AI in Education – the question of hype and reality

Max Gruber / Better Images of AI / Banana / Plant / Flask / CC-BY 4.0

I have spent a lot of time over the past two weeks working on the first year evaluation report for the AI pioneers Web site. Evaluation seems to come in and out of fashion on European Commission funded projects. And now its in an upswing, partly due to the move to funding projects based on the products and results, rather than number of working days claimed.

For the AI Pioneers project, I have adopted a Participant oriented approach to the evaluation. This puts the needs of project participants as its starting point. Participants are not seen as simply the direct target group of of the project but will includes other stakeholders and potential beneficiaries. Participant-orientated evaluation looks for patterns in the data as the evaluation progresses and data is gathered in a variety of ways, using a range of techniques and culled from many different sources. Understandings grow from observation and bottom up investigation rather than rational deductive processes. The evaluator’s key role is to represent multiple realities and values rather than singular perspectives.

Hopefully we will be able to publish the Evaluation report in the early new year. But here are a few take aways, mainly gleaned from interview I undertook with each of the project partners.

The partners have a high level of commitment to the project. However the work they are able to undertake, depends to a large extent on their role in their organisations and the organisations role in the wider are of education. Pretty much all of the project partners, and I certainly concur with this sentiment, feel overwhelmed by the sheer volume of reports and discourse around AI in education and the speed of development especially around generative AI, makes it difficult to stay up to date. All the partners are using AI to some extent. Having said that there is a tendency to thing of Generative AI as being AI as a whole, and to forget about the many uses of AI which are not based on Large Language Models.

Despite the hype (as John Naughton in his Memex 1.1 newsletter pointed out this week AI is at the peak of the Gartner hype cycle, see illustration) finding actual examples of the use of AI in education and training and Adult Education is not easy.

A survey undertaken by the AI pioneers project found few vcoati0nal education and train9ing organisations in Europe had a policy on AI. There is considerable differences between different sectors - Marketing, Graphic design, computing, robotics and healthcare appear to be ahead but in many subjects and many institutions there is little actual movement in incorporating AI, either as a subject or for teaching and learning. And indeed, where there are initial projects taking place, this is often driven by enthusiastic individuals, with or without the knowledge of their managers.

This finding chimes with reports from other perspect6ives. Donald H Taylor and Egle Vinauskaite have produced a report looking at how AI is being used in workplace Learning and Development today, and concludes that it is in its infancy. "Of course, some extraordinary things are being done with AI within L&D," they say. "But our research suggests that where AI is currently being used by L&D, it is largely for the routine tasks of content creation and increased efficiency."

If there is one message L&D practitioners should take away from this report, it is that there is no need to panic – you are not falling far behind your peers, for the simple reason that very few are making major strides with AI. There is every need, however, to act, if only in a small way, to familiarize yourself with what AI has to offer.

Donald H Taylor and Egle Vinauskaite. Focus on AI in L&D, https://donaldhtaylor.co.uk/research_base/focus-on-ai-in-ld/

Indeed, for all the talk of the digital transformation in education and training, it my be that education, and certainly higher education, is remarkably resistant to the much vaunted hype of disruption and that even though AI will have a major impact it may be slower than predicted.

Unesco AI Competency Framework

Unesco have released the latest version of their draft competency framework for teachers in the use of AI. Fengchun Miao says "We expect that all teachers should be able develop and apply the competencies under the first level of progression (acquisition) across all 5 aspects through pre-service teacher preparation programmes or in-service trainings." Unesco are continuing their consultation around the Framework. #AI #AIED

Searching for the AI bridge builders

We need to democratise access to AI but the language we use to talk about it is a barrier.

There is a lot of fear around AI advances and this is perpetuated when only the ‘big tech’ and the academics have access to the tools, the theory and the conversations. For me this is a major theme in AI ethics right now. We can’t have conversations about ‘the black box around generative AI’ as if everyone understands the concept. Similarly ‘language models’, ‘the dynamics of knowledge production’, or ‘neural networks’. I suspect that I have already lost a large chunk of my friends and family and we are only on the first paragraph.

We talk a lot about bias in the data; there’s a great advert doing the rounds on social media at the moment where an AI was prompted to draw Barbie dolls from around the world. Some of the results are quite a shocking reflection on our own stereotypes and cultural tropes with German Barbie depicted in a Nazi uniform and African Barbie carrying a gun. AI may have created the images but we have supplied the data. It is an accessible depiction of bias, we need more accessible depictions of AI concepts.

As academics, researchers and professionals, what we don't see so easily is the bias innate in our own use of language around AI. It is the same in all industries, in all academic circles across all disciplines, we are so used to discussing with each other that we become stuck in our bubble of understanding, of acronyms and concepts. What we need is a giant pin, and we need more AI Pioneers to bridge the gap between theory and practice. More people willing to stop and ask questions. More translators of AI speak. More people who are comfortable in both worlds, who do not feel alienated by the academic circles and equally do not alienate practitioners, who, lets face it, are the real experts here. It is the practitioners who will be finding innovative ways to teach with and about the tools, and as with all previous ed-tech advances, it is the practitioners who will work out how to ‘hack’ the systems to fit their contexts. It is also the trainers who will be on the ground working with learners with poor digital literacy, trying to engage and enthuse them to not be automated out of a job.

I’d like to think that my work and that the projects Pontydysgu are involved with fit the gap nicely, providing introductory materials and creative ways to use AI tools, but I was reminded by a group of trainers I ran a workshop with recently of the need to slow down, take things back to basics. 

When I first started out in edtech I was the trainer-in-training, in one session billed as a ‘hands-on practical introduction to e-learning’ the instructor showed us how learners’ work could be exhibited on a website - it was new and exciting, the dawn of web2.0, everyone in the room was eager to learn how. But we were then left with the bamboozling task of “now build a website.”

In my workshop, I heard the words “now use that to build a bot” escape my mouth and realised that the student had truly become the master. 

We need to remember to put the scaffolding into place so as not to lose people over the edge, and that includes explaining ourselves clearly or at least signposting people who can. To quote Einstein, “If you can't explain it simply, you don't understand it well enough” If you are one of those people, a gap-bridger, a mediator, an educator and also an AI enthusiast I warmly invite you to join the AI Pioneers network. Use the contact form on our website to get in touch, join in the conversation on Mastodon (like Twitter but without the megalomania) or find us via LinkedIn.

 

Designing new social AI systems for education

UNESCO-UNEVOC/Ludi Yana under CC BY-NC-SA 4.0 IGO

Very much like the conclusion to Mike Sharples paper, 'Towards social generative AI for education: theory, practices and ethics':

Designing new social AI systems for education requires more than fine tuning existing language models for educational purposes. It requires building GAI to follow fundamental human rights, respect the expertise of teachers and care for the diversity and development of students. This work should be a partnership of experts in neural and symbolic AI working alongside experts in pedagogy and the science of learning, to design models founded on best principles of collaborative and conversational learning, engaging with teachers and education practitioners to test, critique and deploy them. The result could be a new online space for educational dialogue and exploration that merges human empathy and
experience with networked machine learning.