TY - GEN
T1 - Empowering HEIs through LLMs and cloud computing: strategies for seamless integration and sustainable transformation
AU - Idris, Mohamed Diab
AU - Feng, Xiaohua
AU - Dyo, Vladimir
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2026.
PY - 2025/7/23
Y1 - 2025/7/23
N2 - Large Language Models (LLMs) have demonstrated significant potential to revolutionize higher education, prompting a need for strategic guidance on leveraging their benefits while addressing associated challenges [1]. This paper reaches into the critical role of cloud computing in enabling the smooth integration and sustainable transformation of Higher Education Institutions (HEIs) through LLMs. By examining the mutually beneficial relationship between LLMs and cloud technologies, this paper highlights how the cloud empowers HEIs to utilize the full potential of LLMs, overcoming challenges related to scalability, accessibility, and cost-effectiveness. The paper presents a comprehensive framework for the strategic integration of LLMs and cloud computing within HEIs, addressing key considerations such as data privacy, security, interoperability, and ethical governance. Through a systematic review of case studies and best practices, the paper offers actionable insights and recommendations for HEIs to navigate the complexities of LLM deployment in the cloud era. The findings emphasize the importance of a holistic, collaborative approach that engages diverse stakeholders, prioritizes data management, and aligns with the core values of higher education. By incorporating the merging of LLMs and cloud computing, HEIs can unlock new limits in personalized learning, research innovation, and societal impact, ultimately redefining the landscape of higher education in the Artificial Intelligence (AI)-driven era.
AB - Large Language Models (LLMs) have demonstrated significant potential to revolutionize higher education, prompting a need for strategic guidance on leveraging their benefits while addressing associated challenges [1]. This paper reaches into the critical role of cloud computing in enabling the smooth integration and sustainable transformation of Higher Education Institutions (HEIs) through LLMs. By examining the mutually beneficial relationship between LLMs and cloud technologies, this paper highlights how the cloud empowers HEIs to utilize the full potential of LLMs, overcoming challenges related to scalability, accessibility, and cost-effectiveness. The paper presents a comprehensive framework for the strategic integration of LLMs and cloud computing within HEIs, addressing key considerations such as data privacy, security, interoperability, and ethical governance. Through a systematic review of case studies and best practices, the paper offers actionable insights and recommendations for HEIs to navigate the complexities of LLM deployment in the cloud era. The findings emphasize the importance of a holistic, collaborative approach that engages diverse stakeholders, prioritizes data management, and aligns with the core values of higher education. By incorporating the merging of LLMs and cloud computing, HEIs can unlock new limits in personalized learning, research innovation, and societal impact, ultimately redefining the landscape of higher education in the Artificial Intelligence (AI)-driven era.
KW - HEIs
KW - Integration
KW - LLMs
KW - Large language models
KW - cloud
KW - higher education institutions
UR - https://www.scopus.com/pages/publications/105012249418
U2 - 10.1007/978-3-031-92517-7_7
DO - 10.1007/978-3-031-92517-7_7
M3 - Conference contribution
SN - 9783031925160
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 57
EP - 73
BT - Cloud Computing - 12th EAI International Conference, CloudComp 2024, Proceedings
A2 - Feng, Xiaohua
A2 - Siarry, Patrick
A2 - Han, Liangxiu
A2 - Yang, Longzhi
PB - Springer
T2 - EAI CloudComp 2024 - 13th EAI International Conference on Cloud Computing
Y2 - 9 September 2024 through 9 September 2024
ER -