Bringing AI to the education environment will require IT leaders within the school environment to pave the way with the right kind of infrastructure. After all, any AI system is only as good as the fundamental technology that supports it. This all starts with data.
It’s an old mentality in IT, but one that still holds true: get your data in order and everything else will follow. AI models simply can’t operate effectively without the right kind of quality data, both in the training stage and when models perform inference in the field.
If you’re looking to implement AI models in a school environment, these will need to have been trained on secure data and fine-tuned to limit any unwanted outputs. But on a more fundamental level, schools looking to use AI in any capacity will need to make sure that their digital record-keeping is as good as it can be to get the most out of their AI services.
In practical terms, this means investing in sufficient data infrastructure.
Building a Fit-for-AI Infrastructure: What to Know
Establish a rigorous data governance model that takes into account the current and short-to-medium-term demands on your data. Depending on the level of digital transformation your institution has already undergone, this may also involve heavy digitization efforts, all of which will help smooth AI integration.
Data should be processed, labeled for ease of use, and stored in a scalable environment to meet future data expansion. In the short term, this will be ideal for any school looking to use AI to derive insights from their data using data analytics. If the need arises for a school to train an AI model based on its own data, for example, to be used in internal communications, properly structured and annotated data will help data scientists to give the model the most relevant information.
Private sector businesses are largely turning to the cloud to meet these needs and this is something that the public sector would also be well-advised to pursue. One of the chief benefits of investing in cloud infrastructure is that it’s inherently scalable, so can expand or contract to match the changing needs of any organization.
Proper data management is a high priority given the kind of data schools will be collecting and storing. At the same time as any educational administrator considers the ethical impacts and limits of using AI in their school, they will also need to question the extent to which their current data storage solution can keep student and staff data secure.
If there’s any question of exposing sensitive information to a third party for AI training purposes, leaders will have to add extra protections to their tech stack. This could mean investing in privacy-enhancing technologies (PETs), which allow organizations to analyze data without moving or exposing it and also to generate synthetic data on which a Large Language Model (LLM) can be trained. This would theoretically allow a school to train an LLM on example answers in a test, for example, without exposing students’ real answers.
Be ready to add new hardware specifically designed to support AI. AI hardware is in hot demand right now – it’s hard to see graphics processing units (GPUs) and neural processing units (NPUs) dropping much in price while AI interest remains high. Luckily, even the largest schools are unlikely to need to invest directly in AI hardware and can instead bring their data to specialized hardware via the cloud.
Managed service providers (MSPs) can help here too, with ready-made frameworks for AI hosted in the cloud and backed up by hardware accelerators built by the likes of AI specialist Nvidia. MSPs can provide bespoke model training or inference needs depending on the outcome a school is seeking and replace up-front CapEx – a headache for any school leader looking at their budget – with OpEx linked to the actual usage of AI on a day-to-day basis.
Be aware that future-proofing AI infrastructure for any school is anticipating future demand. Additionally, while current innovations in the AI space might make it seem like schools will be forced to keep up with constant changes and leaps forward with AI technology, this is unlikely. The latest models are already reaching a balance between power and parameter size, which could offset initial concerns that organizations such as schools will be kept from harnessing AI due to high running costs.
Ultimately, if you’ve worked out a plan for exactly how you want to adopt AI in your school or have an outline for what needs to be set up to allow for future adoption, you’re actually already well on your way.
Once the initial AI infrastructure is in place, scalability and efficiency improvements will allow you to make incremental improvements without the associated costs of lifting and shifting your systems more regularly.