Anthony Ojo

In recent years, reliance on data-driven adaptive learning technologies has increased substantially within virtual learning environments. However, their application within gamified learning environments may introduce unintended bias in the interpretation of learner engagement. This study investigates how fairness-aware learning analytics can inform adaptive gamification to support inclusive and sustainable virtual learning. The Open University Learning Analytic Dataset (OULAD) was adopted and used in analysing behavioural, demographic, and assessment data for students with or without disabilities. This was with the purpose of examining patterns of engagement and predicting academic achievement among these groups of students. Both logistic regression and gradient boosting predictive models were employed and evaluated using performance metrics and fairness measures. The results show that students with disabilities recorded lower levels of early engagement, while the logistic regression model achieved high overall accuracy but produced inconsistent outcomes across learner groups. Findings also reveal that the application of fairness-aware threshold calibration brought about reduction in group-level differences with sustained predictive performance. These findings indicate the potential for integrating fairness-aware analytics into adaptive learning systems for the purpose of supporting balanced engagement and promoting inclusive gamification. This study therefore provides actionable guidance for the development of ethical and sustainable data-driven learning technologies.

Inclusion in recruitment remains a critical challenge in the evolving landscape of work, particularly for individuals with neurodivergent cognitive profiles. This study investigates the potential of adaptive Artificial Intelligence (AI) assessments to foster more inclusive hiring practices for neurodiverse talent. Using thematic analysis and simulation-driven data, the research explores how AI systems can be optimized to support equity, reduce cognitive bias, and enhance candidate-job alignment. Grounded in Adaptive Theory, Evidence-Based Rationale, and the Input-Environment-Output (I-E-O) framework, the study models an AI-powered recruitment pipeline using psychometric clustering and retention data. Results reveal that while AI tools can personalise assessments and improve hiring outcomes, they also risk amplifying bias if not calibrated across diverse cognitive profiles. The study emphasizes the importance of neurodiversity-aware model tuning, participatory design involving neurodivergent individuals, and the integration of fairness metrics such as demographic parity and equal opportunity. The findings contribute practical insights and a replicable framework for advancing inclusive hiring through AI-enabled systems.

Microfinance institutions frequently operate with sparse, imbalanced, and incomplete borrower records, which limits the reliability of traditional credit-scoring models. This study proposes an AI-driven micro-loan eligibility framework designed specifically for low-data environments by integrating transfer learning, domain adaptation, and multi-level explainabil-ity. Using a FICO-derived micro-lending dataset (9,578 records, 14 variables; 83.99% vs 16.01% class imbalance), baseline models trained on constrained data fractions (1–100%) exhibited increasing ROC-AUC but declining recall, confirming well-documented challenges of data scarcity in microfinance. (up to 0.6439 ± 0.0145 at 100%) (from 0.2500 ± 0.0559 at 1% to 0.1211 ± 0.0323 at 100%), confirming the well-documented challenges associated with data scarcity in microfinance. To address this limitation, pretrained gradient-boosted models were fine-tuned on limited target samples, and importance-weighted domain adaptation (SSWMD) was employed to mitigate source–target distribution mismatch. These approaches yielded substantial improvements, increasing ROC-AUC from 0.6308 (scratch, 10%) to 0.8061 (fine-tuned) and 0.8484 (domain-adapted), with the pretrained full-data reference achieving 0.9496. This result supports prior evidence on the value of cross-domain knowledge transfer in financial risk modeling. Expanded SHAP analysis (global, dependence, and local explanations) identified consistent key predictors, supporting transparency, practitioner trust, and regulatory accountability. Overall, the results demonstrate that accurate, interpretable, and data-efficient micro-loan eligibility assessment is achievable, even under severe data constraints, through the use of transfer learning and domain adaptation.

Soft-Loan application programmes are increasingly leveraging machine-learning (ML) techniques to automate credit eligibility decisions. Despite high predictive performance, many models retain a " black-box " character, which limits interpretability, preventing regulatory and compliance which has raised the challenge of bias especially with the intending soft-loan customers who mostly did not have documented credit history or regular bank statements. Preceding studies on explainable AI (XAI) in credit approval and risk management majorly dues on traditional loans, with little focus to ethical and explainable decision to be concluded for commonly acceptable soft-loan systems. To bridge this gap, this work has comes up with an explainable soft-loan approval framework that combines predictive techniques with SHAP and LIME method on explainability and no limitation on age bracket. Experimenting with the Give Me Some Credit dataset, Logistic Regression, Random Forest and XGBoost models were assessed and XGBoost retained the highest output with performance as (AUC = 0.8674, Recall = 0.7696). SHAP exposed the usage, disadvantageous record and debt issue as the focus expectation while LIME gave insightful case studies for interpretation. Non bias assessment displayed average difference among the age brackets (Accuracy STD = 0.1045; Recall STD = 0.0894), emphasising the importance for justified techniques deployment. The outcome describes that integrating XGBoost with XAI produces an interpretable, auditable and non-biased approach to the soft-loan decision model.

The integration of AI into education, governance, and leadership reshapes how institutions operate, make decisions, and deliver services. From personalized learning platforms and automated administrative processes in schools to data-driven policymaking and strategic planning in leadership contexts, AI offers opportunities to enhance efficiency, equity, and effectiveness. However, alongside these innovations come concerns about ethical use, data privacy, and algorithmic bias. Examining the adoption and impact of AI requires a balanced understanding of its transformative potential and the ethical frameworks guiding its responsible use.

AI in Education, Governance, and Leadership: Adoption, Impact, and Ethics explores the integration of intelligent technology into educational administration, policy, and instruction. It examines various challenges associated with AI, including its effective adoption, potential impact, and ethical dilemmas. This book covers topics such as educational governance, special education, and digital technology, and is a useful resource for educators, policymakers, computer engineers, academicians, researchers, and data scientists.

This study developed and evaluated an integrated intrusion detection and prevention (IDP) model for Moodle Learning
Management System (LMS), utilizing Snort 3, Open-Source Security (OSSEC), ModSecurity, and Moodle's security
settings. The increasing security threats facing LMS platforms was addressed in the study by leveraging the strengths of
each tool: Snort 3 for network-level detection, OSSEC for host-based monitoring, ModSecurity for web application
protection, and Moodle’s native security features for enhanced control. An experimental approach was adopted,
beginning with a literature review to identify vulnerabilities, followed by system design, tool configuration, and
integration. The model was tested against simulated attacks, with performance measured by detection accuracy. The
results demonstrated the model's effectiveness in identifying and mitigating common security threats within Moodle LMS
such as distributed denial of service, brute force attack, SQL injection and aggressive scan. The study concludes by
recommending the deployment of the IDP model in a live environment for both private/individual owned and public
owned Moodle platforms, for the provision of a robust framework for enhancing security. This work contributes to the
broader field of LMS security through the provision of a comprehensive, multi-layered approach to protecting
educational platforms from cyber threats.