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More as promised in my last post from the interviews we are doing on AI and Education.

One implication of AI and automation is changes in curriculum content and pedagogy. I talked with Chris Percy about this.

Chris pointed out that for school leavers qualification at GCSE level maths and English are a requirement even for vocational students and he thinks this is unlikely to change. However he thinks that programmes in these subjects will move to  –to adaptive personal learning environments.

Furthermore he says the flipped classroom model will change the role of teachers. “It has proved impossible to improve the staff student ration – general courses have 20 – 40 students or 7 to 10 on niche courses. This needs 3 / 4 way differentiation. Teachers are more conductors than coaches.” However Chris added a caveat – research suggests the the flipped classroom re model has limits. “It only really works for those who want to learn. It is possible that adults know what they want to learn but lack the motivation for self learning. Peers and teachers are important for extrinsic motivation. Disengaged teenagers are frequently not sufficiently motivated. Self taught learning even wth a mentor will only go so far. ” Cris also says that learning has a social element and questions whether avatars can really replace the social role played by teachers. As he points out, generalized AI is still out of reach.  “Chatbots cannot replace teachers at the front of a classroom. Students will have no respect for a chatbot. Teachers are skilled in developing engagement. Chatbots are good for students with a base level of motivation.”

The issue of motivation has come up in most of the interviews I have undertaken as part of the AI and Vocational Education and Learning project. I will talk more about this in a short podcast this weekend talking about my experiences as a language learner using the popular and heavily gamified DuoLingo application.

Regular readers will know I am working on a project on AI and Vocational Education and Training (VET). We are looking both at the impact of AI and automation on work and occupations and the use of AI for teaching and learning. Later in the year we will be organizing a MOOC around this: at the moment we are undertaking interviews with teachers, trainers , managers and developers (among others) in Italy, Greece, Lithuania, Germany and the UK.

The interviews are loosely structured around five questions:

• What influence do you think AI and automation is going to have on occupations that you or your institution provide training for?
• Do you think AI is going to effect approaches to teaching and learning? If so could you tell us how?
• Have you or your institution any projects based around AI. If so could you tell us about them?
• How can curricula be updated quickly enough to respond to the introduction of AI?
• Do you think AI and automation will result in less jobs in the future or will it generate new jobs? If so what do you think the content of those jobs will be?

Of course it depends on the work role and interests of the interviewee as to which questions are most discussed. And rather than an interview, with the people I have talked with it tends to be more of a discussion.

while the outcomes of this work will be published in a report later this spring, I will publish here some of the issues which have been come up.

Last week I talked with Chris Percy, who describes himself as a Business strategy consultant and economist.

Chris sees AI and technology as driving an increasing pace of change in how work is done. He says the model for vocational education is to attend college to get skills and enter a trade for ten or twenty years – albeit with refreshers and licenses to update knowledge. This, he says, has been the model for the last 50 years but it may not hold if knowledge is so fast changing. He is not an AI evangelist and thinks changes feed through more slowly. With this change new models for vocational education and training are needed, although what that model might be is open. It could be e to spend one year learning in every seven years or one day a week for three months every year.

The main issue for VET is not how to apply AI but how we structure jobs, Lifelong Learning and pedagogy.

One problem, at least in the UK. has been a reduction in the provision of Life Long Learning has gone down in the UK. In this he sees a disconnect between policy and the needs of the economy.  But it may also be that if change is slower than in the discourse it just has just not impacted yet. Tasks within a job are changing rather than jobs as a whole. We need to update knowledge  for practices we do not yet have. A third possible explanation is that although there are benefits from new technologies and work processes the benefits from learning are not important enough for providing new skills.

New ways of learning are needed – a responsive learning based on AI could help here – but there is not enough demand to overcome inertia. The underpinning technologies are there but have not yet translated into schools to benefit retraining.

Relatively few jobs will disappear in their entirety – but a lot of logistics, front of store jobs, restaurants etc. will be transformed. It could be there will be a lower tier of services based on AI and automation and a higher tier with human provision. Regulators can inhibit the pace of change – which is uneven in different countries and cities e.g. Self driving cars.

In most of the rest of the economy people will change as tasks change. For example the use of digital search in the legal industry  has been done by students, interns and paralegals because someone has to do it – now with AI supporting due diligence students can progress faster to more interesting parts of the work. Due diligence is now AI enabled.

Chris thinks that although AI and automation will impact on jobs, global economic developments will still be a bigger influence on the future of work.

More from the interviews later this week. In the meantime if you would like to contribute to the research – or just would like to contribute your ideas – please et in touch.

