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International Journal of Advances in Engineering and Computer Science

Vol. 2, Issue 2 — 2026

2 Articles  ·  Published: Aug 1, 2026  ·  Academians Publishers

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Article 1 of 2

Theoretical Exploration of Artificial Intelligence Integration in Educational Enhancement: A Contextual Framework for North-East Nigeria

Abdussalam Abba Tukur, Amuniddeen Abubakar, Abba Muhammad Rabiu, Ibilade Abdurrasaq Ibiyemi, Muhammad Shamsu Usman
Pages: 18-28 DOI: @ Published: Aug 1, 2026

Abstract

Artificial Intelligence (AI) is increasingly transforming educational systems across the globe, offering unprecedented opportunities for personalized learning, administrative efficiency, and educational access. However, the integration of AI in under-resourced and developing regions, particularly conflict-affected areas such as North-East Nigeria remains critically limited and theoretically underexplored. This study investigates the theoretical dimensions of AI adoption in education, focusing on its potential roles, prospects, and associated challenges in the unique context of North-East Nigeria. Employing a theoretical research design grounded in systematic literature synthesis, the study critically examines existing AI-in-education frameworks, conceptual models, and global case studies relevant to low-resource environments. The research synthesizes these insights to develop a context-sensitive conceptual framework, coined as the AI-Enhanced Educational Transformation (AI-EET) Model that can guide the effective integration of AI into educational systems within fragile and resource-constrained settings. The framework identifies five interdependent pillars: Infrastructure Readiness, Human Capacity Development, Policy and Regulatory Framework, Community Engagement and Trust, and Pedagogical Integration. The study further analyzes the unique challenges of North-East Nigeria, including infrastructural deficits, the legacy of conflict, teacher shortages, and digital divides, while highlighting the transformative potential of AI-driven educational tools in improving access, personalization, and educational outcomes. The study contributes to scholarship by proposing a model tailored to the socio-economic and security realities of North-East Nigeria and offers actionable recommendations for policymakers, educators, and development partners. The findings have implications for similar contexts across the Global South where AI integration in education remains nascent yet urgently needed.

Keywords: Artificial Intelligence; Educational Technology; AI Integration; North-East Nigeria; Developing Regions; Educational Transformation; Conceptual Framework; Digital Divide

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Article 2 of 2

Comparative Analysis of Machine Learning Models for Rock Mass Rating (RMR) Prediction in Road Tunnel Construction Using Synthetic Geotechnical Data

Prakash Bhatta
Pages: 29-37 DOI: @ Published: Aug 1, 2026

Abstract

Accurate rock mass classification is a fundamental requirement in road tunnel design and construction. The Rock Mass Rating (RMR) system, developed by Bieniawski (1989), remains one of the most widely adopted frameworks for evaluating rock mass quality; however, its application relies heavily on subjective engineering judgment, introducing variability and inconsistency in assessment outcomes. This study presents a comparative investigation of five supervised machine learning (ML) models — Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Multilayer Perceptron Artificial Neural Network (MLP-ANN), and Gradient Boosting (GB) — for the prediction of RMR classes in road tunnel construction scenarios. A synthetic geotechnical dataset of 2,000 samples was generated using the standard Bieniawski (1989) RMR formulation, incorporating six input parameters: Uniaxial Compressive Strength (UCS), Rock Quality Designation (RQD), joint spacing, joint condition, groundwater condition, and discontinuity orientation adjustment. Rock physics modeling principles, consistent with RokDoc software workflows, informed the parametric distributions used for data generation. Model performance was evaluated using test accuracy, five-fold cross-validation, confusion matrices, and classification reports. The RF model achieved the highest test accuracy of 98.7%, followed by XGBoost at 98.2%, GB at 97.9%, MLP-ANN at 96.4%, and SVM at 95.1%. SHapley Additive exPlanations (SHAP) analysis identified joint condition and RQD as the most influential predictors of RMR class. The findings demonstrate the viability of ML-based approaches for objective, rapid, and consistent rock mass classification, with significant implications for tunnel face assessment and geotechnical risk management in road tunnel projects.

Keywords: Rock Mass Rating; RMR prediction; machine learning; road tunnel; random forest; XGBoost; SHAP explainability; geotechnical engineering; synthetic data; rock mass classification

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