EduIDEAtor: An AI-Powered Project Idea Generator for Data Science Education

  • 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-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.show moreshow less

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Author:Kamyab Farokhi
URN:urn:nbn:de:hbz:kob7-26102
DOI:https://doi.org/10.82549/opus4-2610
Place of publication:Koblenz
Referee:Frank Hopfgartner
Advisor:Stefania Zourlidou
Document Type:Master's Thesis
Language:English
Date of Publication (online):2025/11/18
Date of first Publication:2025/11/18
Publishing Institution:Universität Koblenz, Universitätsbibliothek
Granting Institution:Universität Koblenz, Fachbereich 4
Date of final exam:2025/11/13
Release Date:2025/11/18
Page Number:84
Institutes:Fachbereich 4 / Institute for Web Science and Technologies
Licence (German):CC BY-SA
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