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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.

The Open Universiteit and the Centre for Education and Learning (CEL) at Leiden-Delft-Erasmus are publishing a new video series. “The digital revolution is having a significant impact on the way we learn and the ways in which educational institutions operate and engage with their students”, they say. Learning in a Digital Society vodcast series gives a platform to leading experts in Technology-Enhanced-Learning (TEL) to discuss this digital transformation. In each episode an expert delves into a single topic and discusses the challenges and opportunities presented by technology and their vision for the near future. Some address questions such as how to teach programming to children, or why technological innovation in education is often slow. Other videos provide a sketch of key research topics in TEL such as Learning Analytics and Open Education.

The first in the series is by Geoff Stead,  Chief Technical Officer at the language learning app Babbel. In a time when Ed-tech adherents are increasingly questioning the effectiveness and efficacy of their work – see for instance Andrey Waters much discussed ‘The 100 Worst Ed-Tech Debacles of the Decade’ – Geoff remains enthusiastic about the future of tech. Embrace the edges, he says, and don’t just be a passive consumer of tech.

Anyway, regardless of the content, I like the format and production.

I am a little bemused by the approach of many of those writing about Artificial Intelligence in education to knowledge. The recently released Open University Innovation Report, Innovating Pedagogy, is typical in that respect.

“Helping students learn how to live effectively in a world increasingly impacted by AI also requires a pedagogy”, they say, “that, rather than focusing on what computers are good at (e.g. knowledge acquisition), puts more emphasis on the skills that make humans uniquely human (e.g. critical thinking, communication, collaboration and creativity) – skills in which computers remain weak.”

I have nothing against critical thinking, collaboration or creativity, although I think these are hard subjects to teach. But I find it curious that knowledge is being downplayed on the grounds that computers are good at it. Books have become very good at knowledge over the years but it doesn’t mean that humans have abandoned it to the books. What is striking though is the failure to distinguish between abstracted and applied knowledge. Computers are very good at producing (and using) information and data. But they are not nearly as good at applying that knowledge in real world interactions. Computers (in the form of robots) will struggle to open a door. Computers may know all about the latest hair styles but I very much doubt that we will be trusting them to cut our hair in the near future. But of course, the skills I am talking about here are vocational skills – not the skills that universities are used to teaching.

As opposed to the emergent Anglo Saxon discourse around “the skills that make humans uniquely human” in Germany the focus on Industry 4.0 is leading to an alternative idea. They are seeing AI and automation as requiring new and higher levels of vocational knowledge and skills in areas like, for example, the preventative maintenance of automated production machinery. This seems to me to be a far more promising area of development. The problem I suspect for education researchers in the UK is that they have to start thinking about education outside the sometimes rarified world of the university.

Equally I do not agree with the reports assertion that most AI applications for education are student-facing and are designed to replace some existing teacher tasks. “If this continues”, they say “while in the short run it might relieve some teacher burdens, it will inevitably lead to teachers becoming side-lined or deprofessionalised. In this possible AI-driven future, teachers will only be in classrooms to facilitate the AI to do the ‘actual’ teaching.”

The reality is that there are an increasing number of AI applications which assist tecahers rather than replace them – and that allow teachers to get on with their real job of teaching and supporting learning, rather than undertaking an onerous workload of admin. There is no evidence of the inevitability of teachers being either sidelined or deprofessionaised. And those experiments from Silicon Valley trying to ‘disrupy’ education by a move to purely online and algorithm driven learning have generally been a big failure.

I guess we are going to be hearing a lot about AI in education in the next year. As regular readers will know, I am working on a European Commission Erasmus Plus project on Artificial Intelligence and Vocational Education and Training. One subject which is constantly appearing is the issue of ethics. Apart from the UK universities requirements for ethical approval of research projects (more about this in a future post), the issue of ethics rarely appears in education as a focus for debate. Yet it is all over the discussion of AI and how we can or should use AI in education.

There is an interesting and (long) blog post – ‘The Invention of “Ethical AI“‘ recently published by Rodrigo Ochigame on the Intercept web site.

Orchigame worked as a graduate student researcher in the former director of the MIT Media Lab, Joichi Ito’s group on AI ethics at the Media Lab. He left in August last year , immediately after Ito published his initial “apology” regarding his ties to Epstein, in which he acknowledged accepting money from the disgraced financier both for the Media Lab and for Ito’s outside venture funds.

The quotes below provide an outline of his argument although for anyone interested in this field the article merits a full read. the

The emergence of this field is a recent phenomenon, as past AI researchers had been largely uninterested in the study of ethics

The discourse of “ethical AI,” championed substantially by Ito, was aligned strategically with a Silicon Valley effort seeking to avoid legally enforceable restrictions of controversial technologies.

