Improving skills and competences of VET teachers and trainers in the age of Artificial Intelligence
This is an interesting diagram, from a publication, Artificial intelligence and knowledge management: A partnership between human and AI, by Mohammad Hossein Jarrahi, David Askay, Ali Eshraghi and Preston Smith. I picked it ip from Juan Domingo Farnos's Facebook account.
What I like is that although talking about AI, it is building on older debates around knowledge development and particularly on 'Know why' and on tacit knowledge transferred through social interactions. In the past tacit knowledge was seen as important for sharing and developing new knowledge - in organisations and between organisation. And while AI is great for codifying knowledge, it would seem unlikely that it is going to develop tacit knowledge any time soon. So in terms of human roles and AI roles in knowledge it becomes a question of how tacit knowledge will become codified through working with AI.
HM - need to think a bit more about this.
It is hard to keep up with the avalanche of talks, posts, reports and so on about AI and education, sparked by Open AI's release of Chat GPT and then the many tools which have followed. Talking with teachers in different countries in Europe, I am impressed how many seem to have just quietly got on with it, accepting that AI is there and it is important that their students know how to use it properly and sensibly. Having said that, in Italy Chat GPT remains banned, as it is viewed by the government as being in conflict with the General Data Protection Regulation (GDPR).
The big problem area for institutions is assessment. Joe Wilson's opening speech at the City Of Glasgow College's teaching and learning conference yesterday. Joe Wilson is Head of Digital Skills and his presentation was entitled the 'March of Artificial Intelligence from Tinder to Training in 30 minutes.' The key take aways from his presentation were to:
1. Make you aware of rise of artificial intelligence and implications for education and assessment.
2. Make you aware of a range of tools you can use in your own practice
3. Consider how you should introduce AI to your learners to allow them to use it ethically
4. Reflect on what it means for policy makers.
Talking about assessment (which he approached as part of professional practice) he said
1 .Ideally make assessment a demonstration of competence.
2. Require personal reflection and insights.
3. Require that notes and drafts are submitted with the final work. - know your learner’s writing style
He suggested promoting Portfolios and blogs and eliciting reports on specific activities (How I did/achieved this) as well as creating assessments that require Video or oral assessments and seting tasks that require analysis of charts, images, or videos.
All of which would seem a good idea to me, regardless of Generative AI.
You can see the full presentation on Google Docs
In the work we have been doing over the past three years around the use of Artificial Intelligence in Vocational Education and Training, one of the most frequent requests from teachers and trainers has been for examples of how people are doing this. We are picking up on this under the new Erasmus + Large scale project - AI Pioneers. And my colleague Ludger Deitmer, from the ITB at the University of Bremen, is doing a great job funding examples of teaching with and about AI in the German vocational schools and seeking videos and other materials about what schools are doing. By no means every vocational school is using AI, but there seems to be a growing number developing projects and experiments, especially reflecting on how AI is going to change the nature of work in different occupations.
The video above (in English) is from the Berufsbildende Schulen 2 in Wolfsburg who have developed a project on AI and energy. They say:
Energy saving and green energy is the most important topic for all of us to survive on our wonderful planet earth.
We see many opportunities to reach this goal all together.
We would like to show that energy saving does not cost money – it will payback after a few years.We want to combine artificial with human intelligence to solve this challenge.
I seem to be increasingly subscribing to (and reading) newsletters. For AI, by far the best I have found is The Algorithmic Bridge by Alberto Romero. In the latest edition where he discusses ChatGPT's seeming avoidance of any degree of implausibility, especially in peoples' CVs, he explains his own implausible background as an aerospace engineer who went on to work for an AI startup and then study cognitive neuroscience to end up writing on the internet.
Earlier in the newsletter he quotes the work of Shannon Vallor, a philosopher at the University of Edinburgh whose research is focused on “the philosophy and ethics of emerging science and technologies,” particularly AI.
"I vividly recall reading Vallor’s insights", he says. "They influenced my later perspectives on AI and language models. Here’s, in my opinion, the most illuminating excerpt from her essay “GPT-3 and the Missing Labor of Understanding”:
“Understanding is beyond GPT-3’s reach because understanding cannot occur in an isolated behavior, no matter how clever. Understanding is not an act but a labor. Labor is entirely irrelevant to a computational model that has no history or trajectory; a tool that endlessly simulates meaning anew from a pool of data untethered to its previous efforts. In contrast, understanding is a lifelong social labor. It’s a sustained project that we carry out daily, as we build, repair and strengthen the ever-shifting bonds of sense that anchor us to the others, things, times and places, that constitute a world.”
Alberto continues:
Love this framing. The way it emphasizes the social and cultural dimensions of human understanding. It departs from the typical “AI models can’t understand because they don’t have a world model” or “because they can’t access the meaning behind the form of the words.” Those are true, too, but this one—understanding as labor we do actively and daily—was refreshing.