basic training experience. In research, complex infrastructure setup and resource management make it hard for domain researchers to focus on AI model experimentation.
To solve this, the AI Convergence Research Center at Inha University built its own AI platform, EduBridge, on top of Mondrian AI's AI platform Yennefer. This gave the university one unified environment to run AI coursework, graduate research, and student challenges.
■ Standardizing the AI Education Environment with EduBridge on Yennefer
Founded in 2020 as Incheon's first AI-specialized research institute, the AI Convergence Research Center conducts research and education across vision AI, robotics AI, medical AI, and big data. It also runs an AI major within the university's general graduate school, producing master's and doctoral-level AI talent each year.
To run AI education and research more reliably, the center built EduBridge on Yennefer. Before Yennefer, differences in student development environments were a major burden in class operations. Every semester began with heavy setup time, and in non-major courses, that setup itself became a barrier to entry for AI learning.
After EduBridge, students could access a GPU-based hands-on environment through a browser—without complex installation. Instructors could design classes on the assumption that all students run code in the same environment, and class time shifted from environment setup to the essentials of AI learning: model architecture, data handling, and result interpretation.
■ A GPU-Based Experimentation Environment That Extends from Education to Research
Yennefer didn't stop at coursework. Inha University used EduBridge for graduate research and industry-academia projects as well. From 2022 to 2025, about 435 students used Yennefer across various courses and research environments—including the Graduate School of Logistics and the AI Convergence Project course.
One representative case is a daily logistics forecasting model developed by a team at the Graduate School of Logistics. The team combined delivery data from courier companies with geographic and socioeconomic data to build a model that expanded into forecasts by building use, region, and item type. The model was even applied to the Yongsan International Business District development plan—showing how AI models built in the lab can connect to real urban planning.
Throughout this work, Yennefer reduced the burden of configuring GPU servers or repeatedly setting up environments, letting researchers focus on data analysis and model experimentation. The platform gave a foundation not just to AI-trained researchers, but also to domain experts in logistics and urban planning who wanted to bring their research questions to life through AI models.
■ Letting Students Start AI Projects from the Same Starting Line
Inha University also used Yennefer to run student-driven AI challenges. In hackathons and challenges, the environment for actually training and implementing a model matters as much as the idea itself. Since not every student owns high-performance GPU equipment, differences in hardware access can translate into differences in project completeness.
By giving all Inha AI Challenge participants the same GPU environment, Yennefer let students focus on implementing ideas and refining models rather than working around hardware limits. Participants experienced the full flow of an AI project—handling data, training models, reviewing results, and iterating on code.
This went beyond simply providing a convenient hands-on environment. It opened AI experimentation to more students. With high-performance hardware no longer a personal prerequisite, students could take on AI projects under more equal conditions.
After adopting Yennefer, Inha University could run AI education, research, and student projects on one integrated platform rather than in separate environments. Students reduced the burden of setup and focused on learning and experimentation, while instructors and researchers expanded classes and research in a more stable environment.
The Inha University case shows that Yennefer is more than a GPU access environment—it can serve as the foundation for universities to operate AI education and research continuously. Through Yennefer, Mondrian AI helped the university open AI experimentation to more students and researchers, building an environment where coursework, research, and industry-academia projects all connect.
👉 To learn more about the Inha University Yennefer case, click the [Blog] button at the bottom right.
