In November of 2018, the Joint Research Center, the European Commission’s science and knowledge service, released a report that is aimed at policy makers but that also contains important information for those studying AI’s potential influence on the future intersection of society, education, and workplace.

I read it at least 10 times – it was that fascinating to me. It was the best explanation of the history and abilities of AI vs ML I’ve come across.

The obvious elephant in the room is the demand on the modern educational system to cultivate competencies that allow people to be contributing members of the economic sphere – in the face of prospective automation.

Most of us heard the high-level speculation and/or viewed the TED talks such as those in the TED Playlist on Artificial Intelligence. However, this report took the hard questions such as “Which occupations will become obsolete and what will be valuable skills in a world where AI is widely used?” and placed them into a context where I saw the clues on how they could be addressed.

According to Tuomi (2018), “AI can enable new ways of learning, teaching, and education, and it may also change the society in ways that pose new challenges for educational institutions. It may amplify skill differences and polarize jobs, or it may equalize opportunities for learning. The use of AI in education may generate insights on how learning happens, and it can change the way learning is assessed.

(Tuomi, 2018, p. 5)

The author dedicates the bulk of the report to an explanation of the history, types, and the impact of technological enablement of AI/ML, then relating it to models of learning, psychology, and society.

Midway through came the eye-opener for me. Recent studies, notably those of Frey and Osborn, have taken a risk-based rather than a skill-based approach to assessing possible automation. The subtle difference got me (as in I had to reread it several times) and it revealed an essential comprehension for me.

According to Tuomi (2018), “In skill-biased models, jobs that do not require educated, experienced, and skilled workers are susceptible to automation. In such models, computers are expected to be used mainly for tasks requiring limited skill. It becomes natural to assume that to avoid unemployment people need more and higher-level education” (p. 23).

It is not quite that simple, especially while AI is rapidly progressing. In a task-based analysis, something deeper is revealed. That is a reassessment of occupations where many daily tasks may be susceptible to automation, regardless if, overall, it requires higher cognitive activities to fill the job role. Check out Table 1 on page 23 about Middle School Teacher task automation!

This is the most grounded explanation of what I have 100% bought into already – we cannot effectively focus on work related skills and instead need to focus on building competencies platforms that enable lifelong learning.

I suggest you take a read at the more detailed argument presented in the report, including some notes on the limitation of AI/ML talent on progress, the importance of “no AI without UI”, and the last 10 pages on the impact of AI that I am not walking you through here.

In general, I am fascinated because there are only a few on the leading edge of getting far enough ahead of this as to minimize the level of impact that occurred during industrialization.

Further Tuomi states (2018), ” When AI systems predict our acts using historical data averaged over a large number of other persons, AI systems cannot understand people who make true choices or who break out from historical patterns of behaviour. AI can therefore also limit the
domain where humans can express their agency” (p. 39).

It almost seems imperative that we actually widen the net to acquire personal data for learning and educational purposes past that which is primarily owned by corporations. At the end of the say – I hope we find the ability to cooperate will enough to serve learners freedom of agency.