This thesis tackles a common bottleneck in data-science courses: students struggle to turn a broad interest into a focused, workable project idea. This thesis set out to design and evaluate a compact assistant—EduIDEAtor—that makes this first mile simpler and more intentional. The tool uses a text-first interface with plain inputs, a small set of clearly different directions, and quick, reversible edits so students can steer ideas without losing momentum. After building and iterating the web appli- cation, The thesis evaluated how students experienced it and how it compared with familiar, non-AI brainstorming. The findings are consistent: navigation and input clarity were strong; students felt more able to generate and shape ideas; overall sat- isfaction and willingness to continue using the tool were high. Two practical refine- ments emerged—make back navigation clearly visible and give users finer control over how broad or specific the suggestions are both achievable without changing the core design. The contribution is a concrete pattern for first-mile ideation and a set of actionable guidelines for course-level adoption.
Kamyab Farokhi (2025) studied this question.