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When Algorithms Give Real Students Imaginary Grades

students working on laptops at same table

This op-ed by throws light on the extremely troubling use of predictive grading for assessing the International Baccalaureate examinations, which recently gave thousands of students a failing grade based on data points like the student’s socio-economic demographics, teacher estimated grades, and other measures. Broussard discusses how the algorithms enabling predictive grading are rooted in racial and class biases, which disproportionally failed students of color in low income areas. Broussard drives home a clear lesson: “Algorithms should not be used to assign student grades. And we should think much more critically about algorithmic decision-making overall, especially in education. The pandemic makes it tempting to imagine that social institutions like school can be replaced by technological solutions. They can’t.” It is high time that we reimagined the role of artificial intelligence in teaching and learning.

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