The Journal of Informatics

Association Between Artificial Intelligence and Academic Integrity in Higher Education in Tanzania

Wiljames Mmari — 2025-12-27

The study assessed the association between artificial intelligence (AI) use and academic integrity among higher education students in Tanzania by specifically examining the relationship between the frequency of AI use, the purpose of AI use, and the perceived usefulness of AI with students’ adherence to academic integrity. Data were collected from 154 bachelor's degree students from the University of Dar es Salaam and the Institute of Finance Management using a structured questionnaire and analysed using descriptive statistics and multiple regression analysis. The findings reveal that the purpose of AI use, frequency of use, and perceived usefulness are all significantly associated with academic integrity, with the purpose of use showing the strongest influence. Therefore, responsible AI use can enhance learning efficiency, while misuse may undermine integrity. It recommends that higher education institutions develop clear policies, ethical guidelines, AI literacy programmes, and faculty training to ensure AI supports learning while maintaining academic standards.

User-Centred Design and Usability of Visual Analytic Interface Media: An Empirical Study of PhD Students of Federal University of Technology, Minna, Nigeria.

Muhammad Murtala Ibrahim — 2025-12-30

This paper studies the user-cantered design principles and usability of VAIs among PhD students in Nigeria. A mixed-methods approach was used to evaluate students' interactions with an existing visual analytics system (www.odvas.com), identifying the usability of the visual analytics interface and exploring the essential features of a user-centred visual analytics interface among students. Data were collected from 45 PhD students in the Department of Library and Information Science at the Federal University of Technology, Minna, in Nigeria, using questionnaires, system usability testing, and interviews. Participants expressed a desire for improved data presentation, customizable dashboards, and better interactive features, and the findings also indicated a positive correlation between Ease of Use vs. User Satisfaction: - r = 0.79, which indicates a strong positive relationship, meaning that as students find the system easier to use, their satisfaction with the system increases. The higher the perceived ease of use, the happier the users are likely to be. - p < 0.01: indicates that the result is statistically significant and the likelihood of this correlation happening by chance is less than 1%. On Interactivity vs. Efficiency: - r = 0.72 indicates a positive correlation between interactivity and efficiency, as the system becomes more interactive, users' task efficiency improves. However, usability challenges such as a steep learning curve, inconsistent design, and limited customisation options were identified. Developers and designers are recommended to develop more user-friendly and efficient VAIs. LIS educators should include VAIs training to meet the needs of LIS students.

Development of an Adaptive Traffic Light Signals Control Model Based on Long Short-Term Memory (LSTM) Network and Deep Q-Network: A Case of Mwenge Intersection, Dar es Salaam City, Tanzania

Isakwisa Gaddy Tende — 2025-12-30

Urban traffic congestion is a major challenge in cities like Dar es Salaam. Traditional fixed-time traffic light systems fail to respond to real-time traffic changes, causing long waits, fuel wastage, and pollution. This study proposes a hybrid traffic light control model for Mwenge intersection using Long Short-Term Memory (LSTM) and Deep Q-Network (DQN) models. The LSTM predicts short-term traffic volumes, which regulate vehicle flow in the SIMULATION OF URBAN MOBILITY (SUMO) simulation environment. At the same time, the Deep Q-Network (DQN) adaptively adjusts signal phases based on real-time traffic states. Traffic data collected over 240 hours was used for model training and simulation. The LSTM achieved an average Test Mean Absolute Error (MAE) of 3 vehicles/min across all directions, indicating accurate traffic prediction. The Deep Q-Network (DQN) improved intersection performance with Average Wait Time (21.6s), Queue Length (9.2 vehicles), and Throughput (888 vehicles), outperforming the traditional model (38.4s, 17.1 vehicles, and 712 vehicles, respectively). Overall, the hybrid LSTM-DQN model reduced wait times by 43.8%, queue lengths by 46.2%, and increased throughput by 25%. The proposed approach offers an adaptive, cost-effective solution for optimising traffic signals in resource-constrained cities like Dar es Salaam, supporting smarter, more sustainable urban mobility. Policymakers are encouraged to adopt this model for busy intersections in Tanzania.

Assessment of Data Security Interventions Among Tour Operating Firms in Tanzania

Maximillian Mgina — 2025-12-30

This study assesses the effectiveness of data security measures among tour operating firms in Arusha City, Tanzania, using a quantitative, cross-sectional survey research design. The targeted population included approximately 400 registered tour companies, from which a sample of 50 firms that experienced cyber-attacks or data breaches in the past five years was selected through simple random sampling. Data were analysed using descriptive statistics and inferential tests, including the Chi-square test, and presented through plots, charts, and graphs. Findings reveal high awareness (94%) of security measures such as firewalls, antivirus software, encryption techniques, and secure passwords. Significant associations were found between the effectiveness of practical interventions and factors such as data encryption methods, data backups, and the presence of dedicated security teams. In contrast, access controls and risk assessments showed no significant correlation. Challenges identified include resource constraints, technological complexity, employee training gaps, and regulatory compliance, underscoring the human and organisational aspects of cybersecurity. The study highlights the need for dynamic educational programs, continuous research, industry-specific studies, and interdisciplinary collaboration to enhance data security in the tourism sector. Implications suggest that adopting adaptive, holistic security strategies and fostering a culture of compliance can improve resilience against emerging cyber threats in tour operating firms in Arusha and similar contexts.

Optimizing Urban Traffic Congestion Through Artificial Intelligence and Machine Learning: A Case Study of Dar Es Salaam City

Lazaro Kumbo — 2025-12-27

Urban traffic congestion remains a defining obstacle to efficient mobility and infrastructure use in rapidly growing cities. In Dar es Salaam, particularly along Morogoro Road, static lane configurations are poorly matched to dynamic, directional traffic patterns, producing peak-direction bottlenecks and underutilised capacity in the counterflow. This study evaluates a Dynamic Lane Allocation Logic (DLAR) coupled with a Utilisation Efficiency (UE) metric to optimise lane use in real time. Using observed directional volumes and a standardised urban lane capacity of 1,800 pcu/h, we operationalise UE to compare baseline static (2+2), dynamic (e.g., 4+1), and AI-adaptive configurations (up to 5+1). Scenarios are implemented and tested in the Simulation of Urban Mobility (SUMO) platform to ensure reproducible, controlled experiments. Results indicate that dynamic configurations substantially improve efficiency, with AI-adaptive allocations achieving UE values of up to 95% during peak periods, compared to approximately 60% under static control. These findings underscore the value of data-driven, context-aware lane management and support near-term pilots of AI-based traffic control, investment in real-time sensing and monitoring infrastructure, and the adoption of localised capacity benchmarks in planning practice. For sub-Saharan African cities facing similar constraints, the approach offers a pragmatic pathway to relieve congestion without costly physical expansion, aligning day-to-day operations with the realities of fluctuating urban demand.