Feedback Generation for Automatic Programming Assessment Utilizing AI Techniques: An Initial Analysis of Systematic Mapping Studies

Automated Programming Assessment (APA) has evolved into an essential and effective method of assisting both students and educators in the process of learning programming. It significantly reduces the burden on lecturers associated with programming assessment activities while also providing students with proper support for summative and formative assessment, particularly in achieving the course learning outcome. However, there is still a need to provide proper formative assessment feedback related to static analysis so that students can improve the quality of their programming codes and consistently progress forward at the end of their learning process. Despite several early attempts to integrate APA approaches to static analysis and Artificial Intelligence (AI) techniques, which are critical assessment components in course learning outcomes. This paper reveals an initial analysis of the Systematic Mapping Study (SMS) related to the integration of AI with APA used to support feedback generation for static analysis of APA. This review will contribute to the current state of AI techniques used in APA, as well as the types of assessment feedback that have been primarily focused on the context of these studies.