SSRJAI Volume 3, Issue 6, 2026

Utilization of Teaching Platforms for Instructional Delivery Among Lecturers in Federal Universities South- South, Nigeria

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This study investigated the extent of utilization of teaching platforms for instructional delivery among lecturers in federal universities south-south, Nigeria. Two specific objectives, two research questions and two hypotheses were postulated to guide the study.  The study adopted descriptive survey research design. 280 computing and non-computing lecturers were drawn from a population of 980 lecturers from the eight (8) accredited federal universities in south – south Zone of Nigeria using a proportionate sampling technique. A researcher made instrument title “Teaching Platforms for Instructional Delivery among Lecturers’ Questionnaire” (TPFIDAL-Q)” was used to elicit data for the study. The instrument was subjected to face validation by three experts; subsequently, a reliability of   0.99 was obtained using the Alpha’s Cronbach technique. The instruments were administered by the researcher with the help of research assistants who were among the computing and non-computing lecturers in all the eight federal Universities in the zone. Mean statistics was used to answer the research questions, while Independent t-test was used to test the null hypotheses at 0.05 level of significance. The findings of the study revealed that computing and non-computing lecturers utilize teaching platform such as Google classroom to a great extent and moderate extent for instructional delivery respectively. For platform like WIZIQ is to very great extent and very little extent but there was a significant difference between computing and non-computing lecturers on the extent to which they utilize Teaching Platforms for instructional delivery. It is concluded that though non computing lecturers utilize Teaching platforms, the computing lecturers also utilize it to a great extent for instructional purposes than the non-computing lecturers. The researcher recommended among others that University lecturers should be encouraged by the university management to use Teaching Platforms for instructional delivery for effective learning and teaching.

Implications of Cloud Computing in the American Automobile Manufacturing

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Cloud computing has become one of the most transformative technologies in the global industrial environment. The American automobile industry is increasingly integrating cloud-based systems into manufacturing, supply chain management, customer relationship management, autonomous driving technologies, predictive maintenance, and data analytics. This journal paper investigates the implications of cloud computing within the American automobile industry by analyzing operational efficiency, cost reduction, cybersecurity concerns, sustainability, innovation, and future opportunities. The study uses secondary qualitative and quantitative analysis derived from industry reports, scholarly articles, and market data. Findings indicate that cloud computing significantly improves operational flexibility, manufacturing efficiency, real-time decision-making, and customer services while simultaneously introducing challenges related to cybersecurity, data privacy, infrastructure dependency, and regulatory compliance. The paper concludes that cloud computing will remain a foundational technology for the future growth of the American automobile sector.

Insider Threat Detection in Enterprise Communication Logs Using Transformer-Based NLP Models

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Insider threats represent a critical challenge in cybersecurity, often bypassing traditional perimeter defenses due to their legitimate access privileges. This paper proposes a transformer-based natural language processing (NLP) framework for detecting insider threats through the analysis of enterprise communication logs. Leveraging contextual embeddings and behavioral features, the model identifies anomalous patterns indicative of malicious intent. Experiments conducted on the Enron email dataset demonstrate the efficacy of the proposed approach, achieving an F1-score of 0.87 in threat classification. The study also addresses ethical considerations and proposes a human-in-the-loop review mechanism for deployment.

Distributed Database Management System Skills Need for Enhanced Job Performance of System Administrators in Universities in South-South, Nigeria

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The study investigated the distributed database management system skills required for enhanced job performance of system administrators in universities in Southern Nigeria. Two specific objectives, two research questions, and two null hypotheses guided the study. A descriptive survey research design was adopted. The population of the study comprised 387 lecturers and system administrators drawn from universities in Southern Nigeria. Using the Taro Yamane formula, a sample size of 197 respondents, consisting of 60 system administrators and 137 lecturers, was selected for the study. Data were collected using a structured questionnaire titled Distributed Database Management System Skills Need Questionnaire (DDMSNQ). The instrument was validated by three experts, while a reliability coefficient of 0.714 was obtained using the Cronbach alpha method. Data collected were analyzed using mean and Improvement Need Index (INI) to answer the research questions, while independent t-test statistics were used to test the null hypotheses at a 0.05 level of significance. The findings revealed that database security management and database modeling skills were required by system administrators for enhanced job performance in universities in Southern Nigeria. The study concluded that the acquisition of distributed database management system skills is essential for improving the efficiency and effectiveness of system administrators in universities. Based on the findings, the study recommended that university management should organize regular training and capacity-building programmes to equip system administrators with relevant distributed database management skills for improved job performance.

