Private LLMs for Higher Education: Secure GenAI for Academic & Administrative Content
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V6I4P103Keywords:
Private Large Language Models, Higher Education, Secure GenAI, Data Privacy, Academic Administration, Artificial Intelligence GovernanceAbstract
Generative Artificial Intelligence (GenAI) based on Large Language Models (LLMs) has rapidly transformed knowledge-intensive domains, including higher education. Universities and academic institutions increasingly rely on LLMs for academic writing support, intelligent tutoring systems, admissions processing, research assistance, and administrative automation. However, the adoption of public, cloud-hosted LLMs introduces critical risks related to data privacy, intellectual property leakage, regulatory non-compliance, and institutional sovereignty. Sensitive academic data such as student records, examination materials, unpublished research, and administrative communications cannot be safely exposed to external GenAI platforms governed by opaque data retention and training policies. In response to these challenges, Private LLMs have emerged as a secure and controllable alternative, enabling institutions to deploy GenAI capabilities within on-premise or institution-controlled cloud infrastructures. Private LLMs preserve data confidentiality while offering customization aligned with institutional pedagogy, governance, and compliance requirements. This paper presents a comprehensive study on the design, deployment, and evaluation of Private LLMs tailored for higher education environments. It examines architectural frameworks, security mechanisms, fine-tuning strategies, and governance models that support academic and administrative use cases. The study further analyzes the trade-offs between performance, scalability, and security when compared to public LLM services. Experimental evaluation demonstrates that well-optimized private LLMs can achieve competitive performance while ensuring compliance with data protection regulations such as FERPA, GDPR, and institutional ethical guidelines. The paper concludes that Private LLMs represent a sustainable and secure pathway for the responsible adoption of GenAI in higher education, fostering innovation without compromising trust, privacy, or academic integrity
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