Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Information Systems and Informatics
Malusi Sibiya
University of South Africa
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
AI-Enabled E-Learning in Correctional Centres: A Systematic Literature Review Molefe Maloma; Malusi Sibiya; James Chibueze
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1538

Abstract

Artificial Intelligence (AI) is increasingly transforming education through personalization, automation, and data-informed decision-making, yet its application in correctional education remains underexplored. This study examines the implementation of AI-enabled e-learning in correctional centres worldwide, emphasizing secure, adaptive, and ethical deployment. A systematic literature review following the PRISMA framework analysed peer-reviewed studies published between 2019 and 2025. No empirical studies were identified that evaluated fully AI-enabled learning systems on-site in correctional settings. Instead, the available evidence focuses on AI-adjacent educational technologies, secure offline platforms, and adaptive learning management systems. Findings indicate that digital education is increasingly feasible in correctional environments, although implementation is constrained by security protocols, limited infrastructure, and resource availability. Reported benefits include improved learner motivation, autonomy, and digital literacy, while evidence linking higher education to reduced recidivism remains cautious. AI-driven adaptive and personalized learning therefore remains largely aspirational. Significant ethical and legal gaps also persist, particularly regarding algorithmic bias, privacy, and data governance. Correctional education is consequently at a transitional stage requiring stronger regulation, participatory design, and longitudinal research to ensure that AI supports rehabilitation without reinforcing existing inequalities.
Machine Learning for Software Deployment in the Public Sector: A Systematic Review and Research Agenda for African Contexts Johnson Nuviadenu; Themba Masombuka; Ernest Mnkandla; Malusi Sibiya
Journal of Information System and Informatics Vol 8 No 4 (2026): August
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i4.1839

Abstract

Failures in deploying digital public services can disrupt essential systems and affect millions of citizens. Machine learning (ML)-based software deployment decision support, including build risk prediction, release gating, autoscaling, rollback assistance, and post-deployment anomaly detection, offers opportunities for safer and more reliable releases. However, the extent of existing evidence in public-sector environments, particularly within African institutions, remains unclear. Following the PRISMA 2020 guidelines, this systematic literature review searched five databases using predefined inclusion criteria and a six-item quality assessment. A total of 33 peer-reviewed studies published between 2018 and 2025 were included, while studies focusing exclusively on MLOps were excluded. The findings reveal that none of the reviewed studies (0/33) explicitly evaluated ML deployment decision support in public-sector contexts or African institutions; existing evidence originates primarily from private-sector or unspecified environments. Research efforts are concentrated on autoscaling (12/33, 36%) and build prediction (9/33, 27%), with tree-based models being the dominant approach (16/33, 48%). Furthermore, only one study (3%) reported statistical significance testing or confidence intervals. This review identifies a research and evidence gap rather than confirming the absence of practical adoption. It proposes a staged research agenda toward explainable, lightweight, and context-aware ML deployment support for African public institutions.
Co-Authors Ernest Mnkandla James Chibueze Johnson Nuviadenu Molefe Maloma Themba Masombuka