arXiv subjects

Abayomi O. Agbeyangi

Publications and source records attributed to Abayomi O. Agbeyangi.

4 recordsLinked to original sources

arXiv2026

AI-Driven Feedback Systems, Digital Labour, and Silent Quitting: Transforming African Workplaces

The current trend of digitalisation has revolutionised the organisation of work and the way it is measured and performed across the globe, with AI becoming more common for managing labour and performance, as well as employee communication. In African organisations, where there is increasing adoption of remote work, hybrid models of work, digital collaboration, and data-based HR management, the notion of silent quitting has become more relevant, defined as worker disengagement when employees are still doing their job but do not put any effort into achieving good performance and exhibiting any emotion. This paper investigates how AI-driven feedback mechanisms, including sentiment analysis systems, pulse surveys, chatbots, engagement dashboards, and predictive analytics, are changing African workplaces through offering continuous listening, instant performance information and proactive engagement with employees. The study also explores how AI can assist organisations in identifying early disengagement and enable intervention and better employee communication in both private and public sector organisations in Africa. At the same time, we address the challenges of socioeconomic development and governance posed by AI implementation in developing countries, including digital inequality, infrastructure shortcomings, privacy concerns, algorithmic bias, and the risk of workplace surveillance. By situating silent quitting within wider debates on digital labour and automation, the paper contributes an African-centred perspective to discussions on the future of work and offers practical recommendations for HR professionals, managers, policymakers, and technology developers seeking responsible, context-sensitive approaches to workplace transformation across the continent.

cs.CY↗

arXiv2026

Mapping the Artificial Intelligence Divide in Africa: Infrastructure, Accessibility and Capacity

Artificial Intelligence (AI) has the potential to be transformative for development, but Africa is currently facing a fragmented and challenging "AI divide". This paper provides an empirical analysis of the current state of the AI landscape and how it compares with Africa's technological preparedness for the future. In our analysis, we approach the "AI Divide" from three angles: infrastructure, accessibility, and human capacity. First, we look at the physical constraints that prevent Africa from integrating digitally. We then evaluate the human-centred factors that limit the development of AI technology on the continent. Finally, we examine the human capacity to develop AI systems on the continent and provide three focused case studies. Our investigation shows that the physical infrastructure needed to build an AI economy on the continent is lagging, with only 38% internet penetration, poor broadband coverage and less than 1% of all data centres globally. Other constraints include high data costs relative to income, gender-based digital divides, and the need to build more representative NLP models that can understand Africa's native languages. However, there are positive trends towards the emergence of local initiatives and grassroots movements, such as startups and universities, contributing to AI development on the continent. Based on these findings, we provide concrete recommendations to policymakers to help develop a more comprehensive and equitable AI ecosystem on the African continent.

cs.CY↗

arXiv2026

Text Detoxification in isiXhosa and Yorùbá: A Cross-Lingual Machine Learning Approach for Low-Resource African Languages

Toxic language is one of the major barrier to safe online participation, yet robust mitigation tools are scarce for African languages. This study addresses this critical gap by investigating automatic text detoxification (toxic to neutral rewriting) for two low-resource African languages, isiXhosa and Yorùbá. The work contributes a novel, pragmatic hybrid methodology: a lightweight, interpretable TF-IDF and Logistic Regression model for transparent toxicity detection, and a controlled lexicon- and token-guided rewriting component. A parallel corpus of toxic to neutral rewrites, which captures idiomatic usage, diacritics, and code switching, was developed to train and evaluate the model. The detection component achieved stratified K-fold accuracies of 61-72% (isiXhosa) and 72-86% (Yorùbá), with per-language ROC-AUCs up to 0.88. The rewriting component successfully detoxified all detected toxic sentences while preserving 100% of non-toxic sentences. These results demonstrate that scalable, interpretable machine learning detectors combined with rule-based edits offer a competitive and resource-efficient solution for culturally adaptive safety tooling, setting a new benchmark for low-resource Text Style Transfer (TST) in African languages.

cs.CL↗

arXiv2025

Is social media hindering or helping Academic Performance? A case study of Walter Sisulu University Buffalo City Campus

Social media platforms are popular among higher education students and have seen increased usage for academic purposes, especially during the COVID-19 pandemic. However, excessive use of social media can negatively impact students' academic performance. This preliminary study examines social media's impact on students' academic performance at Walter Sisulu University (WSU), Buffalo City campus. Using a positivist paradigm and a quantitative approach, randomly sampled data were collected from 71 students through a survey to identify trends and generate preliminary insights. Results indicate that while social media can facilitate academic work, it predominantly acts as a distraction, negatively affecting academic performance, particularly for first-year students. Notably, 84.5% of the students spend more than four hours daily on social media, and 39.4% agree that it negatively impacts their assignment completion. The study underscores the need for students to balance their social media use and academic responsibilities, highlighting the importance of this issue. Recommendations for achieving this balance, such as adopting time management strategies and integrating social media into teaching methodologies, are discussed.

cs.CY↗