Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
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This Author published in this journals
All Journal IJIE (Indonesian Journal of Informatics Education) Journal of Information Systems and Informatics
Zvinodashe Revesai
Reformed Church University
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Digital Empowerment in Social Work: Leveraging AI to Enhance Educational Access in Developing Nations Zvinodashe Revesai; Benjamin Tungwa; Telson Anesu Chisosa; Vanessa Runyararo Meki
IJIE (Indonesian Journal of Informatics Education) Vol 8, No 2 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijie.v8i2.92951

Abstract

Social work education in developing countries faces significant challenges, including limited resources, restricted access to current knowledge, and inadequate training opportunities. This study aims to examine the potential of emerging Artificial Intelligence (AI) technologies in empowering social work students by enhancing access to information through machine translation and intelligent search tools, improving resource availability via virtual simulations and adaptive learning platforms, and integrating AI-powered self-help tools into the curriculum. A qualitative research design was employed, utilizing in-depth interviews with 16 educators and 8 field training officers, along with focus group discussions involving 24 social work students across selected institutions in Zimbabwe. All interviews were audio-recorded with participant consent, with translators assisting where necessary for local languages. Additional data were collected from documents, public reports, learning platforms, and policy papers to provide context on AI adoption strategies. Data were analyzed using thematic analysis, examining cases and models where AI has expanded access to scholarly materials through automated translation services, enabled localized resources through virtual training simulations, and facilitated the incorporation of culturally aligned self-help tools such as AI chatbots and wellness applications. The findings show that, with careful implementation and consideration of the context, artificial intelligence can reduce inequalities in education and enhance students' abilities through personalized learning paths, virtual environments for practice, and automated feedback systems. However, this research points out the need for addressing the digital divide and ethical issues associated with artificial intelligence, including problems of privacy and algorithmic bias. The study concludes by making a call for further research into models of safe and equitable AI integration in social work education.
Cost-Optimised IoT Architecture for Real-Time E-Waste Monitoring with Operational Validation Belinda Ndlovu; Zvinodashe Revesai; Kudakwashe Maguraushe
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.v8i2.1553

Abstract

Electronic waste (e-waste) is the fastest-growing solid waste stream worldwide, yet formal collection systems remain limited. Many existing Internet of Things (IoT) solutions emphasize advanced functionality at the expense of cost efficiency and practical deployability. This paper presents a cost-optimized IoT architecture for real-time monitoring of e-waste bins. The proposed system adopts a four-layer architecture integrating ESP32 microcontrollers, ultrasonic sensors for fill-level detection, and infrared sensors for monitoring, supported by a Node.js backend that provides real-time data updates. System validation was conducted through sensor calibration (n = 30), functional testing, stress testing, and cost-performance benchmarking against RFID-, GSM-, and LoRa-based alternatives. Experimental results demonstrate a fill-level accuracy of ±3.2%, temperature precision of ±1.8°C, system reliability of 97.3%, uptime of 98.7%, and an average latency of 2.1 s. The deployment cost was USD 78 per bin, which is approximately 40% lower than comparable RFID-based systems. In addition, the system reduced unnecessary collection trips by 35% and yielded an estimated return on investment (ROI) of 8.5 months. These results show that a low-complexity, cost-efficient IoT design can provide a scalable and practical solution for e-waste bin monitoring.
Co-Authors Belinda Ndlovu Benjamin Tungwa Kudakwashe Maguraushe Telson Anesu Chisosa Vanessa Runyararo Meki