CareerChat Bot

chatbot, bot, assistant

mohamed_hassan (CC0), Pixabay

Pontydysgu is very happy to be part of a consortium, led by DMH Associates, selected as a finalist for the CareerTech Challenge Prize!

The project is called CareerChat and the ‘pitch’ video above expalisn the ideas behind the project. CareerChat is a chatbot providing a personalised, guided career journey experience for working adults aged 24 to 65 in low skilled jobs in three major cities: Bristol, Derby and Newcastle. It offers informed, friendly and flexible high-quality, local contextual and national labour market information including specific course/training opportunities, and job vacancies to support adults within ‘at risk’ sectors and occupations

CareerChat incorporates advanced AI technologies, database applications and Natural Language Processing and can be accessed on computers, mobile phones and devices. It allows users to reflect, explore, find out and identify pathways and access to new training and work opportunities.

Nesta is delivering the CareerTech Challenge in partnership with the Department for Education as part of their National Retraining Scheme

  • Nesta research suggests that more than six million people in the UK are currently employed in occupations that are likely to radically change or entirely disappear by 2030 due to automation, population aging, urbanisation and the rise of the green economy.
  • In the nearer-term, the coronavirus crisis has intensified the importance of this problem. Recent warnings suggest that a prolonged lockdown could result in 6.5 million people losing their jobs. [1] Of these workers, nearly 80% do not have a university degree. [2]
  • The solutions being funded through the CareerTech Challenge are designed to support people who will be hit the hardest by an insecure job market over the coming years. This includes those without a degree, and working in sectors such as retail, manufacturing, construction and transport.

You can find out more information about the programme here: and email Graham Attwell directly if you would like to know more about the CareerChat project

Is this the right way to use machine learning in education?

An article ‘Predicting Employment through Machine Learning‘ by Linsey S. Hugo on the National Association of Colleges and Employers web site,confirms some of my worries about the use of machine learning in education.

The article presents a scenario which it is said “illustrates the role that machine learning, a form of predictive analytics, can play in supporting student career outcomes.” It is based on a recent study at Ohio University (OHIO) which  leveraged machine learning to forecast successful job offers before graduation with 87 percent accuracy. “The study used data from first-destination surveys and registrar reports for undergraduate business school graduates from the 2016-2017 and 2017-2018 academic years. The study included data from 846 students for which outcomes were known; these data were then used in predicting outcomes for 212 students.”

A key step in the project was “identifying employability signals” based on the idea that “it is well-recognized that employers desire particular skills from undergraduate students, such as a strong work ethic, critical thinking, adept communication, and teamwork.” These signals were adapted as proxies for the “well recognised”skills.

The data were used to develop numerous machine learning models, from commonly recognized methodologies, such as logistic regression, to advanced, non-linear models, such as a support-vector machine. Following the development of the models, new student data points were added to determine if the model could predict those students’ employment status at graduation. It correctly predicted that 107 students would be employed at graduation and 78 students would not be employed at graduation—185 correct predictions out of 212 student records, an 87 percent accuracy rate.

Additionally, this research assessed sensitivity, identifying which input variables were most predictive. In this study, internships were the most predictive variable, followed by specific majors and then co-curricular activities.

As in many learning analytics applications the data could then be used as a basis for intervention to support students employability on gradation. If they has not already undertaken a summer internship then they could be supported in this and so on.

Now on the one hand this is an impressive development of learning analytics to support over worked careers advisers and to improve the chances of graduates finding a job. Also the detailed testing of different machine learning and AI approaches is both exemplary and unusually well documented.

However I still find myself uneasy with the project. Firstly it reduces the purpose of degree level education to employment. Secondly it accepts that employers call the shots through proxies based on unquestioned and unchallenged “well recognised skills” demanded by employers. It may be “well recognised” that employers are biased against certain social groups or have a preference for upper class students. Should this be incorporated in the algorithm. Thirdly it places responsibility for employability on the individual students, rather than looking more closely at societal factors in employment. It is also noted that participation in unpaid interneships is also an increasing factor in employment in the UK: fairly obviously the financial ability to undertake such unpaid work is the preserve of the more wealthy. And suppose that all students are assisted in achieving the “predictive input variable”. Does that mean they would all achieve employment on graduation? Graduate unemployment is not only predicated on individual student achievement (whatever variables are taken into account) but also on the availability of graduate jobs. In teh UK  many graduates are employed in what are classified as non graduate jobs (the classification system is something I will return to in another blog). But is this because they fail to develop their employability signals or simply because there simply are not enough jobs?

Having said all this, I remain optimistic about the role of learning analytics and AI in education and in careers guidance. But there are many issues to be discussed and pitfalls to overcome.


Jobs of the Future

There is a lot of speculation at the moment as to the jobs of the future. On the one hand, it is said that we are educating young people for jobs which do not yet exist; on the other hand there are dire predictions that up to of existing 55 per cent of jobs may disappear to automation in the next five years.

If it is hard as a researcher who works with labour market data to make sense of all this, imagine what it is like for young people trying to plan a career (and if doing a degree in the UK, running up major debt).

However, there is beginning to appear some more nuanced research on the future of jobs. Michael Chui, James Manyika, and Mehdi Miremadi have just published the initial report on a research project looking at how automation will affect future employment. The report, entitled ‘Where machines could replace humans—and where they can’t (yet)’, is based on detailed analysis of 2,000-plus work activities for more than 800 occupations. Using data from the US Bureau of Labor Statistics and O*Net, they have quantified both the amount of time spent on these activities across the economy of the United States and the technical feasibility of automating each of them.

