The number of computer science students is rising significantly each semester. To meet the demand for prompt and effective feedback, particularly in programming tasks, it is essential to enhance Athena, a platform for (semi-)automated text and programming assessments, to facilitate a higher volume of (semi-)automated feedback. This thesis aims to develop and integrate two new modules into Athena, evaluating them based on resource consumption, including computational capacity and memory usage, and the relevance of their feedback to specific programming tasks. The module that demonstrates the most efficient performance, in terms of both operational efficiency and feedback applicability, will then be incorporated into Artemis, an online learning platform. These modules will be built upon the foundation of abstract syntax trees and code embeddings. The primary goal is to substantially reduce the grading workload for instructors and tutors, thereby increasing Athena’s role within the Artemis ecosystem.