Document Type
Book
Publication Date
2026
Abstract
Artificial Intelligence (AI) is not a peripheral or speculative technology in the university setting, it is rather a rapidly evolving transformational force that will reshape research, teaching, and administrative operations. Recent systematic reviews conclude that generative AI tools are already being widely used by students and instructors and can positively affect learning outcomes and academic performance when thoughtfully integrated into instruction (Hon, 2025; Qian, 2025).
At the same time, case studies warn that unmanaged or poorly governed AI use can undermine academic integrity, exacerbate inequities, and create new institutional risks (Mangundu, 2025; Şen et al., 2026). AI adoption brings unparalleled opportunities for innovation and efficiency. In research, AI can accelerate discovery throughout a wide range of avenues, including extant literature review and synthesis, advanced analytics of big data, pattern recognition in medical images, and generative modeling (Hon, 2025). In teaching and learning, AI can support personalized feedback at scale, provide low-stakes practice opportunities, and assist non-traditional or remote learners when appropriately scaffolded (Qian, 2025). In administrative operations, AI can enhance decision support systems to improve forecasting, resource allocation, and student success interventions by analyzing large institutional datasets (Mangundu, 2025). However, uncoordinated or uninformed AI use magnifies inherent risks to academic integrity, institutional credibility, equity, and compliance, especially where high stakes decisions are made without adequate transparency or human oversight (Mangundu, 2025; Şen et al., 2026).
It is therefore incumbent on university administrators to elaborate and execute a comprehensive strategy for AI integration that leverages the manifest opportunities while guarding against the equally inevitable risks. Emerging work on agile AI governance in higher education argues that universities must navigate a tension between regulation and innovation. Static, acutely responsive guidelines are insufficient for a rapidly evolving technological landscape (Şen et al., 2026). Governance frameworks that emphasize iterative guidance piloting, stakeholder engagement, and continuous review appear better suited to balancing institutional legitimacy, regulatory compliance, and innovation.
This manual establishes a strategic framework for AI integration, including both implementation guidance and operational guidance, to ensure that AI enhances the university mission while maintaining accountability. The presented framework is intended to be generalizable across universities with Research 1 (R1) designation per the Carnegie Classification of Institutions of Higher Education, with selective specification towards the operational details at the University of South Carolina. In doing so, it aligns with broader guidance from emerging AI governance literature in higher education, including studies of institutional preparedness and governance maturity (Mangundu, 2025).
Rights
© 2026, University of South Carolina