Artificial Education
This August, an MIT committee of faculty, students, and staff released a remarkably thoughtful report on “AI Use in Teaching, Learning, and Research Training.” Its many cautions included the warning that “as a community, what should worry us most is that many uses of AI deprive students of the opportunity to learn.”
By contrast, Harvard’s administration appears oblivious to such concerns — notoriously so. Harvard College Dean David J. Deming’s recent recommendation that student use of AI in writing-intensive classes should be not only accepted but encouraged, is only the most outrageous instance. This policy was, fittingly, the leading example cited in a Chronicle of Higher Education article titled “The Ivy League’s Surrender to AI Is Insane.”
Has Harvard indeed lost its mind? The comparison with the MIT report, set against a growing body of empirical research into the effects of generative AI on learning, is a good place to start answering that question.
While of course noting the astounding benefits of AI in many areas of academic research, the MIT report takes careful stock of the ways in which reliance on AI risks the abandonment of crucial educational experiences, including the problem set, the take-home exam, office hours, study groups, and undergraduate research opportunities. In the latter case, the committee found that instructors were considering outsourcing research assistance to AI instead of hiring undergraduates, presumably on the grounds of cost-savings and efficiency. The committee inquired, “But if those criteria come to dominate our decisions, we all have to ask, ‘What is it that we are here together to do?’”
