STRATEGIC MANAGEMENT OF GENERATIVE AI IN INSTITUTIONS OF HIGHER LEARNING: BALANCING INNOVATION AND ACADEMIC INTEGRITY
Oladele Olubukola Olabode, Ph. D, The Nigerian Baptist Theological Seminary, Ogbomoso.
MSI Journal of AI and Technology | https://zenodo.org/records/19450104 | Page 01 to 21
Abstract
Institutions of higher learning have traditionally served as centers of knowledge creation, intellectual inquiry, and character formation. The advent of generative artificial intelligence (AI) represents a paradigm shift in higher education, as these systems can independently produce essays, research summaries, programming scripts, and problem-solving frameworks, transforming traditional academic work. This rapid technological diffusion has generated institutional tensions, including faculty concerns over plagiarism and academic integrity, student dependence on AI for coursework, and administrative uncertainty in policy formulation and enforcement. Ethical questions regarding authorship, originality, and the boundary between human creativity and machine assistance further complicate the landscape. This paper argues that universities must adopt deliberate strategic frameworks for AI governance, integrating policy design, leadership foresight, risk management, and ethical oversight. By implementing intentional and adaptive governance structures, higher education institutions can harness the innovative potential of generative AI while preserving academic integrity and authentic learning.
Keywords: Strategic Management, Generative AI, Institutions of Higher Learning, Innovation and Academic Integrity.
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For articles published under a Creative Commons CC BY 4.0 license, any part of the article may be reused for any purpose, including commercial use, provided that the original MSIP article is clearly cited.
Cyber Security Threats and Risk Management in Modern Information Technology
Muhammad Faisal Nawaz, School of Computer Science, Jiangsu University, China.
Muhammad Hasnain, University of Agriculture, Pakistan.
MSI Journal of AI and Technology | https://zenodo.org/records/19382148 | Page 01 to 17
Abstract
The pervasive integration of information technology (IT) into the fabric of modern society, from critical national infrastructure to personal consumer devices, has created an expanded and complex digital landscape. This digital transformation, while driving unprecedented innovation and efficiency, has concurrently introduced a sophisticated and evolving array of cyber security threats. This research article provides a comprehensive analysis of the contemporary cyber security threat landscape and evaluates the corresponding frameworks for risk management within modern IT environments. The study begins with an examination of the evolution of threats, moving from simple malware to advanced persistent threats (APTs), ransomware-as-a-service (RaaS), supply chain attacks, and the emerging risks associated with artificial intelligence (AI) and the Internet of Things (IoT). Through a systematic literature review, this paper synthesizes existing academic and industry knowledge on threat vectors, vulnerability management, and the principles of risk assessment. The methodology section outlines a qualitative approach, leveraging case study analysis of major security incidents and a critical review of established risk management frameworks, including the NIST Cybersecurity Framework (CSF) and ISO/IEC 27001. The results and discussion section presents key findings, highlighting a significant gap between the rapid proliferation of threats and the often-siloed, reactive nature of traditional risk management practices. It argues for a paradigm shift towards a proactive, continuous, and integrated risk management strategy that embeds security into the DevOps lifecycle (DevSecOps) and leverages predictive analytics. The article concludes that effective cyber security in the modern era is not merely a technical challenge but a fundamental business risk that requires strategic alignment, continuous adaptation, and a culture of shared responsibility to ensure organizational resilience.
Keywords: Cyber Security, Risk Management, Threat Landscape, Advanced Persistent Threats, Ransomware, Supply Chain Attacks, NIST Cybersecurity Framework, DevSecOps.
All articles published by MSIP are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of any MSIP article, including figures and tables.
For articles published under a Creative Commons CC BY 4.0 license, any part of the article may be reused for any purpose, including commercial use, provided that the original MSIP article is clearly cited.
AI - AUGMENTED LEADERSHIP AND EMOTIONAL INTELLIGENCE MODELLING IN AFRICAN INDUSTRIAL SETTING
Yahaya Segun ALILU, Ph. D, Department of Political Science, Faculty of Social Sciences, Prince Abubakar Audu University, Anyigba, Kogi State – Nigeria.
Timothy Abayomi Atoyebi, Ph. D, Department of Sociology, Faculty of Social Sciences, Prince Abubakar Audu University, Anyigba, Kogi State – Nigeria.
Edime YUNUSA, Department of Sociology, Faculty of Social Sciences, Prince Abubakar Audu University, Anyigba, Kogi State – Nigeria.
MSI Journal of AI and Technology | https://zenodo.org/records/19371541 | Page 01 to 30
Abstract
The accelerating convergence of Artificial Intelligence (AI) and organizational leadership is reshaping productivity across industries, yet African contexts remain insufficiently theorized, particularly regarding the integration of AI with human-centred leadership constructs. This paper examined the interface between AI-augmented leadership and Emotional Intelligence (EI) in driving adaptive performance and sustainable competitiveness in African industries. It advances a context-sensitive integrative framework that explains how the synergy between AI capabilities and EI competencies enhances leadership effectiveness. To achieve this aim, the paper evaluated the extent to which AI-augmented analytics improves leaders’ decision-making processes, examined the interaction between EI and AI systems in shaping organizational performance, and developed a framework for implementing AI-augmented emotionally intelligent leadership within African settings. The paper was anchored on Transformational Leadership Theory and Goleman’s Emotional Intelligence framework, offering a dual perspective that integrates technological augmentation with relational competencies. Methodologically, a systematic analytical review approach was adopted, drawing on secondary data to synthesize existing empirical and conceptual insights. Findings indicated that leaders who effectively integrate AI into their practices while demonstrating high EI exhibit greater adaptability, reduced organizational conflict, and improved employee engagement. These outcomes highlight the complementary relationship between AI-driven analytics and emotionally intelligent leadership. The paper concluded that AI is unlikely to replace human leadership; rather, when strategically integrated, it enhances leaders’ effectiveness and emotional capacity. The paper recommended among others that African organizations should institutionalize leadership development programmes that combine AI literacy with emotional intelligence to support data-driven and human-centred decision-making.
Keywords: Artificial Intelligence, Emotional Intelligence, AI-Augmented Leadership in African Industries, Organizational Performance.
All articles published by MSIP are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of any MSIP article, including figures and tables.
For articles published under a Creative Commons CC BY 4.0 license, any part of the article may be reused for any purpose, including commercial use, provided that the original MSIP article is clearly cited.
