Generative Artificial Intelligence in Higher Education: Benefits, Risks, and Implementation Challenges
Keywords:
generative artificial intelligence, higher education, ChatGPT, academic integrity, digital learning, systematic literature reviewAbstract
The development of generative artificial intelligence (GenAI) has brought significant change to the practices of learning, teaching, assessment, and management in higher education. Technologies such as ChatGPT allow students and lecturers to generate text, obtain explanations, develop ideas, and support a range of academic activities. Yet their use also raises concerns about academic integrity, the reliability of information, privacy, bias, technological dependence, and the changing role of educators. This article synthesizes the literature on the benefits, risks, and implementation challenges of GenAI in higher education and identifies the research and institutional-policy agenda required. The study was developed through a structured review of a provided set of references that includes systematic reviews, empirical studies, analyses of stakeholder perspectives, and studies of university policy. The synthesis identifies five main themes: support for learning and academic productivity, the transformation of teaching and assessment, risks to academic integrity and quality, ethical and governance challenges, and the need to develop competence and institutional policy. The literature shows that GenAI can broaden access to learning support and assist academic work, but these benefits depend on users’ ability to verify outputs, understand the models’ limitations, and integrate the technology critically. Responsible implementation requires transparent policy, adaptive assessment design, AI literacy, data protection, and collaboration among stakeholders. The article offers a conceptual framework for balancing pedagogical benefits, academic risks, and institutional readiness. Because search and selection documentation is not yet available, the results are a thematic synthesis rather than a systematically screened SLR corpus.
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