A Responsible AI Governance Framework for Generative AI in Higher Education: Balancing Innovation, Academic Integrity, Privacy, and Accountability

• Jayprakash Vijay

  Department of Electronics and Communication Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur, India

  Author

Generative Artificial Intelligence (GenAI) has rapidly emerged as a transformative technology in higher education, offering new opportunities for personalized learning, instructional support, programming assistance, research activities, content development, and administrative efficiency. At the same time, its rapid adoption has created significant concerns related to academic integrity, privacy, inaccurate information, algorithmic bias, intellectual property, unequal access, student dependency, and institutional accountability. Existing institutional responses have frequently focused either on restricting GenAI or promoting its adoption without sufficiently developed governance mechanisms. Both approaches are inadequate for addressing the complexity of contemporary AI-enabled education. This paper proposes a Responsible AI Governance Framework for Generative AI in Higher Education (RAIGF-HE) that integrates technological innovation with academic integrity, privacy, transparency, human oversight, accountability, AI literacy, equity, and continuous risk management. The framework is developed through an integrative synthesis of established AI ethics literature, international governance frameworks, higher education research, and recent studies on GenAI adoption and academic integrity. The proposed framework consists of eight interconnected dimensions: strategic AI governance, responsible innovation, academic integrity, privacy and data governance, transparency, human oversight and accountability, AI literacy, and continuous monitoring and improvement. A five-stage implementation lifecycle—Assess, Govern, Implement, Monitor, and Improve—is also proposed. The framework shifts the emphasis from blanket prohibition and exclusive reliance on AI-detection technologies toward risk-based governance, authentic assessment, transparent AI use, and human-centred decision-making. The proposed model provides universities, faculty members, policymakers, and technology administrators with a practical foundation for achieving a balance between the educational benefits of GenAI and the protection of academic standards, privacy, institutional trust, and human agency.

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