The aim of this article was to provide evidence pertaining to cloud computing (CC) adoption in education, namely higher education institutions (HEIs) or Universities. A systematic literature review (SLR) of empirical studies exploring the current CC adoption levels in HEIs and the benefits and challenges for using CC in HEIs was performed. A total of 20 papers were included in the SLR. It was discovered that a number of universities have a keen interest in using CC in their institution, and the evidence indicates a high level of successful CC adoption in the HEIs reviewed in the SLR. In conclusion, the SLR identified a clear literature gap in this research area: there exists limited empirical studies focusing on CC utilisation in HEIs.

Cloud computing has become a major talking point in recent times. An innovation such as cloud computing for higher education institutions (HEI) can be a cost effective means to operate their IT systems effectively without having to spend vast amounts of money on developing their IT infrastructure. HEIs also face the burden of several challenges e.g. limited infrastructure resources and IT budget, as well as limited teaching staff, technical experts, and IT skilled personnel. With support from a systematic literature review approach, this article identifies the key determinants of cloud adoption from a technological, organisational, environmental and personal perspectives. A total of 17 cloud adoption studies in the HEI context and their respected models from the period of 2012 to 2017 are reviewed and discussed. The findings suggest a lack of cloud adoption studies in the HEI domain from multiple perspectives, particularly in relation to the wider socio-technical concerns related to cloud adoption and future studies related to this research gap are deliberated.

This chapter provides an overview of research on AI applications in higher education using a systematic review approach. There were 146 articles included for further analysis, based on explicit inclusion and exclusion criteria. The findings show that Computer Science and STEM make up the majority of disciplines involved in AI education literature and that quantitative methods were the most frequently used in empirical studies. Four areas of AI education applications in academic support services and institutional and administrative services were revealed, including profiling and prediction, assessment and evaluation, adaptive systems and personalisation, and intelligent tutoring systems. This chapter reflects on the challenges and risks of AI education, the lack of association between theoretical pedagogical perspectives, and the need for additional exploration of pedagogical, ethical, social, cultural, and economic dimensions of AI education.

Background, Motivation and Objective: Cloud computing (CC) within UK HEIs is an emerging research phenomenon. When adopting new innovations, it is important to assess whether it will add value to the organisation. Contribution/Method: This exploratory empirical research is derived from the technology, organisation and environment model (TOE), which targeted higher education institutions (HEIs). A qualitative study, which explored the potential barriers and enablers of CC adoption by HEIs from a doctoral student perspective, was conducted, where 32 students at a University that had recently adopted the educational cloud situated in Northwest England, were interviewed. Results/Discussion/Conclusion: The results showed that the most significant enablers of the educational cloud were cost efficiency, scalability, flexibility and mobility, and especially collaboration among students. Whereas the most significant barriers of the educational cloud were security and cultural resistance. In conclusion, the adoption rate of CC is increasing gradually in UK HEIs, which is mostly down to the cost efficient and collaborative nature of the educational cloud, but there is still the issue of privacy and trust in the educational cloud which impedes adoption.

The key determinants of cloud computing provide a convincing argument for HEIs and its stakeholders to adopt the innovation. These benefits reflect the essential quality characteristics of the cloud, such as Broad network Access; Measured Service; On-demand Self-Service; Rapid Elasticity; and Resource Pooling. However, there are also risks associated with the cloud, leading to non-adoption, such as Confidence, Privacy, Security, Surety and Trust. Understanding the impact of these factors can support multiple stakeholders, such as students, lecturers, senior managers and admins in their adoptive decision of CC in their respected institutions. Using the Multiview 3 (MV3) methodology, a research model was proposed to explore the key qualities and risks that determine the adoption or non-adoption of CC by UK HEIs from multiple perspectives. An exploratory qualitative study was conducted on 32 University stakeholders across 2 UK Universities. The findings suggest that security, privacy and trust are the key determinants to non-adoption as participants felt that the cloud cannot fully guarantee the safeguarding of sensitive information. Determinants to cloud adoption include improving relationships between students and teachers via collaborative tools, in addition to proposing cloud apps for mobile devices for accessing virtual learning materials and email securely off-campus. In conclusion, University stakeholders are still at a crossroad when it comes to cloud adoption, but future advances of the cloud may help to steer their decision to adopt this innovative technology given its overwhelming potential.