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.

This post by Sophia Roppertz and Ludger Deitmer is part of the TaccleAI project for “‘Improving the Skills and Competences of VET teachers and trainers in the age of Artificial Intelligence.” It describes what is clled a ‘Deep Reinforcement Learning Project” in a German Vocational Education and Training school.

The topic of the project was Deep Reinforcement Learning – preparation of the topic “artificial intelligence” and implementation of an agent in the game “Sonic the Hedgehog”. Sonic is a computer game series of the Japanese publisher Sega. The classic main parts of the series are characterized by fast 2D jump ‘n’ run passages. There you control the blue game character Sonic The Hedgehog through so-called “zones”, which are divided into individual “acts”. In all Sonic games, rings are collected, which the main character loses when touching an opponent. If he is hit without rings, you lose an extra life. In the classic main games, after using up all extra lives and continues, you have to start all over again after a game over.

The task of the student group was to implement an agent into the game and finally to give a project presentation about the project. To accomplish this overall goal, some intermediate goals had to be achieved:

1) Acquire an understanding of artificial intelligence and neural networks

2) Gain advanced knowledge of the Python programming language

3) The AI should master different levels independently

How is the project structured?

Trainees of the vocational school “information technology assistants” (German: “Informationstechnische*r Assistent*in”) took part in the AI project. The AI project took place in the second year of training within the framework of the learning field “Planning, implementing and evaluating projects” (practice). The total time required was 160 hours per school year. The project meetings usually took place on a full day of lessons. The students had the opportunity to work in the computer room or in the corresponding workshops of the school. During this time, a teacher was present to provide support but did not actively participate in the project.

As part of the KI project, the students were given a presentation on project management by the responsible teacher. With this knowledge team rules were established, field analyses were made, a target matrix was created, a schedule and work packages were created. The individual work packages were assigned performance specifications and outputs that had to be delivered. Responsibilities for the work packages were also defined. Furthermore, the students were assigned roles within the project group: e.g.

Team speaker: Moderates the group work and makes sure that everyone can get involved, that the topic is worked on consistently, and that the team rules are observed.

Timekeeper: Makes sure that the timetable is respected.

Foreign Minister: Communicates with people outside the team, maintains contact, and involves people.

What do the trainees learn in the project?

The trainees were able to acquire both technical and social skills in the course of this project. On the one hand, they learned project-oriented work in a group, they set themselves goals and divided and organised their work independently. On the other hand, they independently dealt with a programming language (Python) that was new to them and learned its basics to the extent that they were able to understand, modify, and create programs. In addition, the trainees have dealt with the basics of neural networks and the different terms of machine learning, so that they were able to present the basics to their fellow students and explain the terms. They acquired this knowledge mainly by watching videos. They used textbooks less because they mostly dealt with the AI topic in a very mathematical way and the mathematical knowledge of the students was not sufficient for this.

They have dealt with the topic “Deep Reinforcement Learning” and were able to program an agent to such an extent or to change existing programs in such a way that this “agent” learns to improve “his” game. In the end, they got so far into the programming of the “agent” that they were able to explain to their classmates which parameters they had to adjust/change so that their “agent” could improve his game.

Reflection and Recommendations for other teachers

The supervising teacher reports in the interview that basic knowledge in the field of AI is becoming increasingly important for information technology assistants since, in the context of the digitalised working world, processes are increasingly influenced by algorithms and the use of computers. In addition, many of the students attend the technical secondary school (In Germany: Fachoberschule für Technik) after their vocational schooling in order to subsequently complete a corresponding course of study. Since the students have to deal with the topic of artificial intelligence at the latest then, it makes sense to deal with it already in the vocational school. In the project documentation, the students report that it was surprisingly easy to acquire basic knowledge about AI. However, they emphasize that the deeper immersion in the subject matter was an obstacle, as more complex mathematical knowledge would have been necessary. The students report that reading about this AI content sometimes led to lower motivation and productivity. Overall, however, the students report that the choice of project was a good decision and that they have gained an advanced understanding of AI and its practical implementation.

When asked about what needs to happen on the part of the school and the teachers so that such projects can be practiced regularly, the teacher interviewed reported that, on the one hand, appropriate further training for the teachers is necessary. Besides the transfer of knowledge about AI, the joint development of teaching concepts should be more important. In addition, existing teaching materials should be jointly reviewed and classified. Useful material could then be made available to interested colleagues as Open Educational Resources. The exchange with product developers is considered desirable in the area of teacher training. In such a framework, the social, political, and sociological aspects of AI should be discussed more critically.

The teacher recommends that the students have a say in choosing the appropriate topic. Students need motivation and perseverance to work in project groups, so it is an advantage if the project tasks are linked to the students’ interests. In addition, clear evaluation criteria should be established and communicated transparently.

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.

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.”

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: https://www.nesta.org.uk/project/careertech-challenge/ and email Graham Attwell directly if you would like to know more about the CareerChat project

As part of the AI and vocational education and training project funded through the EU Erasmus plus project we are producing a series of case studies of the use of AI in VET in five European countries. Here is my first case study – the Ada chatbot developed at Bolton College.

