Developing high-quality quiz questions within Artemis currently necessitates significant manual effort, deep domain expertise, and strict alignment with course contents. Consequently, editors face challenges in maintaining robust question pools, often resulting in limited practice material for students. This thesis proposes integrating generative artificial intelligence (AI) into the Artemis platform to streamline the quiz creation lifecycle.
The proposed solution establishes a human-in-the-loop workflow where editors define specific constraints, such as topic and question count, to produce structured initial drafts. Furthermore, generation quality is enhanced by utilizing internal platform data like course content and learning competencies. Finally, a dedicated refinement layer empowers editors to iteratively adjust drafts via natural language instructions prior to final approval.