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Project lead: Kingston University

Generative AI has challenged, even disrupted the global higher education sector. The premise of our project is that developments in Generative AI offer higher education significant opportunities to recalibrate our educational activities to deliver graduates with the skills to address future global challenges. We explore these opportunities below, but first we briefly consider the immediate steps that we have taken in response to the explosion of Generative AI in our universities.

Reaction to Generative AI from our eleven partners has been varied. However, none have an outright ban. Over the last year, individual partners have been working through the ‘nuts and bolts’ implications of Generative AI by agreeing their institutional position, developing their institutional guidance and implementing required regulatory changes. This project has already identified differences in how Generative AI is sanctioned and adopted across our universities. Most notably, some partners have taken a default ‘no’ to the use of AI in assessment, whilst others have taken a default ‘yes’, although in both scenarios, decision making is devolved to the local level. Similar differences are apparent with Generative AI detection software with some currently using detection, whilst others not. All partners, however, have recognised the challenge of hybrid writing where standard tools used by students, and sanctioned by our institutions, have AI embedded within them. 

Shared experiences include the existence of diverging attitudes and behaviours amongst academic educators. In all our institutions, there are pockets of innovative practice where Generative AI is being carefully considered or embraced but there are also many academic educators predicting the decline of academic standards. However, the majority are looking on, unsettled by the speed and complexity of the developments in this technology. Students share a similar range of attitudes. All our institutions are reaching out to our students to gain a clearer understanding on how they are using it, as well as exploring their anxieties about the impact it will have on their lives, including how their teachers and tutors are using AI themselves.

Ensuring that our students know and understand our institutional guidance on Generative AI is a key priority for all partners, but we recognise that students are starting to challenge the efficacy and authenticity of policies and guidance as they increasingly use these tools in their own environments and perceive how they are being used in broader society.

A key consideration challenging all partners is the question of equitable access to Generative AI software with all our institutions exploring or having already licensed one or more Generative AI tools for students and staff. Individuals who can afford monthly subscriptions with one or more of the main Generative AI tool providers have a major advantage over those students who rely on free access, particularly in terms of speed, capacity and the range and power of tools. This is crucial as all partners recognise the enormous capacity of Generative AI to facilitate 'levelling-up’ whether this be through its support for academic writing or the availability of AI tutor tools and capabilities which could make the most difference to those students who feel marginalised from academia, helping them to gain access to the information and support so important for positive academic outcomes.

Whilst Generative AI undoubtedly poses real challenges for institutions, we have also been keen to explore the significant opportunities that Generative AI will bring to higher education, some of which we explore below.

Articulating the development of higher skills in Higher Education 

The resilience of assessment has been a key focus for our universities. Partners have begun to rethink assessment, some creating explicit tools to help staff stress test their assessments. However, it is clear that as Generative AI technologies evolve, initial workarounds will no longer be reliable, and many types of assessments will not be ‘AI proof’ and present a low barrier to misuse. As a result, we argue that the most sustainable approach will be to focus on authentic assessment principles where students are disincentivised to use AI as a tool to ‘do’ their assessment, but are supported to use it effectively and appropriately (where it is integral to the assessment), with a greater emphasis on the process of completing the assessment and the skills set that this requires.

Developing more authentic assessment will require a focus on active pedagogies which encourage the development of skills. Arguably, the paradigm shift towards active learning in Higher Education has only been partially embraced, with traditional lectures still being commonplace in many of our universities. Generative AI boosts the need to enact pedagogies which further strengthen the foci such as higher order skills.  Moving towards skills-focused curricula will require Universities to more effectively articulate where and how we are developing our students’ graduate attributes – which we all claim to do. These variably include digital competencies and literacies, collaboration and team working, creative problem solving, entrepreneurship, having a questioning mindset, adaptability, empathy, resilience, self-awareness and inclusivity. As a response to the challenges of Generative AI, our institutions are reaching out to employers to explore how they are using AI in the workplace and the implications this has for the skills and attributes that they expect from our graduates. For example, a student-led project at the Business school at the University of the West of England is doing just this, a project which will lead to a set of recommendations for programme enhancement that will address any key skills gaps which are identified.

Building digital and AI literacies

The proliferation of Generative AI provides us with a unique opportunity to explore with our students and staff the strengths and limitations of the current generation of technologies and therefore the importance of developing higher-order cognitive skills. The key to effectively using Generative AI is understanding what it is and what it isn’t, what it can and cannot do, and fundamentally understanding that no matter how ‘clever’ these tools seem, they are not intelligent in the human sense. This understanding can help inform what can be effective practice at the ‘jagged edge’, and what can be ultimately damaging to student learning and development if not thoroughly addressed on an institutional scale.

Discussions with students in our own institutions have found that a significant number of students are not aware of how often Generative AI systems produce inaccurate or made-up facts, statistics or citations, produce biased outputs, or have considered the many ethical questions in the creation and use of these tools, mirroring national trends (, 2024). Added to this is the lack of understanding by students (and staff) on how digital media of all types can be generated, and potentially manipulated by AI, underlining more than ever the critical and evaluative skills that are needed to work in the digital world.

Building the AI literacies of our students will undoubtedly require us to more effectively support our staff to use these tools and will necessitate a digital upskilling amongst the academic community. All our institutions have been heavily involved with delivering professional development for our academic and professional staff.

Addressing global challenges

A critical assessment of Generative AI focussed on key global challenges such as the climate crisis and social justice provides real learning opportunities in our higher education learning spaces. Incorporating discussion of the implicit biases of Generative AI into our teaching, learning and assessment also provides excellent opportunities to drive conversations about who generates and legitimates knowledge, bringing debates perhaps more traditionally aligned with the social sciences into all academic disciplines. A focus on Generative AI and the Human Intelligence that drives it is also an opportunity to consider and debate the global ‘price’ paid for knowledge creation and dissemination, and who are the ultimate winners and the losers.

Central to these debates is the environmental impact of running these technologies in general and Generative AI in particular, in terms of energy consumption, carbon emissions and the consumption of water. Whilst it remains very difficult to get accurate data on the environmental impacts of Generative AI (, 2024), there are a growing number of case-studies about how its exponential growth has placed already vulnerable communities in increasingly perilous situations. The explosion of Generative AI provides an opportunity for all higher education educators to address social justice and sustainable futures in their programme syllabi.

A question of quality?

Finally, the explosion of Generative AI has encouraged us to reflect on our academic regulations and broader measures of quality and standards, not least because developments in AI are moving faster than our traditional regulatory and quality cycles.  Generative AI is unlocking challenging questions about the purpose of higher education and more specifically about how we define and measure quality. Traditional measures are arguably no longer fit for purpose, such as technical proficiency in written English.  Currently, the English regulator expects providers to assess spelling, punctuation and grammar to “ensure the quality of students’ education” (). However, if Generative AI can deliver in this space on its own or added to existing tools like Studiosity or Grammarly, then we must ensure that our quality frameworks are measuring the appropriation and development of higher order skills.

How we achieve this will be a key focus for our University leaders as they grapple both with the broader existential question about the value of higher education, alongside more tangible challenges including the articulation of their institution’s definition of educational gain (at least in England for the next Teaching Excellence Framework).

Next steps

As we move forward with our project, we will continue to identify and share how Generative AI is challenging new ways of thinking in our institutions. We will be working with our staff and students to create an opportunities-based framework which provides resources on how academic educators can embed AI in their learning, teaching and assessment and how best to support students to effectively use Generative AI to support their learning.