Learning the rules of predicting the future

Author: mweller
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I was asked to contribute a piece on the future of higher ed in the next 50 years. I don’t really like these sorts of things, they tend to be a bit ‘hoverboards for education!’ fluff pieces. But in exploring why I didn’t like them, I wrote the following, which I don’t think is what they were after. Here it is anyway, with added Parks and Recreation gifs:

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Predicting the future of education is a game to which we never seem to learn the rules. Despite nearly all previous predictions being wrong, the tendency is to continue in the belief that this time, technology will lead to wholesale revolution. The first rule to learn about change in higher education is that very little changes, while simultaneously everything changes. Any prediction that highlights just one of these elements underestimates either the immutability of the general higher education system, or the degree of innovation that actually does occur within it. So, a prediction would be that the future of education will look not dissimilar on the surface, but closer inspection will reveal significant changes around the use of technology to support learning.

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A second rule is that technological change is rarely about the technology. Take recent innovations such as eportfolios or digital badges. The technology here is fairly robust and straightforward, but what they require to have impact is a shift in cultural attitudes from employers and learners regarding recognition, the format of learning and alternative accreditation. A second prediction then will be that many existing technologies will still be around, but that some of them will have developed the appropriate social structures for broad adoption, whereas others will have withered in this task.

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The third rule is to appreciate the historical amnesia in much of educational technology. Every few years the same ideas are reinvented and heralded as a new innovation, for instance MOOCs were proclaimed by some to be the first attempt at online learning, which had in fact been working effectively for 15 years or so. A related prediction then will be that exactly the same technologies we see now will be present in the future, but under different names.

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The fourth, and final rule I would suggest is that technology is not ethically or politically neutral. This has become increasingly evident through the use of social media for political purposes, the misuse of data by Cambridge Analytica and the manner in which AI algorithms reinforce the gender or racial bias in much of society. The prediction here then is that awareness of this will continue to grow, with educators and learners viewing technology use in education as a political choice (whether to partake in data capitalism for instance) as an educational one.

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Some aspects will become commonplace as a trajectory of what we have now, for instance the use of online education will expand as people are increasingly comfortable and adept at creating social bonds online. The distinction between face to face and online diminishes, so all university study is to an extent, blended. The use of narrow AI focused on particular tasks will increase, but so too will the scepticism around what this means. Similarly learning analytics will become an increasingly contested ground, between what is possible, what is ethical, what is desirable from a learner perspective and what is useful for an educator.

In short, the future will have much resonance with the present, but it will be one where the relationship between people and increasingly powerful technology is one that is constantly examined and negotiated. I would not expect any grand revolution in the higher education space, the much quoted concept of disruption is almost entirely absent and inappropriate in this space. So don’t expect the type of future often predicted by educational technology entrepreneurs, with all existing universities made redundant by a new technology centric model. Instead we see a continual model of innovation, testing, adaption and revisiting within the constraints of an existing, and robust system.

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Learning the rules of predicting the future
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