Archive for the ‘#AIinEd’ Category

Digitalisation, Artificial Intelligence and Vocational Occupations and Skills

July 27th, 2020 by Graham Attwell
web, network, programming

geralt (CC0), Pixabay

The Taccle AI project on Artificial Intelligence and Vocational Education and Training, has published a preprint  version of a paper which has been submitted of publication to the VET network of the European Research Association.

The paper, entitled  Digitalisation, Artificial Intelligence and Vocational Occupations and Skills: What are the needs for training Teachers and Trainers, seeks to explore the impact AI and automation have on vocational occupations and skills and to examine what that means for teachers and trainers in VET. It looks at how AI can be used to shape learning and teaching processes, through for example, digital assistants which support teachers. It also focuses on the transformative power of AI that promises profound changes in employment and work tasks. The paper is based on research being undertaken through the EU Erasmus+ Taccle AI project. It presents the results of an extensive literature review and of interviews with VET managers, teachers and AI experts in five countries. It asks whether machines will complement or replace humans in the workplace before going to look at developments in using AI for teaching and learning in VET. Finally, it proposes extensions to the EU DigiCompEdu Framework for training teachers and trainers in using technology.

The paper can be downloaded here.

July 22nd, 2020 by Graham Attwell

As part  of the Taccle AI project, around the impact of AI on vocational education and training in Europe, we have undertaken interviews with managers, teachers, trainers and developers in five European countries (the report of the interviews, and of an accompanying literature review, will be published next week).  One of the interviews I made was with Aftab Hussein, the ILT manager at Bolton College in the north west  of Engand. Aftab describes himself on Twitter (@Aftab_Hussein) as “exploring the use of campus digital assistants and the computer assisted assessment of open-ended question.”

Ada, Bolton College’s campus digital assistant has been supporting student enquiries about college services and their studies since April 2017.In September 2020, the college is launching a new crowdsourcing project which seeks to teach Ada about subject topics. They are seeking the support of teachers to teach Ada about their subjects.

According to Aftab “Teachers will be able to set up questions that students typically ask about subject topics and they will have the opportunity to compose answers against each of these questions. No coding experience is required to set up questions and answers.Students of all ages will have access to a website where they will be able to select a subject chatbot and ask it questions. Ada will respond with answers that incorporate the use of text, images, links to resources and embedded videos.

The service will be free to use by teachers and students.”

If you are interested in supporting the project complete the online Google form.

AI and Young People

July 17th, 2020 by Graham Attwell

Last December, the Youth Department of the Council of Europe organised a seminar on Artificial Intelligence and its Impact on Young People. The aim of the seminar was to explore the issues, role and possible contributions of the youth sector in an effort to ensure that AI is responsibly used in democratic societies and that young people have a say about matters that concern their present and future. The seminar looked, among other things, into three dimensions of AI”

  • AI and democratic youth participation (including young people’s trust/interest in democracy)
  • AI and young people’s access to rights (including social rights)
  • AI and youth policy and youth work

According to the report of the seminar, the programme enabled the participants to put together their experience and knowledge in proposing answers to the following questions:

  • What are the impacts of on young people and how can young people benefit from it?
  • How can the youth sector make use of the capacities of to enhance the potential of youth work and youth policy provisions for the benefit of young people?
  • How to inform and “educate” young people about the potential benefits and risks of AI, notably in relation to young people’s human rights and democratic participation and the need to involve all young people in the process?
  • How does AI influence young people’s access to rights?
  • What should the youth sector of the Council of Europe, through the use of its various instruments and partners, do about AI in the future?

Not only is there a written report of the seminar but also an excellent illustrated report. Sadly it is not in a format that  can be embedded, but  it is well worth going to the Council of Europe’s web page on AI and scrolling to the bottom to see the report.

European Union, AI and data strategy

July 9th, 2020 by Graham Attwell
lens, colorful, background

geralt (CC0), Pixabay

is the rapporteur for the industry committe for European Parliament’s own-initiative  on data strategy and  a standing rapporteur on the World Trade Organization e-commerce negotiations in the European Parliament’s international trade committee.

