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All Journal Journal of Information Systems and Informatics Aceh International Journal of Science and Technology
Mafuhure, Tirivashe
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences

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Journal : Journal of Information Systems and Informatics

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AI-Based Assignment Marking in African Open and Distance e-Learning Institutions: A Systematic Review Mafuhure, Tirivashe
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1530

Abstract

The rapid growth of student enrolment in African Open and Distance e-Learning (ODeL) institutions has intensified pressure on assessment systems, particularly in assignment marking, moderation, and feedback provision. Artificial Intelligence (AI) offers a promising solution for improving the scalability, consistency, and timeliness of assessment processes. However, evidence on the implementation, effectiveness, and governance of AI-assisted assessment in African ODeL institutions remains fragmented. This study synthesised literature published between 2019 and 2025 to evaluate the extent to which African ODeL institutions have utilised AI techniques in assignment marking. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic search of major academic databases identified 18 studies that met the inclusion criteria. The review examined AI techniques used, assessment types, evaluation methods, and reported challenges. Findings show that Machine Learning (ML), Natural Language Processing (NLP), and generative AI are the most frequently applied techniques, mainly in text-based assessments such as essays and short-answer responses. Although studies report gains in grading efficiency, consistency, and feedback generation, adoption remains constrained by infrastructural limitations, fairness concerns, linguistic diversity, weak governance frameworks, and limited empirical validation. Sustainable implementation requires standardised human-AI workflows, robust evaluation frameworks, and clear ethical and regulatory guidelines.
Co-Authors Mbohwa, Charles Phahlane, Mampilo