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  • Written in 1920, R.U.R. (Rossum’s Universal Robots) by Karel Čapek is most well-known for having coined the term ‘robot.’ Although derided by many (including Isaac Asimov, who called the play ‘terribly bad’) it anticipates and responds to a Ann portent argument that continues to be used to justify automation projects today.
  • The most common argument for automation (one that is used by almost every vendor) is not new. It dates back to Aristotle who used the same logic to justify using slaves, women, and children in similar ways.
  • The most important contribution of Čapek’s play is not just that it coins a term, but in the work the term does. By deliberately connecting automation with Aristotelian slavery, and then viewing the results through a pragmatic lens, Čapek challenges us to consider the consequences of a technology-centered approach to automation and consider whether a more human approach is possible. …

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Our current use of AI in higher education involves automating parts (and at times the whole) of the human decision-making process. Where there is automation there is standardization. Where there are decisions, there are values. As a consequence, we can think of one of the functions of AI as the standardization of values. Depending on what your values are, and the extent to which they are reflected by algorithms as they are deployed, this may be more or less a good or bad thing.

Augmenting Human Decision-Making

An example of how AI is being used to automate parts of the decision-making process is through nudging. According to Thaler and Sunstein, the concept of nudging is rooted in an ethical perspective that they term ‘libertarian paternalism.’ Wanting to encourage people to behave in ways that are likely to benefit them, but not also wanting to undermine human freedom of choice (which Thaler, Sunstein, and many others view as an unequivocal good), nudging aims to structure environments so as to increase the chances that human beings will freely make the ‘right decisions.’ In higher education, a nudge could be something as simple as an automated alert reminding a student to register for the next semester or begin the next assignment. It could be an approach to instructional design meant to increase a student’s level of engagement in an online course. It could be student-facing analytics meant to promote increased reflection about one’s level of interaction in a discussion board. Nudges don’t have to involve AI (a grading rubric is a great example of a formative assessment practice designed to increase the salience of certain values at the expense of others), but what AI allows us to do is to scale and standardize nudges in a way that was, until recently, unimaginable. …

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How should we approach the evaluation of predictive models in higher education?

It is easy to fall into the trap of thinking that the goal of a predictive algorithm is to be as accurate as possible. But, as I have explained previously, the desire to increase the accuracy of a model for its own sake is one that fundamentally misunderstands the purpose of predictive analytics. …


Timothy Harfield

Engaging communities at the intersection of humanism and technology.

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