Writing in Social Europe she says:

Building a human-centric data economy and human-centric artificial intelligence starts from the user. First, we need trust. We need to demystify the data economy and AI: people tend to avoid, resist or even fear developments they do not fully understand.

Education plays a crucial role in shaping this understanding and in making digitalisation inclusive. Although better services—such as services used remotely—make life easier also outside cities, the benefits of digitalisation have so far mostly accrued to an educated fragment of citizens in urban metropoles and one of the biggest obstacles to the digital shift is lack of awareness of new possibilities and skills.

Kampula-Natri draws attention to the Finnish-developed, free online course, ‘Elements of AI’. This started as a course for students in the University of Helsinki but has extended  its reach to over 1 per cent of Finnish citizens.

Kampula-Natri points out that in the Nordic countries, the majority of participants on the ‘Elements of AI’ course are female and in the rest of the world the proportion exceeds 40 per cent—more than three times as high as the average ratio of women working in the technology sector. She says that after the course had been running in Finland for a while, the number of women applying to study computer science in the University of Helsinki increased by 80 per cent.

Learning about surveillance

July 3rd, 2020 by Graham Attwell
eye, surveillance, privacy

GDJ (CC0), Pixabay

I found this on the Social Media Collective website. The Social Media Collective is a network of social science and humanistic researchers, part of the Microsoft Research labs in New England and New York.

Yesterday the Wayne County Prosecutor publicly apologized to the first American known to be wrongfully arrested by a facial recognition algorithm: a black man arrested earlier this year by the Detroit Police. The statement cited the unreliability of software, especially as applied to people of color.

With this context in mind, some university and high school instructors teaching about technology may be interested in engaging with the Black Lives Matter protests by teaching about computing, race, and surveillance.

I’m delighted that thanks to the generosity of Tawana Petty and others, ESC can share a module on this topic developed for an online course. You are free to make use of it in your own teaching, or you might just find the materials interesting (or shocking).

The lesson consists of a case study of Detroit’s Project Green Light, a new city-wide police surveillance system that involves automated facial recognition, real-time police monitoring, very-high-resolution imagery, cameras indoors on private property, a paid priority response system, a public/private partnership, and other distinctive features. The system has allegedly been deployed to target peaceful Black Lives Matter protesters.

Here is the lesson:

Race, Policing, and Detroit’s Project Green Light

AI cloud computing to support formative assessment in vocational education and training

June 30th, 2020 by Graham Attwell

geralt (CC0), Pixabay

I have written before abut the the great work being done around AI by Bolton College in the UK and particularly their ADA Chatbot.

One of my main interests about the use of AI in vocational education and training is the potential for freeing  up teachers for more personalized learning support for both those students who are struggling and also for the advanced students. At the moment too many teachers are forced by workloads to teaxh to the middle.

My second big hope is around assessment. Vocational students need, I think, regular feedback and that can come form formative assessment. However, at present teacher do not have the time to prepare and provide feedback on regular formative assessments. But with AI this become possible.

Bolton College previously received VocTech seed funding to prove the concept of using Artificial Intelligence (AI) to analyse short and long form answers and to demonstrate that real-time feedback can be offered to vocational learners as they respond to online open-ended formative assessment tasks.

Their FirstPass tool provided an initial introduction to AI cloud computing technologies which are able to support vocational students and their teachers with open-ended formative assessment tasks.

Now according to Ufi who provide Voctech fundiing, a new project :will provide further development of FirstPass to ensure that it is effective and robust in use and can demonstrably improve the teaching, learning and assessment experience of vocational learners. It will provide teachers with a richer medium for assessing students due to its ability to pose open-ended questions that can be automatically analysed and assessed by a computer, giving students real-time feedback and the opportunity to qualify and clarify their responses.”

Ethics in AI and Education

June 10th, 2020 by Graham Attwell
industry, industry 4, web

geralt (CC0), Pixabay

The news that IBM is pulling out of the facial recognition market and is calling for “a national dialogue” on the technology’s use in law enforcement has highlighted the ethical concerns around AI powered technology. But the issue is not just confined to policing: it is also a growing concern in education. This post is based on a section in a forthcoming publication on the use of Artificial Intelligence in Vocational Education and Training, produced by the Taccle AI Erasmus Plus project.

