How Do Computer Science Students Perceive Self-Study with Open-Source Repositories for Building AI/ML Systems?
Year: 2025
Type: conference-paper
Study design: Observational Study
Abstract: The world of software development has fundamentally changed because of the explosive growth of opensource repositories in recent years. Open-source repositories have become a valuable tool for software developers and researchers because they are free and usually easy to use. Likewise, learning Artificial Intelligence (AI) and Machine Learning (ML) skills are in high demand, especially among software engineering students, as they increasingly require AI skills to drive innovation, solve complex problems, and remain competitive. There are several AI/ML open-source projects that contain code explanations, e.g., comments and/or documentation, making them potential educational tools. However, it is currently unclear how well AI novices can benefit from these resources. Hence, we studied how computer science bachelor students perceive self-study with open-source repositories to build more complex AI/ML systems to gauge the usefulness of these repositories. After a learning period, we surveyed the perception and learning outcomes from the viewpoint of 112 students. By analyzing the responses, we found that 75 % of the students stated that they could now build complex AI/ML systems if provided with enough documentation and descriptions and are motivated to work on them. While this indicates that learning or improving AI/ML skills via open-source repositories is promising, more research beyond self-reporting is needed.
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W4411233979