However, a large engagement exercise would be needed to explain how and why the technology is being implemented. Without clear explanation, there would be potential for misconceptions around privacy to arise, as well as potential fears from IT staff that AI tools will take away their jobs.
Up until now, universities have been looked upon more as hubs for research and teaching on AI and the development of AI tools for use in industry rather than organisations which could benefit from implementing such technology.
The UK's AI 'sector deal', for example, sets out how business, academia and government might work in partnership to drive improvements in UK productivity through support for AI. The deal envisages a central role for universities in collaborating with companies in AI research and development, hosting industry-funded Masters courses in AI, and driving implementation of the deal, with representatives from the sector among those making up a new AI Council.
However, AI offers potential solutions for universities themselves. Examples include 'chatbots' that could offer instant answers to basic student queries, freeing up academics and other staff to do more valuable work, and AI tools that can detect plagiarism.
Perhaps even greater potential benefits are available from using AI to manage the growing strain on communications networks.
Big data analysis is increasingly common in research work, and involves significant computer processing power to analyse huge volumes of information. In addition, universities are collecting, generating and processing increasing amounts of data as part of their core activities of research and teaching and also as part of their operational activities. This is particularly the case as more and more processes become digitised.
These activities rely on their being sufficient bandwith to support data processing, but with university communication networks also relied upon by students for their own studies and social activities and those students accessing the network through an increasing number of devices, universities face a challenge in making sure their networks operate without disruption.
AI tools could be used to monitor use of networks and predict when capacity crunches or network performance issues will arise. This can enable universities to proactively address potential disruption by deploying technical network management measures to fix issues fast or even prevent them from occurring. This can also enable universities to gain greater insight into why issues arise and help them to plan their network resilience strategy accordingly..
Like with the introduction of any new technology, universities would need to open an engagement exercise with their staff and students to address potential misconceptions that might arise about why it is deploying AI.
IT staff may fear, for example, that the use of AI for network management could impact their job security – the reality may instead be that it could enable them to work on projects around the implementation of new technologies across the university rather than focus on reacting to more routine network problems when they arise.
The use of AI may prompt privacy concerns among users of the network, fearful perhaps that AI tools are being deployed to track their activities more and more including the number of devices they are individually using and for what purpose they are using the network. It will be important for universities to dispel any myths and set out clearly the benefits from using AI tools for smart network management. Universities would also need to consider any data protection implications of using such AI technologies to ensure they are complying with data protection laws.
Joanne McIntosh is a specialist in technology contracts in the universities sector at Pinsent Masons, the law firm behind Out-Law.com.