Journal of Information and Technology

A Systematic Review of AI Anxiety in Ghana’s Tertiary Education: A Competency Framework for Effective Integration of AI in Academia

2025-12-23T16:09:33+00:00

The rapid integration of Artificial Intelligence (AI) into higher education globally has exposed a significant barrier to adoption, particularly AI anxiety among faculty and administrators. While this phenomenon is recognized, its manifestations and drivers in resource-constrained contexts like Ghana remain critically underexplored. Nonetheless, with a literature bias towards Western, individual-centric models. This study addresses this gap by conducting a systematic review of literature from 2019 onwards to investigate the nature of AI anxiety within Ghana's tertiary education sector. The findings reveal that anxiety is not primarily a symptom of individual technophobia but a rational response to a profound institutional void, a lack of clear policies, ethical guidelines, and reliable support infrastructure. Consequently, the study posits that prevailing models like the Technology Acceptance Model are insufficient for this context. The primary achievement of this research is the development of a novel Dual-Layered Competency Framework, which argues that sustainable AI integration requires the symbiotic development of institutional competencies (policy, infrastructure) and individual competencies (AI literacy, ethics). This reframing shifts the focus from remediating individual anxiety to building institutional resilience. The new knowledge created underscores that effective integration is a function of institutional readiness. For policymakers and university leadership, this implies that resource allocation must be strategically directed towards strengthening institutional governance and support systems as a prerequisite to, and enabler of, meaningful staff development. The study concludes that a holistic, institution-first approach is essential for mitigating anxiety and harnessing AI’s potential for academic advancement in Ghana and similar contexts.

Deep Learning-Based Brain Tumor Diagnosis on Smartphones Using Optimized MobileNetV2 Models

2025-11-24T11:49:39+00:00

Identifying brain tumors early and accurately is a significant way to improve patient outcomes, but access to numerous advanced diagnostic tools is not standardized across the world due to the cost and availability of MRI scans. We present a lightweight smartphone brain tumor diagnostic tool with deep learning–based diagnostic decision support in a contextualized way. We developed a convolutional neural network (CNN) based on MobileNetV2 for mobile deployment that allows for the processing of MRI images on consumer smartphones in real-time. The model was developed and validated on a publicly available brain tumor MRI dataset of glioma, meningioma, pituitary tumor, and normal cases, achieving an overall accuracy of 98% and classifying cases in less than 100 ms on standard iOS devices. This work demonstrates that with a lightweight architecture and on-device processing for the medical image, diagnostic decision support can be facilitated in a cost-effective, portable way, while also creating confidence factors in patient privacy, and represents an immense opportunity in lower-resourced clinical care, telemedicine, and point-of-care diagnosis around patients. It demonstrates another methodological option for the feasible implementation of advanced deep learning models to assist significant medical imaging workflows in a smartphone device.

Empowering Administrative and Technical Staff at the University of Cape Coast: Leveraging Prompt Engineering for Generative AI to Uphold Administrative Integrity in a Dynamic IT Era

2025-12-23T16:21:59+00:00

The rapid integration of Generative Artificial Intelligence into public sector administration presents a dualistic reality of enhanced efficiency and significant threats to administrative integrity, a tension acutely felt in emerging digital economies. This study addresses the critical absence of an evidence-based framework for leveraging prompt engineering to ensure Generative AI use by administrative and technical staff at the University of Cape Coast reinforces, rather than undermines, administrative integrity. Employing an exploratory sequential mixed-methods design, the research combined qualitative interviews and a quantitative survey to first explore staff practices and then measure prevalent risks. The study achieved its purpose by diagnosing a bifurcated risk structure—comprising substantive Administrative Integrity and procedural Transparency & Accountability concerns—and subsequently developing a novel, theoretically-informed framework for prompt engineering. This framework fills a crucial gap in the literature, which has largely overlooked micro-practices of AI use in African administrative contexts, by providing a multi-layered governance model anchored in Public Value, Institutional, and Technology Acceptance theories. The new knowledge created demonstrates that integrity-preserving AI use requires an integrated system of institutional governance, iterative practice, and robust oversight, moving beyond technical skill-building. The study positions prompt engineering as a vital mechanism for upholding due process and due diligence. Key policy implications include the need to mandate structured prompt engineering literacy, institutionalise curated prompt libraries, and formally integrate AI accountability matrices into administrative procedures to guide effective resource allocation and decision-making for sustainable digital transformation.

Is Integrated Convenience the New Security King? Evaluating the UniFi EFG's Challenge to FortiGate's Enterprise Dominance in the African Context: Lessons from the University of Cape Coast

2025-12-23T15:42:34+00:00

This study critically evaluates the challenge posed by Ubiquiti's UniFi Enterprise Fortress Gateway (EFG) to the established dominance of Fortinet's FortiGate in the African higher education context, using the University of Cape Coast (UCC) as a case study. It addresses the significant disconnect between globally prescribed, high-cost enterprise security models and the operational realities of African universities, which are characterized by chronic underfunding, limited technical staff, and infrastructural instability. Employing a comparative case study methodology, the research analyzes vendor datasheets, independent lab reports, and user reviews to assess both platforms across performance, total cost of ownership (TCO), and organizational fit, framed by the Technology-Organization-Environment (TOE) framework and Resource-Based View (RBV). The findings reveal a performance parity of approximately 90% between the EFG and FortiGate 600F in core security functions, coupled with a dramatic 75-80% reduction in TCO for the EFG. The study concludes that while FortiGate remains a powerful resource for well-resourced core networks, the EFG's "integrated convenience" model, characterized by operational simplicity, a capex-focused financial model, and built-in resilience, represents a strategically superior fit for the network edge of resource-constrained institutions. The research contributes a novel, empirically-grounded hybrid architectural model and provides policymakers with a context-driven framework for technology selection, advocating for a redefinition of "enterprise-grade" based on sustainable performance-to-cost and organizational alignment rather than vendor prestige.