E-Learning Platforms’ Characteristics and Computer Education Students’ Academic Engagement in Federal Universities in South – South, Nigeria

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This study examined the extent to which e-learning platforms’ characteristics influence the academic engagement of Computer Education students in Federal Universities in South–South Nigeria. Seven specific objectives, seven research questions, and seven hypotheses were formulated to guide the study. The study adopted a descriptive survey research design. The population comprised 1,100 (400-level) Computer Education students in selected Federal Universities in the South–South region of Nigeria. A sample size of 286 respondents was drawn using Taro Yamane’s formula, while a simple random sampling technique was employed to select the participants. A 70-item instrument titled “E-learning Platforms’ Characteristics Influence on Academic Engagement Questionnaire (EPCIAE-Q)” was developed by the researcher for data collection. The instrument was subjected to face validation by three experts: one expert from Educational Technology Department University of Uyo, Uyo Akwa Ibom State, one expert from Measurement and Evaluation Department, University of Uyo, Uyo Akwa Ibom State and another one expert from Computer and Robotic Education Department University of Uyo, Uyo Akwa Ibom State. To determine the characteristics of the instrument, the validated questionnaire was administered to 30 Computer Education students from Federal Universities in the South–South region who were not part of the study sample. Cronbach’s Alpha statistics was used to establish the reliability coefficient of the instrument. The administration of the questionnaire was carried out by the researcher with the assistance of research assistants across the three Federal Universities in the region that offer Computer Education as a discipline. Linear regression analysis was used to answer the research questions and test the null hypotheses at the 0.05 level of significance.

Artificial Intelligence Readiness and Digital Competency Among Medical Social Workers at the University of Port Harcourt Teaching Hospital

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Although artificial intelligence is making its way into healthcare in various forms from decision support, triage and documentation tools to predictive analytics and patient-navigation aids, its readiness among medical social workers in Nigerian tertiary hospitals remains largely under-examined. This paper examines the readiness of medical social workers to use AI and their digital competency within the University of Port Harcourt Teaching Hospital, Rivers State. I employed an integrative desk-based evidence-synthesis research strategy, drawing on recent peer-reviewed studies, Nigerian digital health policy, AI governance guidelines, and the organisational context of the hospital’s Medical Social Services Department. Readiness was interpreted through an integrative sociotechnical lens, using digital access, health-data literacy, AI literacy, ethical judgement, professional identity, patient advocacy and organisational support as the main areas of analysis. The findings show that medical social workers occupy an important but exposed position in AI-supported health care; their patient work, psychosocial assessment, discharge planning, counselling, referral coordination and client advocacy can be enhanced by AI-assisted documentation and risk assessment, provided there are safeguards for confidentiality, consent, equity and professional judgement. Five readiness gaps were recognised: lack of AI-specific training, inconsistent digital workflow integration, weak profession-specific directives, data privacy and protection issues, and low interdisciplinary interaction in the use of AI-driven tools. The paper introduces a stepwise competency structure and implementation roadmap for UPTH, based on baseline assessment, capacity building and training, ethical governance, and limited monitored pilot utilisation before large-scale deployment. The article concludes that preparation for the use of AI among medical social workers should not be limited to technical skills alone, but should also support accountable, human-centred digital professionalism in social work by augmenting, rather than replacing, the core relational practice of hospital social work with vulnerable patient populations.

Investigating AI-Supported Learning Analytics for Enhancing Developmental Outcomes of Students in Nigerian Schools: A Mixed-Methods Study

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This study examined how AI-based learning analytics (LA) inform educators regarding students’ developmental needs and assess the predicted benefits and challenges within the Nigerian educational system. It employed two research questions, objectives and one hypothesis that offered a purposeful direction. The study utilised a convergent parallel mixed-methods research design. This design enabled the collection of quantitative survey data from 600 educators and students with qualitative data from semi-structured interviews and platform-generated analytics. The descriptive and inferential statistics were utilised for measuring the impact on instructional precision through Python algorithm. The NVivo was used to thematically explore stakeholders lived experiences. The analyses determined the effectiveness of predictive dashboards in flagging at-risk students and the role of machine-driven response toward reducing administrative workloads among educators. The findings offered that intermittent power supply, high data costs, and digital literacy gaps were the contextual barriers unique to the Nigerian schools. Through the triangulation of data sources, the finding further offered all-inclusive framework for integrating and optimising AI into West African secondary education. It was recommended that policymakers and educators should take advantage of utilising AI not only as a component of technological, but as a tool strategically enhancing holistic students’ developmental outcomes.