If for nothing else than the future of your children, take 12 minutes to watch this.
If you agree, then thriving in the 4th industrial revolution will require nothing short of restructuring public education at all levels, not just k-12. Even doctors will need to change how they educate their young. How many things can you do with a paperclip?
Many not for profits are directing their efforts to provide equitable access to public education. However, putting more students in a broken, dysfunctional system won’t yield the outcomes and impact we want. Instead, the very structure and process of education will need to change if we are to provide students with the knowledge, skills, abilities and competencies they need for jobs that have yet to be created.
What’s more, unless we address the gender social and cultural stereotypes, the 4IR could make gender inequity worse, not better.
One goal should be to create entrepreneurial schools and universities, and by that I don’t mean teaching children how to start businesses. Instead, creating the entrepreneurial mindset is about the pursuit of opportunity with scarce resources with the goal of creating user defined value through the deployment of innovation. Creating a successful business in but one of many ways to do that.
Here are 10 different ways to encourage youth entrepreneurship. The same techniques might apply to graduate students as well.
Other learning objective and curriculum themes are emerging:
3 storytelling (see 1-2)
4 critical thinking (not cynicism)
6 active listening (hear with your eyes)
7 networking (trust and giving)
8 good customer service
9 how to sell
10 to fight against entitlement
Here are some recommendations to Promote digital education and workforce development
As AI applications accelerate across many sectors, it is vital that we reimagine our educational institutions for a world where AI will be ubiquitous and students need a different kind of training than they currently receive. Right now, many students do not receive instruction in the kinds of skills that will be needed in an AI-dominated landscape. For example, there currently are shortages of data scientists, computer scientists, engineers, coders, and platform developers. These are skills that are in short supply; unless our educational system generates more people with these capabilities, it will limit AI development.
For these reasons, both state and federal governments have been investing in AI human capital. For example, in 2017, the National Science Foundation funded over 6,500 graduate students in computer-related fields and has launched several new initiatives designed to encourage data and computer science at all levels from pre-K to higher and continuing education. The goal is to build a larger pipeline of AI and data analytic personnel so that the United States can reap the full advantages of the knowledge revolution.
But there also needs to be substantial changes in the process of learning itself. It is not just technical skills that are needed in an AI world but skills of critical reasoning, collaboration, design, visual display of information, and independent thinking, among others. AI will reconfigure how society and the economy operate, and there needs to be “big picture” thinking on what this will mean for ethics, governance, and societal impact. People will need the ability to think broadly about many questions and integrate knowledge from a number of different areas.
One example of new ways to prepare students for a digital future is IBM’s Teacher Advisor program, utilizing Watson’s free online tools to help teachers bring the latest knowledge into the classroom. They enable instructors to develop new lesson plans in STEM and non-STEM fields, find relevant instructional videos, and help students get the most out of the classroom. As such, they are precursors of new educational environments that need to be created.
Gary Kasparov notes that his loss in 1997 to IBM supercomputer Deep Blue was a victory for its human creators and mankind, not triumph of machine over man. In the same way, machine-generated insight adds to ours, extending our intelligence the way a telescope extends our vision. We aren’t close to creating machines that think for themselves, with the awareness and self-determination that implies. Our machines are still entirely dependent on us to define every aspect of their capabilities and purpose, even as they master increasingly sophisticated tasks.
Here are some challenges future generations will need to solve:
Our economy and standard of living hinges on meeting these wicked challenges. But, like medicine, government and other risk averse and sclerotic industries, the resistance to change will be substantial. Only bottom up pressure led by creative, courageous innovators who practice what they teach will remove the obstacles in our path. Many of those obstacles are in the classroom next door or the corner office.