Much concern has been expressed over the dangers and ethics of Artificial Intelligence both in general and specifically in education.

The European Commission (2020) has raised the following general issues (Naughton, 2020):

  • human agency and oversight
  • privacy and governance,
  • diversity,
  • non-discrimination and fairness,
  • societal wellbeing,
  • accountability,
  • transparency,
  • trustworthiness

However, John Naughton (2020), a technology journalist from the UK Open University, says “the discourse is invariably three parts generalities, two parts virtue-signalling.” He points to the work of David Spiegelhalter, an eminent Cambridge statistician and former president of the Royal Statistical Society who in January 2020 published an article in the Harvard Data Science Review on the question “Should we trust algorithms?” saying that it is trustworthiness rather than trust we should be focusing on. He suggests a set of seven questions one should ask about any algorithm.

  1. Is it any good when tried in new parts of the real world?
  2. Would something simpler, and more transparent and robust, be just as good?
  3. Could I explain how it works (in general) to anyone who is interested?
  4. Could I explain to an individual how it reached its conclusion in their particular case?
  5. Does it know when it is on shaky ground, and can it acknowledge uncertainty?
  6. Do people use it appropriately, with the right level of scepticism?
  7. Does it actually help in practice?

Many of the concerns around the use of AI in education have already been aired in research around Learning Analytics. These include issues of bias, transparency and data ownership. They also include problematic questions around whether or not it is ethical that students should be told whether they are falling behind or indeed ahead in their work and surveillance of students.

The EU working group on AI in Education has identified the following issues:

  • AI can easily scale up and automate bad pedagogical practices
  • AI may generate stereotyped models of students profiles and behaviours and automatic grading
  • Need for big data on student learning (privacy, security and ownership of data are crucial)
  • Skills for AI and implications of AI for systems requirements
  • Need for policy makers to understand the basics of ethical AI.

Furthermore, it has been noted that AI for education is a spillover from other areas and not purpose built for education. Experts tend to be concentrated in the private sector and may not be sufficiently aware of the requirements in the education sector.

A further and even more troubling concern is the increasing influence and lobbying of large, often multinational, technology companies who are attempting to ‘disrupt’ public education systems. Audrey Waters (2019), who is publishing a book on the history of “teaching machines”, says her concern “is not that “artificial intelligence” will in fact surpass what humans can think or do; not that it will enhance what humans can know; but rather that humans — intellectually, emotionally, occupationally — will be reduced to machines.” “Perhaps nothing,” she says, “has become quite as naturalized in education technology circles as stories about the inevitability of technology, about technology as salvation. She quotes the historian Robert Gordon who asserts that new technologies are incremental changes rather than whole-scale alterations to society we saw a century ago. Many new digital technologies, Gordon argues, are consumer technologies, and these will not — despite all the stories we hear – necessarily restructure our world.

There has been considerable debate and unease around the AI based “Smart Classroom Behaviour Management System” in use in schools in China since 2017. The system uses technology to monitor students’ facial expressions, scanning learners every 30 seconds and determining if they are happy, confused, angry, surprised, fearful or disgusted. It provides real time feedback to teachers about what emotions learners are experiencing. Facial monitoring systems are also being used in the USA. Some commentators have likened these systems to digital surveillance.

A publication entitled “Systematic review of research on artificial intelligence applications in higher education- where are the educators?” (Olaf Zawacki-Richter, Victoria I. Marín, Melissa Bond & Franziska Gouverneur (2019) which reviewed 146 out of 2656 identified publications concluded that there was a lack of critical reflection on risks and challenges. Furthermore, there was a weak connection to pedagogical theories and a need for an exploration of ethical and educational approaches. Martin Weller (2020) says educational technologists are increasingly questioning the impacts of technology on learner and scholarly practice, as well as the long-term implications for education in general. Neil Selwyn (2014) says “the notion of a contemporary educational landscape infused with digital data raises the need for detailed inquiry and critique.”

Martin Weller (2020) is concerned at “the invasive uses of technologies, many of which are co-opted into education, which highlights the importance of developing an understanding of how data is used.”

