Automatic Exam Correction System Involving XAI for Admission to Public Higher Education Institutions: Literature Review

Abstract

The process of correcting entrance exams is an essential procedure for assessing the academic performance of student candidates and ensuring fairness and accuracy in the awarding of marks for their future selection. Most lecturers at Angolan higher education institutions carry out the corrections manually, especially subjective corrections. Due to the high number of students, ensuring a high-quality correction process while meeting institutional deadlines becomes challenging. In this context, this article aims to find the techniques and metrics that are used for the automated correction process of assessments with discursive questions, involving Explainable Artificial Intelligence (XAI). This literature review follows the PRISMA 2020 methodology and includes studies from three bibliographic databases: ACM Digital Library, IEEEXplore and Science Direct. The results obtained show that the use of a combination of similarity measures and Natural Language Processing (NLP) provides greater efficiency for the automated correction of discursive questions.