About Bolton College

Bolton College is one of the leading vocational education and training providers in the North West of England, specialising in delivering training – locally, regionally and nationally – to school leavers, adults and employers. The college employs over 550 staff members who teach over 14,500 full and part time students across a range of centres around Bolton. The college’s Learning Technology Team has a proven reputation for the use of learning analytics, machine learning and adaptive learning to support students as they progress with their studies.

The Ada Chatbot

The Learning Technology Team has developed a digital assistant called Ada which went live in April 2017. Ada, which uses the IBM Watson AI engine, can respond to a wide range of student inquiries across multiple domains. The college’s Learning Technology Lead, Aftab Hussain, says “It transforms the way students get information and insights that support them with their studies.” He explains: “It can be hard to find information on the campus. We have an information overload. We have lots of data but it is hard to manage. We don’t have the tools to manage it – this includes teachers, managers and students.” Ada was first developed to overcome the complexity of accessing information and data.

Student questions

Ada is able to respond to student questions including:

1. General inquiries from students about the college (for example: semester dates, library opening hours, exam office locations, campus activities, deadline for applying for university and more);
2. Specific questions from students about their studies (for example: What lessons do I have today/this afternoon/tomorrow? Who are my teachers? What’s my attendance like? When is my next exam? When and where is my work placement? What qualifications do I have? What courses am I enrolled in? etc.)
3. Subject specific inquiries from students. Bolton College is teaching Ada to respond to questions relating to GCSE Maths, GCSE English and the employability curriculum.

Personalised and contextualised learning

Aftab Hussein explains: “We are connecting all campus data sets. Ada can reply to questions contextually. She recognises who you are and is personalised according to who you are and where you are in the student life cycle. The home page uses Natural Language Processing and the Watson AI engine. It can reply to 25000 questions around issues such as mental health or library opening times etc. It also includes subject specific enquiries including around English, Mathematics and business and employability. All teachers have been invited to submit the top 20 queries they receive. Machine learning can recognise the questions. The technical process is easy.” However, he acknowledges that inputting data into the system can be time consuming and they are looking at ways of automatically reading course documentation and presentations.

All the technical development has been undertaken in house. As well as being accessible through the web, Ada, has both IOS and Android apps and can also be queried though smart speakers.

The system also links to the college Moodle installation and can provide access to assignments, college information services and curriculum materials. The system is increasingly being used in online tutorials providing both questions for participants and access to learning materials for instance videos including for health and social care.

It is personalised for individuals and contextualised according to what they are doing or want to find out. Aftab says: “We are looking at the transactional distance – the system provides immediate feedback reducing the transactional distance. “

Digital assessment

Work is also being undertaken in developing the use of the bot for assessment. This is initially being used for the evaluation of work experience, where students need to provide short examples of how they are meeting objectives – for example in collaboration or problem solving. Answers can uploaded, evaluated by the AI and feedback returned instantly.

Nudging

Since March 2019, the Ada service has provided nudges to students with timely and contextualised information, advice and guidance (IAG) to support their studies. The service nudges students about forthcoming exams, their work placement feedback and more. In the following example, a student receives feedback regarding his work placement from his career coach and employer.

The College is currently implementing ProMonitor, a service which will offer teachers and tutors with a scalable solution for managing and supporting the progress made by their students. Once ProMonitor is in place, Ada will be in a position to nudge students about forthcoming assignments and the grades awarded for those assignments. She will also offer students advice and guidance about staying on track with their studies. Likewise, Ada will nudge teachers and student support teams to inform them about student progress; allowing for timely support to be put in place for students across the College.

A personal lifelong learning companion

For Aftab Hussein the persona of the digital agent is important.

For Aftab Hussein the persona of the digital agent is important. He  thinks that in the future that chatbot will morph into a personal cognitive assistant that supports students throughout their entire educational life, from nursery school to university and beyond.

“The personal assistant will learn from each student throughout their life and adapt according to what they like, while guiding them through studies. It could remind when homework is due, book appointments with tutors, and point towards services and events that might support studies, for example.”

Graham Attwell will be speaking at an online webinar – LiveCareerChat@Lockdown on 6 April. The webinar, organised by DMH Associates will focus on the future challenges for careers identities and careers advice and guidance

Deirdre Hughes says “During these turbulent times, we all have an opportunity for reflection, sharing ideas and offering practical advice on how best to manage career identity and changing work practices. This webinar is designed to bring people together and to listen and/or share experiences of careers support mechanisms at a time of crisis. ”

Graham Attwell will talk about the changing international labour markets and the challenges of new technologies, including AI and automation.

The webinar takes at 1630 – 1730 CEST on Monday 6 April and is free. You can register at https://dmhassociates.easywebinar.live/event-registration-3

I am speaking at a seminar on Vocational Education and Training’s Role in Business Innovation at the Ramon Areces Foundation in Madrid tomorrow. The title of my presentation is ‘The future of work, Artificial Intelligence and automation: Innovation and the Dual Vocational Education and training system in Valencia’ which is really much too long for a title and I have much too much to say for my allotted 20 minutes.

Pexels (CC0), Pixabay

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.