Advice and Guidance

Legacy post: A Maturity Model for AI in Tertiary Education

Legacy post: AI is a fast-moving technology and so we have now published an updated version of this model.  The post is now available just for those that need to reference older articles.


One of the core aims for our national centre for AI in tertiary education is to help institutions along their journey to mature adoption of AI services.  To do this, we are underpinning our work with a maturity model, which we first introduced in our AI in Tertiary Education Report.

This model brings together ideas from a number of other more general AI maturity models, including ones from Microsoft and Gartner, and focuses specifically on the tertiary education sector.

One of the practical values of a maturity model is that it allows institutions to understand where they are, and what they sort of activities and processes might be needed in order to progress along the maturity curve.

Currently, most universities and colleges are at the approaching and understanding stage, with some starting to move to move to the experimenting and exploring stage, with a much smaller group at the operational stage. The last group would have some institution wide AI processes or systems.

As part of our work, we have already published several resources to help with the various stages, and we are planning to create a lot more over the next year, culminating in a complete guide to the route to mature adoption of AI.

In the next section, we’ll look at each stage, and consider the kind of activities that we’d expect to see in that stage.

Approaching and Understanding

At the approaching and understanding stage, institutions will be interested in AI, and want to understand what problems it can solve, how it works, and what issues should be considered. This will include investigating how AI is already being used in our sector, and how it is transforming other sectors, as well as gaining a basic understanding of how AI works.

Understanding the views of various stakeholders is important at this stage.  What are student attitudes to AI? What are they using? What do they understand about AI? And what concerns do they have.  What are the top concerns of staff in various roles?

At this stage, people will also want to understand the broad issues around AI. What are the key ethical issues? And what wider societal impacts should we consider? For example, environmental impacts of AI (for example energy usage and carbon impact) and human impact (for example, who exactly is encoding the data and under what conditions).

Experimenting and Exploring

At the experimenting and exploring stage, institutions will start actively experimenting with AI. The key here is to identify a particular problem you wish to solve and explore whether an AI based solution has the potential to help.  We often see people looking at this in reverse – i.e. we have all this data, surely AI can do something useful with it.  This approach is rarely successfully, particularly in the early stages of exploring AI.

An understanding of successful implementations of AI systems is helpful at this stage, even if on a small scale, along with an understanding of some of the main practical issues. For example, how does AI use data? Do we have the data we need for our application? What data concepts are important to understand and consider in our exploration? For example, how does bias occur, and how can we mitigate this. How do we explain how the AI system is reaching its decision in a way that we and users can understand?

We must also make sure AI driven resources are accessible to all, so at this stage, what specific accessibility issues arise due to our use of AI, and what opportunities does it provide?


At the operational stage, institutions will have moved beyond experimentation, to having AI systems live for one or more processes.

There are two related core areas that we think institutions will need to understand to make AI systems fully operational:

  • What’s different about an AI project?
  • How do we procure AI systems?

In the previous stages, we expect an institution to have gained an understanding of how AI can help, and some of the core issues in AI projects. At the heart of this is the question of what is different about AI projects and service.  Fundamentally, this is down to the difference between a service built on a standard algorithm, where a human can fully explain exactly how it reaches its conclusion, and one based on machine learning, where, in most cases, a human cannot explain exactly how a conclusion is made by the model. The model will almost certainly be based on some form of training data, and we need to understand what issues arise from this data. For example, is it representative, and does it contain historical biases.  So, we need new approaches to understanding and validating the performance of the software, ensuring we minimise/remove risk of bias, and have ways of both assessing these issues at procurement and monitoring them over the lifetime of the system.


During the early part of the operational stage, AI projects are likely to be seen as a special case, but as AI becomes embedded, it will just become part of the consideration for any digital transformation project, underpinned by an organisation’s strategy.

Mature data governance will play a part, as will institutional wide policies and processes. As an example, policies for monitoring AI solutions for performance and integrity will be in place at an institutional level, in much the same way as we see, say, institution wide security policies today.


How will AI transform education? We can speculate that this might be around personalised experiences for students, leading to better, more timely or maybe quicker outcomes, perhaps with more convenience and more attention to wellbeing.  For staff it might mean AI removing all mundane and repetitive tasks, allowing more focus on important tasks directly helping their students.

This is likely to happen when AI starts to not just improve or lower the cost of existing processes, but we start to develop new approaches and processes that take advantage of the things AI can do well.  To start moving to this stage, an understanding of how AI has transformed other sectors will be important, a journey started at the approaching stage but building on the much deeper institutional understand of AI gained in the previous stages.

What next?

Over the next few months, we aim to produce a range of guides, articles, webinars and other resources to support universities and colleges with the different stages, which we plan to bring together in an overall guide next year.


Find out more by visiting our Artificial Intelligence page to view publications and resources, join us for events and discover what AI has to offer through our range of interactive online demos.

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