Although computer science enrollments have recently declined, student numbers remain high and still require scalable teaching tools. This project enhances Artemis and its Artificial Intelligence (AI) subsystem, Athena, to automate feedback. Currently, fragmented interfaces create excessive instructor workloads and a disjointed student experience. These structural issues hinder the adoption of automated tools and reduce the educational value of the feedback provided.
This thesis unifies the assessment ecosystem by generalizing logic across exercise types. Refactoring the architecture into a single service layer eliminates technical debt and ensures data synchronization. A redesigned interface reduces workflow complexity for instructors, while integrated feedback allows students to engage directly within their submissions. This research improves usability, supports adoption, and enhances learning outcomes by providing a consistent AI-Feedback interface design for instructors and students.