What is Industry 4.0, and what does it mean for learning?

Updated: Jul 25, 2020

(Photo by Louis Reed on Unsplash)

How can learning support and prepare workers for a workplace that is already changing due to automation? In our 'post fact' world, will we need to rely on artificial intelligence to do our thinking for us? Industry 4.0 has already started, and it means big changes for the skills and knowledge that people need - how can education help? How much of an impact does teacher effectiveness make on learning outcomes for students?

Welcome to The Future Learning Project's post for the week. Inside, we are taking a look at just a few of the different elements that make up Industry 4.0: automation, the use of artificial intelligence and examples of how these are applied in practice. We look at implications for workers, what learning needs to look like so that people can thrive, and why the present focus of most education systems is both insufficient and ineffective.

'Too much time discussing whether robots can take your job; not enough time discussing what happens next.'

Our first article looks at implications for jobs in the age of automation: lower worker skill means lower wages, along with less job security and fewer benefits. Two classes of intervention and support for workers are identified:

1. A 'quasi-Luddite' approach, in which automation is either slowed or reversed through measures such as taxing products produced by robots or automation.

2. Developing 'coping strategies', in this case a) a universal basic income (UBI) and b) retraining and up-skilling of workers.

Each is recognised as being insufficient - given emerging trends derailing automation is unlikely to work, and we don't know enough about how UBI and worker re-training is going to work. That's exactly what the article concludes - there needs to be a lot more research into exactly how we intend to manage this transition, and identify which interventions are likely to succeed.

But first, let's start the conversation - in our case, and about what automation means for learning in all its forms.

In our 'fake news' reality, Project Debater from IBM Research is looking to cut through the post-fact world of one-sided narratives and misinformation. It uses AI to digest huge amounts of information on a topic, and construct thoughtful, critical arguments uninhibited by bias or emotion.

The team is working to increase AI's comprehension and narrative language abilities in order to provide diverse, well-informed viewpoints during actual debates vs. a human opponent. What's interesting is that the debate topic is not known in advance, and the AI is not pre-trained. The AI is designed to quickly mine hundreds of millions of information sources such as journals and newspaper articles, pick the most compelling and well-supported arguments, and present them. It also listens and responds to the human debater using Watson speech to text.

Have a look at the video embedded in the page - it's pretty incredible even in its early stages. So what are the implications for learning?

Our post-fact, fake-news reality appears to have evolved from an inability to engage critically with the information we receive. We are unable (and/or unwilling) to look beyond our personal biases, explore the pros and cons of multiple viewpoints and maintain an open and thoughtful approach when encountering something new. Us humans are very capable of doing this, which like all skills can be learned and practised.

We need to wage a war on lazy, superficial thinking in our schools and workplaces, and it needs to happen urgently. If we don't, AI might end up doing it for us. Surely humans are better than that.

More data will be generated and analysed within the next two years than the whole of previous human history. Many tasks involving visual inspection are already performed better by machines than humans - in a couple of years it will be 'most' tasks. The per-unit cost of a multi-axis industrial robot is now about USD$12,000, down from hundreds of thousands of dollars ten years ago. Brain-machine interfaces are shipping for commercial use in 2018. With advanced 3D printing, almost any design can be prototyped and tested in the market within hours.

Welcome to Industry 4.0 - one in which large scale manufacturing combines with personalisation, constant changes to product lines, fewer human workers, vastly improved efficiency and extremely short delivery times. Startup manufacturers have already begun, and large ones are taking notice.

Fortunately, the cost to jobs is not expected to be as dramatic as feared. New factories operating in this space will provide opportunities for new types of work, and existing factories will likely remain fundamentally unchanged until their equipment needs replacing. However, jobs will still be lost.

So what does this mean for learning? Unfortunately, this article doesn't offer much direction, beyond that people should "... retrain to work more efficiently alongside supporting robots... (or) ...transition into something else." However, reading between the lines we can find some clues:

1. People will need to develop a capacity for life-long learning.

2. Human jobs will become more non-routine and require greater technical capability, with skills and competencies that cannot be replicated by machines.

3. Those who understand what is already happening, and are prepared for it, are better positioned to flourish in the new economy.

It might almost be time for our education and learning systems to demonstrate the same sort of openness to change and innovation that almost all businesses and private enterprises have to. Their survival is at stake if they don't, and the gap between the way students learn and the real world grows ever wider. Let's have a discussion about how we can effect meaningful change, and leave your thoughts in the comments.

Given these trends, how are governments and education systems working to prepare students for future success? Improved teacher effectiveness has been a focus for many education systems globally, with enhanced teacher training, standards based assessments linked to teacher performance, and programmes to have high-performing graduates teaching in low-performing schools. In the United States the Intensive Partnerships for Effective Teaching Initiative was designed and funded by the Bill and Melinda Gates Foundation to dramatically improve student outcomes through greatly improved access to effective teaching. Teaching effectiveness (TE) was assessed, feedback given, improvements made and best practices shared and refined. The 575 million dollar result?

'With minor exceptions, by 2014–2015, student achievement, access to effective teaching, and dropout rates were not dramatically better than they were for similar sites that did not participate in the Intensive Partnerships Initiative.'


'A near-exclusive focus on Teaching Effectiveness might be insufficient to dramatically improve student outcomes. Many other factors might need to be addressed, ranging from early childhood ....'

Ouch. These findings appear to align with The Coleman Report (1966) and more recent research from Deary (2007), which highlighted that school and teacher variables together account for less than 10% of variance in a student's achievement at school. In New Zealand, socio-economic factors have been identified as having a far greater influence on post-secondary achievement than any other by a considerable margin.

There is no doubt that good teachers matter, but the focus on teacher effectiveness and standards exclusively will be insufficient to transform how out students learn, and how we develop human potential so that people can thrive in an uncertain future.

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.


#Automation #ArtificialIntelligence #Lifelonglearning #Retraining #Learning

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