Pew Research Center and Elon’s Imagining the Internet Center canvassed 1,408 technologists, scholars, practitioners, strategic thinkers, and education leaders about what was likely to evolve in workplace learning by 2026.
The specific question asked was: In the next ten years, do you think we will see the emergence of new educational and training programs that can successfully train large numbers of workers in the skills they will need to perform the jobs of the future?
Seventy percent of the respondents said “yes” such programs will emerge. A majority of the 30 percent who said “no” generally do not believe adaptation in teaching environments will be sufficient to teach new skills at scale.
Respondents collectively articulated five major themes:
The training system will evolve, with a mix of innovation in all education formats
- More learning systems will migrate online. Some will be self-directed and some offered or required by employers; others will be hybrid online/real-world classes. Workers will be expected to learn continuously.
- Online courses will get a big boost from advances in augmented reality (AR), virtual reality (VR), and artificial intelligence (AI).
- Universities still have special roles to play in preparing people for life, but some are likely to diversify and differentiate.
Learners must cultivate 21st-century skills, capabilities, and attributes
- Tough-to-teach intangibles such as emotional intelligence, curiosity, creativity, adaptability, resilience, and critical thinking will be most highly valued.
- Practical, experiential learning via apprenticeships and mentoring will advance.
New credentialing systems will arise as self-directed learning expands
- While the traditional college degree will still hold sway in 2026, more employers may accept alternate credentialing systems as self-directed learning options and their measures evolve.
- The proof of competency may be in the real-world work portfolios.
Training and learning systems will not meet 21st century needs by 2026
- Education systems in the next decade will not be up to the task of adapting to train or retrain people for skills most prized in the future.
- Many doubts revolve around a lack of political will and necessary funding.
- Some people are incapable of or uninterested in self-directed learning.
Jobs, what jobs? Technological forces will fundamentally change work and the economic landscape.
- There will be many millions more people and millions fewer jobs in the future.
- Capitalism itself is in real trouble: the future of jobs for humans is so baleful, that capitalism may fail as an economic system.
As we think about the future of training, the question must be asked, “Training for what?” The question Pew asked the respondents was, “Do you think we will see the emergence of new educational and training programs that can successfully train large numbers of workers in the skills they will need to perform the jobs of the future?” If there are significant numbers of workers to be re-skilled, you can bet that those skills will become targets for the AI community.
In the short-term, many of us will be training and educating ourselves to fill-in the gaps – places automation and AI haven’t filled yet; but where will those gaps be and for how long? How do we stay relevant?
Jobs that are repetitive and low-skill have been on the automation radar for a long-time. There is an increasing body of research, however, that higher level jobs are not immune to the more sophisticated AI: dermatologists, insurance claims adjusters, lawyers, retail salespeople, psychological testers, medical test and data analysts, and even computer programmers who create the algorithms, to name a few.
Are CEOs safe? CEOs are people, and people are prone to many psychological biases that cause inferior decision making. No, CEOs are not safe.
If we are going to upgrade our employability, we may need to rethink our definition of what it means to be “smart”. Prof. Ed Hess in the Harvard Business Review (June 19, 2017), makes that argument. We cannot he argues, outsmart the likes of IBM’s Watson. Smart machines can store, process and recall information more quickly than we can, as well as pattern-match and produce more alternative solutions faster and more cheaply. So where will our “smarts” be?
For Prof. Hess, our new definition of “smart” must be one that promotes higher levels of human thinking and emotional engagement – the quality of our thinking, listening, relating, collaborating, and learning. “We will spend more time training to be open-minded and learning to update our beliefs in response to new data. We will practice adjusting after our mistakes, and we will invest more in the skills traditionally associated with emotional intelligence. The new smart will be about trying to overcome the two big inhibitors of critical thinking and team collaboration: our ego and our fears.”
Maybe, maybe not. That’s the problem with the future; it’s always wait and see!
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