AI, Retraining, Slavery, Collaboration and Learning

Updated: Jul 25


(Photo by Khuee Vu M. on Unsplash)

Artificial Intelligence is becoming embedded in our daily work, business and personal lives - how can it benefit learning? Whose responsibility is it to up-skill and retrain workforces? How can learning and education be an antidote to the rise in modern slavery? Can successful collaboration be measured through testing?

At The Future Learning Project we work hard to learn about, understand and share what the future of learning might look like. We are interested in the factors that influence how the world of work is changing, and what this means for how people learn. This week's focus is:

1. A brief explanation about Artificial Intelligence (AI), pioneers in the field and implications for learning.

2. How one company is taking the initiative in retraining its workforce for the new world of work, and why schools and post-secondary learning don't feature in their plans.

3. How rapid automation may lead to a rise in modern day slavery, and why education may be the key to preventing it.

4. Why collaboration is being recognised as an essential skill in learning, and how successful collaboration is being measured.

We often share examples of Artificial Intelligence (AI) as an exponential and disruptive technology that has huge implications for how we work, live and learn. But what exactly does AI do, how does it work, and how does it help?

AI is a branch of computer science, in which a machine simulates human-like intelligence such as reasoning, problem solving, visual perception and speech recognition. AI is driven by powerful computers and complex algorithms, and can process and understand huge amounts of data extremely quickly. Within AI, there are branches developing 'Weak AI' (for particular tasks) and 'Strong AI' (general intelligence systems). Machine learning (ML) is a subset of AI, in which computers learn in a similar way to humans by interpreting data, classifying it, and learning from positive or negative experiences. Amazon.com could not run its highly complex yet efficient business model without machine learning.

This article looks at AI pioneers that are transforming our world, and we've gone through and had a look at their work. Here are three we thought you might like:

1. CognitiveScale - analyses and cross-checks huge amounts of company, market and public data in real time to learn about a business and help leaders make informed decisions. Data includes text, images, and video, e-Commerce platforms, Electronic Health Records, data warehouses, transaction records, social media, individual devices - every part of the business and its market is under constant scrutiny.

2. Primer - automatic analysis and summaries of huge sets of data generated in fortune 50 companies, governments, financial institutions and global media. It reads text, graphs and datasets, operates in multiple languages and generates human-readable reports automatically.

3. Pymetrics - bias-free hiring of workers through neuroscience games and automatic matching of identified personal traits to work opportunities.

AI has already entered the education space (more in a future post), with companies looking at automating back-office tasks, AI-driven private tuition and remedial learning, customised resource materials, powering wearable technology - the list goes on.

There is no doubt that AI will become a major part of education and learning, and could become a powerful partner in enhancing learning for our students. With the power of AI, one downside might include a tendency to find and track every possible data point where none currently exist. Imagine tracking attendance, family engagement, classroom noise, student movement, on or off-task behaviour, teacher/student interactions, toilet breaks, books read, words written, movement around playgrounds, social interactions, devices currently in use, assessment results - all recorded and analysed in real time, with recommendations for teachers and school leaders.

Is this desirable?

The importance of workforce retraining is a major common theme across the many articles we read when looking at the future of learning and work. The automation of once-routine tasks is already leading to human work being augmented or replaced by AI and machines, and this process is likely to accelerate.

A serious potential negative outcome is the loss of jobs - lots of them. Jobs may be lost far more quickly than they can be replaced, especially if people are not trained to take on new forms of work. Hence, retraining for workers will become a priority, and Siemens is thinking about what possible solutions might look like. The CEO, Joe Kaeser, sees the need for governments and businesses to solve the retraining challenge together, with government the framework and business the content.

Siemens currently spends about 500 million Euros each year on training and qualifications for its 370,000 workers globally, and is seeking to increase this investment. It's a smart start, and reassuring to see that global business leaders of significance are taking this issue seriously.

Schools and pre-tertiary education are not often given prominence when it comes to being a solution that provides workers with the skills they need to thrive in an era of automation. We find the occasional reference and report it here, but more usually we see generic references to governments, tertiary education, businesses and workers sharing the responsibility in some way. Why is this?

Is pre-tertiary learning in the 'too hard' basket, not seen as relevant to the conversation or simply not considered? Or a part of the 'government' mix? Or something else?

In the past we have looked at several examples of how workers might be displaced by automation and the serious negative impacts that this might have for families and communities. Mass layoffs due to automation of the garment industry in Bangladesh is one example, but here's another.

There are fears of a rise in slavery as an increasing pool of laid-off workers compete for an ever-diminishing pool of jobs, resulting in conditions ripe for worker exploitation in a 'race to the bottom'. The effects of automation will be felt most keenly in developing countries: Cambodia, The Philippines, Indonesia, Vietnam and others, in which much of the workforce consists of unskilled labour performing routine manual tasks.

Comprehensive social safety nets do not exist in these countries, and there are genuine fears about not only the potential for slavery but the social disruption and upheaval that might be caused. The majority of workers in the garment, textile and footwear industries in Asia, all highly vulnerable to automation, are women.

There are calls for regional governments to adopt 'concrete measures' to educate future generations to function alongside machines, but detail is light about what this actually might look like in practice. If governments do get serious about transforming their education systems to meet this threat, it would a huge undertaking.

We've had a look at Vietnam's (a high-risk country) General Education Renovation Project, and although not Earth-shattering (no inter-disciplinary elements etc.), given the context it is a good start with a completely rewritten competence-based curriculum that is hoped will facilitate a 'paradigm shift' from knowledge acquisition to how students can use knowledge to solve problems.

The test, of course, will come in the implementation: teacher training and up-skilling, community and parent education, improved resourcing, assessment that aligns with this new philosophy etc - a similar effort in 2001 failed due to a lack of trained teachers.

Watch this space as we track this issue - the timeframe for automation of these industries will be short and upon us quickly. Might there be other solutions or ideas for how we might tackle this? Leave your thoughts in the comments.

An ability to collaborate, or work with someone to produce something, is seen as an essential skill in articles we read describing what it takes to thrive in the emerging economy - an era of exponential technologies and disruptive innovation changing the world of work, business and life.

Public school systems are picking up on its importance, and activities that develop an ability to collaborate are being designed into how students learn. Project Lead the Way (PTLW) specialises in project-based STEM learning for students, and is looking to measure how well its students can collaborate. Their answer? Testing.

Not just traditional multiple-choice, but tests that rely on technological advancements to enhance the testing experience. Instead of multiple choice, it's drag and drop answers, collaboration with an AI to solve problems, and fill in the blanks. The jury's out on how different this actually is to traditional tests.

Linda Darling-Hammond from the Learning Policy Institute favours performance assessments (portfolios, observations etc.) through hands-on tasks, given that collaboration traditionally relies on human interaction and teamwork. But then human bias can creep in, and some are concerned that the data can become 'muddied'. So what's the way forward?

We think the answer is both. PTLW is taking a step in using emerging technologies to try and measure a soft skill - not an easy thing to. Just because it's not 100% right now doesn't mean they should not persist, and they may come up with something significant one day. Observation and recording of process is also important, and having excellent teachers well-trained to do so can only benefit students.

Should testing be kept or cast aside? Or should be focus on observing performance over time? Or both? Leave your thoughts in the comments below.

Thank you for joining us this week. Please don't forget to comment on our articles and posts - we want to share ideas, critical thought and constructive feedback.

Sean

#ArtificialIntelligence #AI #Learning #Retraining #Collaboration

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