As the debates and discussions around use of AI continue to develop, I reflect that, perhaps too often, the questions we ask as educators about the impacts of AI can be too small.
There seems to me to be a current over-preoccupation with inward-facing considerations of the impact of AI on our own practices and processes: How we can manage the risks of academic misconduct, how we make our assessments a bit more authentic, how we quality assure students’ development of “AI skills”. I don’t deny that these are important and timely questions, but I think they miss the bigger (knottier) purpose-led picture.
As AI continues to infuse our work in a variety of means and ways we seem sometimes too focused on management and adaptation of processes, rather than working strategically and purposefully to define broader outcomes which face off into the professional and graduate futures of our students and the world they will occupy and shape over the next 50 years.
Until we start asking the bigger questions about the more fundamental challenges to educational purposes that AI brings in its wake, we will not be in a position to understand the shifts in educator capabilities and competencies and indeed professional identities that such a paradigm shift will necessarily require.
Recently, with Prof. Nick Jennings, I argued that we can see two “swim lanes” emerging in AI: one focused on process optimisation and efficiency; one on invention and co-creation. Both are useful, but they require very different things from educators.
AI literacy for optimisation
AI tools offer compelling possibilities to support students with personalised learning support, rapid retrieval of relevant information and coaching prompts for personal and career development. I don’t see these tools replacing human academic and student services professionals; instead they offer a degree of personalised insight and augmentation to human-centric services.
Similarly, AI tools can assist with many of the functions of teaching and learning “delivery”, offering ideas for small-group activities, generating reading lists or other learning resources, offering prompts to structure discussion, rapidly processing student feedback, and so on. Again, this is an efficient, step change augmentation to the spectrum of digital tools that can support effective learning and teaching. Educators will adopt these if they find them to be useful, and according to their disciplinary culture, and their personal orientation towards technology in general.
Just as we have adapted to email or MS Excel (other software is available) as baseline administrative tools used in organisations and businesses, over time I see that academic workflows will no doubt evolve in response to collective learning and accepted wider practices about the usefulness and effectiveness of various AI tools when applied to different elements of academic practice. Some tools might genuinely make academics’ lives easier; others may promise much and deliver very little.
From an institutional perspective it makes sense to curate a flow of discussion about the adoption of AI tools for learning, teaching and student support. Doing so allows for the dissemination of useful practice, contributes to collective understanding about AI’s capabilities and limitations and, optimally, ensures that where AI tools are adopted they are applied ethically and in ways that do not compromise academic quality.
AI literacy for reimagining education futures
With the potential benefits of AI for optimisation duly noted, I don’t think that is the conversation that is going to be the most material for education leaders in the next few years. For me, AI does not represent a specific set of digital capabilities that must be mastered so much as it points to a future that is fundamentally uncertain, and subject to tectonic disruption.
That loss of predictability speaks to a very different set of purposes and outcomes for education – less the acquisition of a body of knowledge than the development of high end human competencies exercised and mediated through a developed technological literacy, all underpinned by a disciplinary knowledge base.
Every new technology, from writing to print to the internet to large language models has prompted a reconsideration of the relationship between educational purposes and disciplinary knowledge. Over time, instead of a student “coming to the discipline” as an apprentice and an assumed future practitioner, disciplinary knowledge is increasingly deployed in the service of a broader range of student outcomes – the discipline “comes to the student.” This is also increasingly reflected in portfolio careers in which core knowledge is rehashed, redeployed, recontextualised and directed towards the challenges of the world and of the workplace, none of which are solved by a single discipline. The difference between previous shifts and the paradigm shift being ushered in by AI is the speed, volatility and unpredictability of what it will do. We are in uncharted waters and, if we are honest, we are not really sure where we are headed or how best to help shape those future outcomes and destinations.
Despite these shifts, or perhaps in part because of them, the idea of the professor still defaults to the guardian and steward of disciplinary knowledge. Recognising that the strength of UK HE in particular comes from a tradition of being organised around somewhat compartmentalised deep disciplinary knowledge, this conceptualisation has remained remarkably consistent even as higher education has become more widely available and serving purposes beyond the passing on of knowledge.
In this sense AI can never (and should never) “replace” academics as stewards of disciplinary knowledge, but it should prompt a deep examination of what that reconfiguration of the relationship between knowledge and education purpose looks like for the different disciplines – and the moments when students need to cross disciplinary boundaries in service of their potential futures, rather than the futures we imagined when in their shoes.
The questions and discussion I am interested in curating asks academics about the potential shape of their discipline and its associated professions in 50 years: What does it mean to think, and “do” your discipline with and alongside AI? What does AI do to the professional practices and identities of the professions allied to your disciplines? The answers to such questions are more readily imagined through contemporary cutting edge research agendas than by established approaches to engaging students with existing bodies of knowledge.
It is only in light of our imagination of the possible futures that await our students that we can start asking what kind of educational environments and approaches we need to build to create the conditions for the development of the skills sets, attitudes and competencies they will need.
My hunch is that we will collectively need to “unwire” ourselves from “standard” PG Cert and PG Dip teaching development tracks and be prepared to look outside the classics of higher education pedagogy and literature, including to primary education, and innovative workplace CPD to find the approaches that work best. While we might retain a foundational basket of knowledge and skills required for entry to the academic profession, I think these will resonate more strongly with a broader set of high end human competencies than with the traditional skills associated with teaching development.
It is likely we’ll need to take a more experimental, co-creative approach to the higher education pedagogy, which engages in the outward facing futurology of graduate paths across the next 50 years as a fundamental starting point for considering our own purpose-led practices. In this we might then retain concepts and theories that serve those purposes while discarding those that have outlived their usefulness.
Sam Grogan will be among the speakers at Kortext LIVE education leaders event on 11 February in London, as part of a panel discussing the Wonkhe/Kortext project Educating the AI Generation. Find out more and book your free spot here.