Browsing by Author "Olanrewaju, Olaitan Toyyib"

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    An Enhanced Web-Based Examination System using Automated Proctoring and Background Activity Detection
    (Journal of Institutional Research, Big Data Analytics and Innovation (JIRBDAI.), 2025-08-20) Olanrewaju, Olaitan Toyyib; Isiaka, Mope Isiaka; Babatunde, Seyi Ronke; Jimoh, Muhammed Kamaldeen; Babatunde, Akinbowale Nathaniel
    The rise of online education demands sturdy systems to uphold academic integrity during remote assessments. This paper presents the design and implementation of a web-based examination platform integrated with automated, multi-modal proctoring tools to detect potential cheating. The system enables educators to create and manage diverse assessments while students’ complete exams in a monitored virtual environment. Key security measures include user authentication with facial recognition using the DeepFace library and continuous webcam analysis to detect mobile phone usage, unauthorized individuals, and unusual head or eye movements. Additional features include browser focus tracking to monitor navigation away from the exam window, and background audio analysis using Voice Activity Detection (VAD) and Root Mean Square (RMS) energy to flag suspicious sounds or conversations. Detected anomalies are logged in real-time for review. The prototype effectively combines visual, auditory, and behavioral monitoring into a cohesive framework, offering a comprehensive solution to reduce academic misconduct in remote settings. Functional testing showed 72% overall system accuracy and over 90%+ accuracy in specific detection modules. Future work should intend to enhance the system's AI capabilities, improve performance on models, and ensure fairness for all users, contributing to credible and secure digital learning environments across disciplines.
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    Assessing the Adequacy of Artificial Intelligence Tools for African Narratives: Towards Responsible and Culturally Inclusivity
    (FUW Trends in Science & Technology Journal, 2025-11-28) Isiaka, Mope Rafiu; Jimoh, Muhammed Kamaldeen; Abdulsalam, Olaniyi Sulaiman; Olanrewaju, Olaitan Toyyib
    The increasing reliance on Generative Artificial Intelligence (GenAI) across diverse domains has sparked global attention regarding its adequacy and accuracy. Specifically, its capacity to capture and represent African narratives remains underexplored. Most AI systems are developed within Western contexts fail to align with Africa's diverse sociocultural realities, thereby perpetuating biases, misrepresentation, and the erasure of indigenous knowledge systems. This study critically evaluates the performance of the ChatGPT, DeepSeek, Gemini, and Perplexity large language models in processing and representing African narratives. Using a mixed-methods approach, it incorporates systematic assessments of selected popular AI tools and qualitative input from domain experts in African linguistics, culture and technology. A survey involving academic staff from state and federal universities in Nigeria’s North-Central region contributed 24 relevant prompts, which were combined with 15 research-generated prompts, totalling 39. These prompts were executed concurrently on the selected AI models, and the resulting outputs were evaluated by subject-matter experts for accuracy, adequacy, and credibility. Perplexity consistently achieved the highest ratings across all parameters, whereas the other models displayed varying degrees of effectiveness. Notably, the findings revealed a “white as default” bias and a tendency to prioritise content from Eastern and Southern Africa. The study also identified serious gaps in the handling of African languages, idioms, and culturally embedded expressions, stemming from the poor representation of low-resource languages, limited infrastructure, skill deficiencies, and weak governance. In response, this study proposes roadmaps for responsible AI development tailored to African contexts, advancing ethical practices and amplifying African voices in the digital era.