Generative AI: A Disruptive Game Changer in Higher Education

Olateju Jumoke Ajanaku and Earlel Thiyagaratnam

Chapter 9 in AI-Driven Revolution:Transforming the Business Landscape, 2025, pp 169-196 from World Scientific Publishing Co. Pte. Ltd.

Abstract: Generative Artificial Intelligence (GenAI) is revolutionizing higher education by transforming instructional practices and enhancing learning outcomes. This study explores the integration of GenAI tools such as ChatGPT, Microsoft Copilot, and Meta AI, employing the Sociomateriality Theory to understand their implications for pedagogy, student engagement, and institutional practices. Through semi-structured interviews with these AI tools, the research investigates their capabilities, limitations, and potential to address challenges like bias, privacy, and academic integrity. Thematic analysis of the findings reveals six key themes: the role of GenAI in education, its impact on teaching and learning, student engagement, ethical considerations, institutional readiness, and future directions. Results indicate that GenAI tools support personalized learning, streamline administrative tasks, and foster critical thinking. However, they also present ethical challenges, including data privacy concerns, algorithmic biases, and risks to academic integrity. The study emphasizes the irreplaceable value of human-led, experiential learning, highlighting the need for a balanced integration of AI and traditional pedagogical methods. Recommendations include enhancing faculty training, developing ethical guidelines, and ensuring equitable access to AI technologies. This research contributes to the ongoing discourse on the transformative potential of GenAI in education, providing actionable insights for educators, policymakers, and technologists. By addressing the duality of GenAI as both an enabler and disruptor, the study advocates for responsible adoption to maximize benefits while safeguarding educational values.

Keywords: Artificial Intelligence; Data Analytics; AI; Digital Landscape; Organizational Strategies; AI Technologies; Machine Learning; Natural Language Processing; Robotics; Digital Transformation; Business Models; Efficiency; Value Propositions; Advanced Analytics; Predictive Modelling; Customer Experiences; AI-driven; Ethical AI; Data Privacy; Algorithmic Bias; Regulation Compliance; Responsible AI; Sustainable AI; Practical Applications; Business Innovation; Emerging Technologies; Industry 4.0; High Tech; Ethics Regulation; Business Leadership; Pattern Recognition; Information Technology; Entrepreneurs; Management (search for similar items in EconPapers)
JEL-codes: L1 L2 L21 L26 M1 (search for similar items in EconPapers)
Date: 2025
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