Notes on Software and Teaching Jobs in Higher Ed After Skimming the Pew report on AI, Robotics, and the Future of Jobs

I came across the Pew report (http://www.pewinternet.org/2014/08/06/future-of-jobs/) referenced in the subject line via a piece in Slate by Will Oremus (http://www.slate.com/articles/technology/future_tense/2014/08/the_new_luddites_what_if_automation_is_a_job_killer_after_all.html). 

Pew canvassed 1896 people by sending an " 'opt in' invitation to experts who have been identified by researching those who are widely quoted as technology builders and analysts and those who have made insightful predictions to our previous queries about the future of the Internet." 

52% think technology will lead to new and better jobs overall, as it historically has done, but 48% worry that technology is replacing jobs and not creating enough new ones.

Oremus framed it this way:

Replacing manual labor with machines on farms and in factories was one thing, the worriers say. Those machines were dumb and highly specialized, requiring humans to oversee them at every stage. But the 21st century is witnessing the rise of far smarter machines that can perform tasks previously thought to be immune to automation.

Today’s software can answer your calls, organize your calendar, sell you shoes, recommend your next movie, and target you with advertisements. Tomorrow’s software will diagnose your diseases, write your news stories, and even drive your car. When even high-skill “knowledge workers” are at risk of being replaced by machines, what human jobs will be left? Politics, perhaps—and, of course, entrepreneurship and management. The rich will get richer, in other words, and the rest of us will be left behind.

All of which has brought John Maynard Keynes’ concept of “technological unemployment” back into the academic discourse, some 80 years after he coined the phrase.

Both the Oremus piece, which includes links to other sources, and more importantly the Pew report itself, would do well in a writing course exploring technology and culture, or technology and economics. That nearly 1900 experts are divided, nearly evenly, on the future of jobs, makes this the kind of topic that will not have easy answers.

And remember, this use of technology is getting into education too, with the push for self-pace (lone learner) platforms that seemingly seek to be student proof or teacher proof  by using adaptive tools, automated assessment, interstitial quizzing and other means to funnel students over a prescribed learning path until they come out the other end having satisfied the learning gauntlet. Pearson's Propero is good example of this kind of learning technology; it's being offered to colleges as a way have students take a course and earn college credit, all with no instructor. It's software designed to replace a person with an MA or PhD who would otherwise teach the course.

So it's not just future jobs that are at stake, but current jobs too. There's a balance lost, especially in learning, when machines take over decision making. Software with fixed paths, even if adaptive and adjusting the content to what is not yet been mastered (as measured by what the software can measure), really doesn't teach learners to learn.

Deep learning requires that learners get advice from fellow learners and instructors, reflect on their prior learning choices and next options, and then make decisions about their own learning, good and bad ones, and learn from those decisions, whether good or bad, something about the subject of the course as well how they learn.

But I don't want to say all software is bad. Adaptive learning, automated writing assessment tools, data on engagement, and other information gleaned from learner action and learner choices, can do great things for students and teachers. When designed to support a learning community, defined broadly as students and teachers talking to and learning with and from one another, educational software can provide evidence, show patterns, recommend learning strategies that students can discuss with classmates, teachers, advisers, and academic coaches. Learning, especially learning to read, write, and think critically is hard and sometimes messy work. Software can supply an ordered view into that messy process. Learning comes from understanding what the views mean or tell, and making choices about what is working, what not, and making changes, experimenting, and reassessing over and again.

Good technology -- software, for example, like Eli from MSU, or MARCA from UGA's Calliope Initiative, or MyReviewer from USF, TTU's RaiderWriter, LightSide's project to use AWE as a basis for student/teacher discussion instead of teacher replacement -- can all give teachers and students useful evidence of learning, or of engagement, that becomes the basis for reflection, planning, conferencing, talking to peers, revising, learning from seeing what is working, what is not, and from all that helping the writer and learner to make a choice -- provides a matrix for transformative assessment -- about what to work on next and how to go about it.

And that's what's useful about the Pew report and its divide. It teases out possibilities as well as perils.

Also, as an aside, Pew URL is interesting for a cool social networking feature. As you read the sampling of comments some of the 1896 people made, you'll see embedded in the text Twitter's bird icon. That indicates excerpts from the findings that Pew has tweeted and a link to the tweet where it was used. Kind of cool and rhetorical move worth its own discussion.