https://hspublishing.org/JRECS <p><em><strong>Journal of Research in Engineering and Computer Sciences (JRECS) </strong></em>ISSN-3049-7590 is a peer-reviewed academic journal published on bi-monthly bases that publishes high-quality research in the fields of engineering and computer sciences. The journal provides a platform for researchers, engineers, and scientists from around the world to share their latest research findings, ideas, and innovations.</p> <p>Engineering and computer sciences are two fields that are constantly evolving and pushing the boundaries of what is possible. They are integral to the development of new technologies and innovations that have transformed the way we live and work. Research in these fields seeks to understand the underlying principles that govern complex systems, as well as to develop new tools and techniques for solving complex problems. From artificial intelligence and machine learning to robotics and biotechnology, engineering and computer science research are at the forefront of many cutting-edge fields. As the demand for new technologies and innovative solutions continues to grow, the importance of research in these fields cannot be overstated.</p> Headstart Publishing - United Kingdom en-US Journal of Research in Engineering and Computer Sciences 3049-7590 https://hspublishing.org/JRECS/article/view/1704 <p>The integration of artificial intelligence (AI) into educational systems has accelerated dramatically, transitioning from experimental pilot programs to mainstream pedagogical practice by 2026. This paper examines the current landscape of AI in education through quantitative analysis of global adoption data, stakeholder perceptions, and performance outcomes. Drawing on surveys from over 3,800 students across 16 countries, institutional reports, and longitudinal studies, we find that 86% of higher education students now utilize AI tools. In contrast, 60% of teachers have integrated AI into their teaching practice. The global AI education market is projected to reach $112.03 billion by 2034, reflecting a compound annual growth rate of approximately 38%. However, significant challenges persist: 65% of educators’ express concern about plagiarism, 71% of K-12 teachers lack formal AI training, and only 7% of schools worldwide have established AI usage guidance. This paper synthesizes current evidence, identifies critical implementation barriers, and proposes a framework for responsible AI integration that preserves human-centric pedagogy while leveraging technological affordances.</p> Ayotunde Alaba Fadele Kole Uhuegho Copyright (c) 2026 Journal of Research in Engineering and Computer Sciences 2026-11-09 2026-11-09 4 06 01 09 10.63002/jrecs.406.1704 https://hspublishing.org/JRECS/article/view/1859 <p>The Aviation Safety Reporting System (ASRS), maintained by the National Aeronautics and Space Administration (NASA), contains a large and continually expanding collection of incident narratives submitted by aviation safety professionals. Manual review of this corpus is increasingly impractical and time-consuming, motivating automated methods that can extract structured safety information from free-text reports. This research presents SafeAeroBERT-Prime, a domain-specific transformer model pre-trained on ASRS and National Transportation Safety Board (NTSB) narratives and fine-tuned with Low-Rank Adaptation (LoRA) for multilabel classification of primary safety factors from narrative scenarios. Cross-validation results indicate that SafeAeroBERT-Prime provides stable and reproducible performance across evaluated data partitions, demonstrating that parameter-efficient fine-tuning can identify meaningful safety-factor patterns even under constrained data and computational resources.&nbsp; Beyond classification, this work establishes a foundation for integrating natural-language processing (NLP) into aviation safety-analysis workflows. A continuously updating machine-learning (ML) pipeline and an interactive dashboard integrated with a chatbot interface are under development to connect model predictions with failure-mode information and component-reliability data. Containerized deployment will support reproducibility and integration into existing engineering environments. For aviation reliability, maintainability, and safety (RMS) engineers, these capabilities can reduce the effort required to identify recurring safety hazards, prioritize relevant reports, and detect emerging safety trends. For the broader aerospace community, SafeAeroBERT-Prime illustrates how domain-adapted, computationally accessible language models may support more proactive, evidence-informed safety risk management (SRM). The system is intended to augment, not replace, established safety processes and expert judgment; accordingly, operational use requires ongoing validation, performance monitoring, and human oversight.</p> Pranav Tikkawar Angelina Legkodimov Yehia F. Khalil Jian Zou Copyright (c) 2026 Journal of Research in Engineering and Computer Sciences 2026-11-09 2026-11-09 4 06 10 31 10.63002/jrecs.406.1859