The sharing session explores practical applications of AI in academic research. Through hands-on examples, it covers workflows ranging from building interactive simulation models based on theoretical equations to automating large-scale statistical analyses in Jupyter Notebooks. The presentation also addresses routine academic tasks such as literature synthesis, ideation, and text production.
A central focus of the talk is the issue of quality control: How can AI-generated results be validated and quality-assured when working at scale with large datasets or numerous output files? The session concludes with a discussion on ensuring traceability and maintaining academic standards within AI-driven research pipelines.