This included working on

the U.S. Department of Defense’s “AI Ethics Principles” for warfare, which embraced “permissibly biased” algorithms and which avoided using the word “fairness” because the Pentagon believes “that fights should not be fair.”

corporations have tried to shift the discussion to focus on voluntary “ethical principles,” “responsible practices,” and technical adjustments or “safeguards” framed in terms of “bias” and “fairness” (e.g., requiring or encouraging police to adopt “unbiased” or “fair” facial recognition).

it is helpful to distinguish between three kinds of regulatory possibilities for a given technology: (1) no legal regulation at all, leaving “ethical principles” and “responsible practices” as merely voluntary; (2) moderate legal regulation encouraging or requiring technical adjustments that do not conflict significantly with profits; or (3) restrictive legal regulation curbing or banning deployment of the technology. Unsurprisingly, the tech industry tends to support the first two and oppose the last. The corporate-sponsored discourse of “ethical AI” enables precisely this position.

the corporate lobby’s effort to shape academic research was extremely successful. There is now an enormous amount of work under the rubric of “AI ethics.” To be fair, some of the research is useful and nuanced, especially in the humanities and social sciences. But the majority of well-funded work on “ethical AI” is aligned with the tech lobby’s agenda: to voluntarily or moderately adjust, rather than legally restrict, the deployment of controversial technologies.

I am not opposed to the emphasis being placed on ethics in AI and education and the debate and practice son Learning Analytics show the need to think clearly about how we use technology. But we have to be careful that we firstly do not just end up paying lip service to ethics and secondly that academic research does not become a cover for teh practices of the Ed tech industry. Moreover, I think we need a clearer understanding of just what we mean when we talk about ethics in the educational context. For me the two biggest ethical issues are the failure of provide education for all and the gross inequalities in educational provision based on things like class and gender.

Much of my work at the moment is focused in two different areas – the training and professional development of teachers and trainers for the use of technology for teaching and learning and the use and understanding of labour market data for careers counseling, guidance and advice. However as data increasingly enters the world of education, the two areas are beginning to overlap.

This morning I received an email from the European Network on Regional Labour Market Monitoring. Although the title may seem a little obscure, the network, which has been active over some time, organises serious research at a pan European level. Each year it selects a theme for research, publications and for its annual conference. Over the last year it has focused on informal employment. Next year’s theme is Small and Medium Enterprises (SMEs) which they point out can be viewed as perhaps the most vibrant and innovative area of the European economy. However, when it comes to researching and understanding SMEs it is not so easy

A number of European or national statistics exist to analyse SMEs’ but they generally use the same categories as for large firms and are, in general, constructed from a large firm perspective or in any case not from a framework based on SME characteristics. Many academic papers focusing on SMEs show that they cannot fully be understood using the same categories as with large firms. The general idea is that firstly, SMEs are same as large ones, just smaller. Secondly, the assumption that they will grow up to become Midcaps, then large firms, is incorrect. Torres and Julien (2005) start their article explaining that “Most, if not all, researchers in small business have accepted the idea that small business is specific (the preponderant role of the owner-manager, low level of functional breakdown, intuitive strategy, etc.)”. A 2019 French publication directed by Bentabet and Gadille tackles the issue of SMEs focussing on their specific “social worlds”, their “action models and logics”, while elsewhere the influences of institutional logics and multi-rationalities of SMEs have been considered. The entry of social worlds highlights the great diversity of micro-enterprises and SMEs, which often makes it difficult to analyse them. As a counterpoint, specific knowledge of these companies is required because they are at the heart of the debates on flexibility, labour market dynamics, skilled labour shortage and disruptions in the vocational training system.

SMEs will be the focus for the next Annual Meeting of the Regional Labour Market Monitoring to be held in September 2020 in Sardinia

We have written before about the gender pay gap in the UK. According to the Office for National Statistics the average hourly (gross, excluding overtime) gender pay gap in the UK for all employees fell from 17.8 per cent in 2018 to 17.3 per cent in 2019. However, nee research has revealed cross-national gaps vary from as much as -5 per cent in Wigan to 32 per cent in Slough suggesting that only focusing on a national perspective might be overly simplistic.

The Centre for Cities has found that 7 of the 10 cities with the highest gender pay gap are located either in the South East or East of England. They say that “as cities in these regions tend to perform economically better than cities in the North of England, economic performance seems to influence the gender pay gap in cities. In general, cities with higher average weekly earnings (e.g. Cambridge, London, Reading, Crawley, Slough) tend to have a higher gender pay gap.”

Another factor the Centre for Cities things is driving higher gender pay gaps in the south of England is the bigger difference between men and women holding a managerial position. While 5.2 of men and 3.2 per cent of women in the north east hold such a position, 8.1 per cent of managers in the south east are men while only 4.4 per cent are women (data is not available below regional level).”

Six out of the ten cities with the smallest gender pay gap are located in the North of England: Wigan, Burnley, Warrington, Sunderland, Blackburn and Middlesbrough. These cities have weaker economies and lower rates of employment

The Centre for Cities has looked at the industrial composition of the labour market in Warrington and Wigan, finding that both cities have a higher share of jobs in education, human and health activities and social work than cities with higher gender pay gaps such as Slough and Crawley.

The composition of sectors in and around cities is seen as important and since women are more likely to be employed in the public sector, for instance, as teachers, social workers and nurses, the gender pay gap tends to be lower in cities with a higher proportion of public sector jobs such as in Middlesbrough, Blackburn, Swansea and Glasgow.