Formative assessment should provide a key role in all education and particularly in vocational education and training. Formative assessment can give vital feedback to learners and guidance in the next steps of their learning journey. It can also help teachers in knowing what is effective and what is not, where the gaps are and help in planning learning interventions.

Yet all too often it does not. Assessment is all too often seen at best as something to overcome and at worst as a stress inducing nightmare. With new regulations in England requiring students in further education to pass tests in English and Mathmatics, students are condemned to endless retaking the same exams regardless of achievement in vocational subjects.

For all these reasons a new report published by Jisc today is very welcome.

Jisc say:

Existing and emerging technologies are starting to play a role in changing assessment and could help address these issues, both today and looking further ahead into the future, to make assessment smarter, faster, fairer and more effective.

The report sets five targets for the next five years to progress assessment towards being more authentic, accessible, appropriately automated, continuous and secure.

• AuthenticAssessments designed to prepare students for what they do next, using technology they will use in their careers

• AccessibleAssessments designed with an accessibility-first principle

• Appropriately automatedA balance found of automated and human marking to deliver maximum benefit to students

• ContinuousAssessment data used to explore opportunities for continuous assessment to improve the learning experience

• SecureAuthoring detection and biometric authentication adopted for identification and remote proctoring

The report: ‘The future of assessment: five principles, five targets for 2025’ can be downloaded from the Jisc website.

I have written before about the issues of interpreting sense making from Labour Market Data and the difference between Labour Market Information and labour Market Intelligence.

This is exposed dramatically in the article in Social Europe by German Bender entitled ‘The myth of job polarisation may fuel populism’. As German explains “It has become conventional wisdom since the turn of the century that labour markets are rapidly becoming polarised in many western countries. The share of medium-skilled jobs is said to be shrinking, while low- and high-skilled jobs are growing in proportion.” But as German points out: “In a research report published last May by the Stockholm-based think tank Arena Idé, Michael Tåhlin, professor of sociology at the Swedish Institute for Social Research, found no job polarisation—rather, a continuous upgrading of the labour market.”

German goes on to explain:

The main reason is that the research, as is to be expected from studies rooted in economics, has used wages as a proxy for skills: low-paying jobs are taken to be low-skilled jobs and so on. But there are direct ways of measuring skill demands in jobs, and Arena Idé’s report is based on a measure commonly used in sociology—educational requirements as classified by the International Labour Organization’s ISCO (International Standard Classification of Occupations) scheme. Using this methodology to analyse the change in skill composition yields strikingly different results for the middle of the skill distribution.

The study found that while jobs relatively low skill demands but relatively high wages—such as factory and warehouse workers, postal staff and truck drivers—have diminished, others with the same or slightly higher skill demands but lower wages—nursing assistants, personal-care workers, cooks and kindergarten teachers—have increased.

The reason is that the former jobs are male dominated whilst the jobs which have grown have a majority of female workers. Research in most countries has shown that women (and jobs in which women are the majority) are lower paid than jobs for men, regardless of skills levels.

“Put simply”, says German: “wages are a problematic way to measure skills, since they clearly reflect the discrimination toward women prevalent in most, if not all, labour markets across the world.”

A further review of two British studies from 2012 and 2013, showed a change in the composition, but not the volume, of intermediate-level jobs. “Perhaps the most important conclusion”, German says “was that ‘the evidence shows that intermediate-level jobs will remain, though they are changing in nature’.”

The implications of this interpretation of the data are profound. If lower and medium skilled jobs are declining there is little incentive to invest in vocational education and training for those occupations. Furthermore, young people may be put off entering such careers and similarly careers advisers may further mislead school leavers.

There has been a trend in many European countries towards higher level apprenticieships, rather than providing training with the skills need to enter such medium skilled jobs. But even a focus on skills, rather than wages, may also be misleading. It is interesting that jobs such as social care and teaching appear more resistant to automation and job replacement from technologies such as Artificial Intelligence. But those who are arguing that we should be teaching so called soft skills such as team building, empathy and communication are talking about the very skills increasingly demanded in the female dominated low and middle skilled occupations. It may be that we need not ony to relook at how we move away from wages as a proxy for skills, but also look at how we measure skills.

German references research by Daniel Oesch and Giorgio Piccitto, who studied occupational change in Germany, Spain, Sweden and the UK from 1992 to 2015, characterising good and bad jobs according to four alternative indicators: earnings, education, prestige and job satisfaction.

They concluded that occupations with high job quality showed by far the strongest job growth, whereas occupations with low job quality showed weak growth regardless of indicator used.