Artemis and Athena help instructors create and assess digital learning activities in large courses, including programming exercises, quizzes, and modeling tasks. This thesis focuses on two practical challenges. First, exercise creators spend a lot of time writing grading instructions from scratch. Second, students can find Athena feedback hard to understand when the interface shows little code context and does not clearly mark feedback as positive or negative.
To address these challenges, the thesis adds LLM support that creates a first draft of grading instructions during exercise creation. It also improves how Artemis shows Athena feedback for programming exercises by adding scores and clearer code context. Together, these changes can reduce manual authoring effort and make feedback easier for students to understand.