Audrey Watters (2018) has compiled a list of the nefarious social and political uses or connections of educational technology, either technology designed for education specifically or co-opted into educational purposes. She draws particular attention to the use of AI to de-professionalise teachers. And Mike Caulfield (2016) in acknowledging the positive impact of the web and related technologies argues that “to do justice to the possibilities means we must take the downsides of these environments seriously and address them.”

References

Caulfield, M. (2016). Announcing the digital polarization initiative, an open pedagogy project [Blog post]. Hapgood. Retrieved from https://hapgood.us/2016/12/07/announcing-the-digital-polarization-initiative-an-open-pedagogy-joint/

European Commission (2020). White Paper on Artificial Intelligence – A European approach to excellence and trust. Luxembourg: Publications Office of the European Union.

Gordon, R. J. (2016). The Rise and Fall of American Growth – The U.S. Standard of Living Since the Civil War. Princeton University Press.

Naughton, J. (2020). The real test of an AI machine is when it can admit to not knowing something. Guardian. Retrieved from  https://www.theguardian.com/commentisfree/2020/feb/22/test-of-ai-is-when-machine-can-admit-to-not-knowing-something.

Spiegelhalter, D. (2020). Should We Trust Algorithms? Harvard Data Science Review. Retrieved from https://hdsr.mitpress.mit.edu/pub/56lnenzj, 27.02.2020.

Watters, A. (2019). Ed-Tech Agitprop. Retrieved from http://hackeducation.com/2019/11/28/ed-tech-agitprop,  27.02.2020

Weller, M (2020). 25 years of Ed Tech. Athabasca University: AU Press.

Pathways to Future Jobs

June 1st, 2020 by Graham Attwell

katielwhite91 (CC0), Pixabay

Even before the COVIP 19 crisis and the consequent looming economic recession labour market researchers and employment experts were concerned at the prospects for the future of work due to automation and Artificial Intelligence.

The jury is still out concerning the overall effect of automation and AI on employment numbers. Some commentators have warned of drastic cuts in jobs, more optimistic projections have speculated that although individual occupations may suffer, the end effect may even be an increase in employment as new occupations and tasks emerge.

There is however general agreement on two things. The first is that there will be disruption to may occupations, in some cases leasing to a drastic reduction in the numbers employed and that secondly the tasks involved in different occupations will change.

In such a situation it is necessary to provide pathways for people from jobs at risk due to automation and AI to new and hopefully secure employment. In the UK NESTA are running the CareerTech Challenge programme, aimed at using technology to support the English Government’s National Retraining Scheme. In Canada, the Brookfield Institute has produced a research report ‘Lost and Found, Pathways from Disruption to Employment‘, proposing a framework for identifying and realizing opportunities in areas of growing employment, which, they say “could help guide the design of policies and programs aimed at supporting mid-career transitions.”

The framework is based on using Labour Market Information. But, as the authors point out, “For people experiencing job loss, the exact pathways from shrinking jobs to growing opportunities are not always readily apparent, even with access to labour market information (LMI).”

The methodology is based on the identification of origin occupations and destination occupations. Origin occupations are jobs which are already showing signs of employment. Decline regardless of the source of th disruption. Destination jobs are future orientated jobs into which individuals form an origin occupation can be reasonably expected to transition. They are growing, competitive and relatively resilient to shocks.

Both origin and destination occupations are identified by an analysis of employment data.

They are matched by analysing the underlying skills, abilities, knowledge, and work activities they require. This is based on data from the O*Net program. Basically, the researchers were looking for a high 80 or 90 per cent match. They also were looking for destination occupations which would include an increase in pay – or at least no decrease.

But even then, some qualitative analysis is needed. For instance, even with a strong skills match, a destination occupation might require certification which would require a lengthy or expensive training programme. Thus, it is not enough to rely on the numbers alone. Yet od such pathways can be identified then it could be possible to provide bespoke training programmes to support people in moving between occupations.

The report emphasises that skills are not the only issue and discusses other factors that affect a worker’s journey, thereby, they say “grounding the model in practical realities. We demonstrate that exploring job pathways must go beyond skills requirements to reflect the realities of how people make career transitions.”

