Generative Artificial Intelligence in Higher Education: Opportunities, Challenges, and Future Directions

Abstract

The integration of Generative Artificial Intelligence (GAI) in higher education has garnered significant scholarly attention. This comprehensive review synthesizes current literature to examine the transformative potential, implementation challenges, and future trajectories of textual (e.g., ChatGPT), visual (e.g., DALL-E), and multimodal GAI tools in academic settings. Our analysis reveals that GAI offers different GAI categories substantial opportunities for personalized learning, pedagogical innovation, and creative skill development while simultaneously presenting critical challenges related to academic integrity, data privacy, and algorithmic bias. We analyze these developments through three interconnected dimensions: technological applications, stakeholder perceptions, and contextual implementation. The paper concludes by proposing six key research priorities: assessment integrity and pedagogical strategies, ethical frameworks and policy development, teaching-learning process impacts, stakeholder perceptions research, technological enhancements, and future skills preparation. These findings provide both theoretical foundations and practical guidance for the responsible integration of GAI technologies in higher education institutions.