Timely and individualized feedback is essential for students' learning progress and motivation, yet providing such feedback has become increasingly challenging due to growing student numbers. This has resulted in a time-consuming, repetitive, and often manual task for educators, contributing to a high workload. This paper presents DAFeeD, an LLM-based approach for automated feedback on student submissions across various exercise domains. The defined feedback process enables interactive learning by allowing students to submit solutions multiple times and automatically receive iterative LLM feedback on their submission attempts before deadlines. By incorporating task details, grading criteria, student solutions, and custom instructions into the prompt, DAFeeD provides clear, personalized, and pedagogically meaningful feedback to support continuous improvement. To evaluate the feedback process, we implemented DAFeeD in an open-source reference implementation integrated into the learning platform Artemis. A controlled study with students working on a programming task in a supervised environment showed that students found the feedback relevant and beneficial. They reported feeling more comfortable and willing to request automated feedback due to its convenience and immediacy. Additionally, deploying DAFeeD in a software engineering course with 450 students demonstrated improvements in student performance and encouraged iterative refinement through multiple submissions. These findings highlight DAFeeD's potential to enhance feedback processes in computing education, improving both learning efficiency and student outcomes.

@inproceedings{0002DK25,
  title = {Direct Automated Feedback Delivery for Student Submissions based on LLMs},
  author = {Maximilian Sölch and Felix T. J. Dietrich and Stephan Krusche},
  booktitle = {FSE},
  year = {2025},
  doi = {10.1145/3696630.3727247}
}