Their overall finding is that while automation will eliminate very few occupations entirely in the next decade, it will affect portions of almost all jobs to a greater or lesser degree, depending on the type of work they entail.
Each whole occupation is made up of multiple types of activities, each with varying degrees of technical feasibility. In practice, they explain, automation will depend on more than just technical feasibility. Five factors are involved: technical feasibility, costs to automate, the relative scarcity, skills and costs of workers who might otherwise do the activity, benefits (e.g. superior performance) of automation beyond labour costs substitution and regulatory and social acceptance considerations.
The likelihood and ease of automation depends on the types of activities organised on a continuum of less susceptible to automation to more susceptible to automation: managing others, applying expertise,  stakeholder interactions, unpredictable physical work, data collection, processing data, predictable physical work. Thus occupations like accommodation, food service and manufacturing which include a large amount of predictable physical work are likely to be automated, similarly work in finance and insurance which involves much processing of data. On the other hand jobs in construction and in agriculture which comprise predominantly unpredictable physical work are unlikely to be automated, at least at present. And there is good news for teachers: “the importance of human interaction is evident in two sectors that, so far, have a relatively low technical potential for automation: healthcare and education.”

The future of work – myths and policies

I like this blog post by Robert Peal entitled ‘A Myth for Teachers: Jobs That Don’t Exist Yet’. The article looks at the origins of the idea that the top 10 in-demand jobs in 2010 didn’t exist in 2004 and its later variant that 60 per cent of the jobs for children in school today have not been invented. In both cases he found it impossible to track these statement in any reliable research. Of course these are myths. But often such myths can be tracked back to quite prosaic political objectives.

For a long time, the European Union has pushed the idea of the knowledge society. And whilst there are many learned papers describing in different ways what such a society might look like or why such a society will emerge there is little evidence of its supposed impact on labour markets. Most common is the disappearance of low and unskilled jobs, linked to growing skill shortages in high skilled employment. Yet in the UK most recent growth in employment has been in low skills, low paid jobs in the retail sector. I remember too in the late 1990s when the European industry lobby group for computers were preaching dire emergencies over the shortage of programmers, with almost apocalyptic predictions of what would happen with the year 200 bug if there were not major efforts to train newcomers to the industry. Of course that never happened either and predictions of skills shortages in software engineering persist despite the fact the UK government statistics show programmers pay falling in the last few years.

I’ve been invited to do several talks in the last year on the future of work. It is not easy. There are two lengthy reports on future skills for the UK – ‘Working Futures 2012- 2022’ and ‘The future of work: jobs and skills in 2030’, published by the UK Commission for Skills and Industry. Both are based on statistical modelling and scenario planning. As one of the reports says (I cannot remember which) “all models are wrong – it is just that some of more useful than others. Some things are relatively clear. There will be a big upturn in (mainly semi skilled) work in healthcare to deal with demographic changes in the age of the population. There will also be plenty of demand for new skilled and semi skilled workers in construction and engineering. Both are major employment sectors and replacement demand alone will result in new job openings even if they do not expand in overall numbers (many commentators seem to forget about replacement demand when looking at future employment).

But then it all starts getting difficult. Chief perhaps amongst this is possible disruptions which can waylay any amount of economic modelling. The following diagram above taken from ‘The future of work: jobs and skills in 2030’Ljubiana_june2015.001 shows possible future disruptions to the UK economy and to future jobs. One of these is the introduction of robots. With various dire reports that up to 40 per cent of jobs may disappear to robots in the next few years, I suspect we are creating another myth. Yes, robots will change patterns of employment in some industries, and web technologies enable disruptions in other areas of the economy. Yet much of the problems with such predictions lay with technological determinism – the idea that technology somehow has some life of its own and that we cannot have any says over it. At the end of the day, despite all the new technologies and the effects of globalization, there are massive policy decisions which will influence what kind of jobs there will be in the future. These include policies for education and training, inter-governmental treaties, labour market and tax policies, employment rights and so on. And such considerations should include what jobs we want to have, how they are organised, where they are and the quality of work. At the moment we seem to be involved in a race to the bottom – using the excuse of austerity – which is a conscious policy – to degrade both pay and work conditions. But it doesn’t need to be like this. Indeed, the excuses for austerity may be the biggest myth of all.




The challenges of open data: emerging technology to support learner journeys

As promised, a post on our stand and presentation at Alt-C on the LMIforAll Labour Market Data project, sponsored by UKCES. Working together with the Institute for Employment Research at Warwick University and Raycom, we have developed a database and APi providing access to a range of data about a wide variety of different occupations in the UK including data about:

  • Pay
  • Gender
  • Numbers employed
  • Future employment projections
  • Occupational profiles
  • Skills and competences
  • Job vacancies
  • University destinations

The API is self documenting and is available free of charge to both for profit and not for profit organisatio0ns and developers. Working with Loud Source we have run a competition for Apps built on the API and together with Rewired State we have organised a series of Hack Days and Mod Days. We are currently redesigning the website to provide better access to the data and to the different applications that have been built to date.

One strange thing that took people visiting our stand some time to understand was that we were not selling anything (I think ours and Jisc were the only non commercial stands).  The second thing was that we were not trying to ‘sell’ them a shiny out of teh box project. To get added value from our database and API requires some thought and development effort on the part of organisations wanting to use the data. We provide the tools, they provide the effort to use them. But when people got that concept they were enthusiastic. And most interestingly they were coming up with completely new ideas for where the data might be valuable. As you can see in our presentation above, we have largely focused on the use of LMIforAll for careers planning. University and Further Education researchers and developers saw big potential using the API as a planning too for future courses and curriculum. Others saw it as a valuable resource for measuring employability, a big agenda point for many UK institutions. It was also suggested to us that the labour market data could be mashed together with data derived from learning analytics, providing possibly a more learner centred approach to analytics than has previously been deployed.

If you are interested in any of these ideas have a play on the LMIforAll web site. And feel free to get in touch if you have any questions.