As universities increasingly adopt digital tools and automated analytics systems, attention often centers on these tools’ gains in accuracy and efficiency. Far less visible, however, is another critical dimension: the additional work students must do to produce, organize, and interpret their own data within these systems.
The greatest risk of AI in higher education isn’t cheatingit’s the erosion of learning itself
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