These could include personal confidence or willingness or ability to move for a new job. They also include the willingness of employers to look beyond formal certificates as the basis for taking on new staff.

The report emphasises the importance of local labour market information. That automation and AI are impacting very differently in different cities and regions is also shown in research from both Nesta and the Centre for Cities in the UK. Put quite simply in some cities there are many jobs likely to be hard hit by automation and AI, in other cities far less. Of course, such analysis is going to be complicated by COVID 19. Cities, such as Derby in the UK, have a high percentage of jobs in the aerospace industry and these previously seemed relatively secure: this is now not so.

In this respect there is a problem with freely available Labour Market Information. The Brookfield Institute researchers were forced to base their work on the Canadian 2006 and 2016 censuses which as they admit was not ideal. Tn the UK data on occupations and employment from the Office of National Statistics is not available at a city level and it is very difficult to match up qualifications to employment. If similar work is to be undertaken in the UK, there will be a need for more disaggregated local Labour Market Information, some of it which may already be being collected through city governments and Local Economic Partnerships.

Creatively working with AI

May 14th, 2020 by Graham Attwell

A major theme in the research literature about Artificial Intelligence (AI) and the future of work is the potential of people working alongside or with AIs. However, it is quite hard to visualize what that might mean, outside the sphere of maintenance technicians in automated factories.

This weeks edition of the Raconteur online magazine provides six examples of people working with AI drawn from very different occupations and contexts:

 

 

 

  • Furniture designer
  • Journalist
  • Filmmaker
  • Musician
  • Fragrance developer

The article says: “Artificial intelligence is shaking up the world of work, automating out routine tasks and freeing workers to concentrate on the more creative elements of their job. But it can often be surprisingly good at mimicking human creativity, and with varying levels of human involvement is now making inroads into new areas of work.”

 

COVID-19, AI and automation

May 13th, 2020 by Graham Attwell

jarmoluk (CC0), Pixabay

It is worth thinking about how the COVID-19 pandemic will effect the future development and implementation of AI and automation. Of course such speculation is problematic – there are many factors coming into play – not least the length of the crisis and the impact of the resulting economic downturn on economies and on business.

A paper by and published by the World Economic Forum suggests “COVID-19 could spur automation and reverse globalization – to some extent.

Providing as an example current supply shortages of critical medical equipment, for example Personal Protection Equipment in the UK, they say: “The current COVID-19 pandemic has fully exposed the vulnerabilities of global value chains (GVCs) which are characterised by high interdependencies between global lead firms and suppliers located across several continents.”

They go on to say that: “Long before the COVID-19 pandemic, in an effort to mitigate supply chain risks, increase flexibility, and improve product standards, global lead firms have relied on Industry 4.0 technologies, such as robots, 3D printing, and smart factories, and occasionally reshored parts of their production.”

and   think the current crisis further spur automation and reshoring in Global Value Chains reducing reliance on “low-skill, low-cost labour in manufacturing” and production moving to a more regional basis, closer to final consumer markets. However they think it   unlikely that entire supply chains will be automated in the short term due to a shortage of skilled workers who are able to operate the machines and the cost of automated production for products with low value-to-weight ratios.

Furthermore growing demand for mid-range consumer goods in emerging markets and the availability of cheap labour in these markets could actually slow down the trend towards automation and reshoring.

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    The gap between poor students and their more affluent peers attending university has widened to its largest point for 12 years, according to data published by the Department for Education (DfE).

    Better-off pupils are significantly more likely to go to university than their more disadvantaged peers. And the gap between the two groups – 18.8 percentage points – is the widest it’s been since 2006/07.

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    As reported by WONKHE, a survey of 1,200 final year students conducted by Prospects in the UK found that 29 per cent have lost their jobs, and 26 per cent have lost internships, while 28 per cent have had their graduate job offer deferred or rescinded. 47 per cent of finalists are considering postgraduate study, and 29 per cent are considering making a career change. Not surprisingly, the majority feel negative about their future careers, with 83 per cent reporting a loss of motivation and 82 per cent saying they feel disconnected from employers


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