{"id":2064721,"date":"2026-06-28T11:01:25","date_gmt":"2026-06-28T09:01:25","guid":{"rendered":"https:\/\/www.luxsure.fr\/2026\/06\/28\/what-rice-university-refuses-to-entrust-to-artificial-intelligence\/"},"modified":"2026-06-28T11:04:03","modified_gmt":"2026-06-28T09:04:03","slug":"what-rice-university-refuses-to-entrust-to-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/www.luxsure.fr\/en\/2026\/06\/28\/what-rice-university-refuses-to-entrust-to-artificial-intelligence\/","title":{"rendered":"What Rice University Refuses to Entrust to Artificial Intelligence"},"content":{"rendered":"\n

Forty thousand applications, each reviewed three times by a human, for an admissions class that will ultimately accept only 1,400: this is the only step that Rice University\u2014a Texas institution that has, for the past three years, been committed to an open embrace of artificial intelligence\u2014still refuses to delegate to a machine. This exception, defended by its president, sheds light on a question that the Houses are only just beginning to ask themselves regarding their own decision-making process. <\/p>\n\n

A deliberate exception<\/h2>\n\n

Reginald DesRoches has been president of Rice University for four years. A civil engineer by training, he has seen his institution\u2014with 9,000 students and located in Houston across from the nation\u2019s largest medical center\u2014shift in three years from a policy of prohibition to one of encouragement. Three years ago, the use of artificial intelligence was prohibited there under penalty of disciplinary action. Two years ago, it became tolerated provided it was disclosed. Today, the university is funding course redesigns centered on these methods and training its faculty to master them. <\/p>\n\n

And yet, when it comes to the specific process by which the institution decides whom to admit, nothing has changed. Each application continues to be reviewed three times by a single person, and half of the applicants continue to be invited for an interview. DesRoches puts it bluntly: the temptation to automate this screening process to identify successful candidates more quickly exists, but the risk of bias in such a critical decision remains, in his view, incompatible with delegating it to a machine. It is not a question of resources\u2014the university invests heavily in artificial intelligence in other areas. It is a question of context: where, precisely, human judgment remains irreplaceable. <\/p>\n\n

The ruling: a rare instance of jurisdiction<\/h2>\n\n

For DesRoches, the most important skill a student can have today is no longer mastery of knowledge, but the ability to evaluate what a machine offers them\u2014what he calls critical judgment. The example he cites is revealing: in a first-year writing course, a teacher has separate groups write the same essay\u2014some by hand, others using various generative systems\u2014and then compares the resulting structures. The goal is not to ban machines, nor to rely on them, but to highlight, through contrast, what they add and what they obscure in the act of writing. <\/p>\n\n

Jason Palmer, an innovation advisor at the National Center on Education and the Economy, tempers this enthusiasm, however, with a more concerning observation: writing is a form of thought, and early studies show that people surveyed after having a text generated by artificial intelligence often no longer know what that text contains. Delegating the task of writing means delegating part of the reasoning that precedes it\u2014and that part cannot be reconstructed afterward. <\/p>\n\n

Details<\/h3>\n\n

Since 2019, more than half of the graduate degrees awarded in the United States have been earned online or through hybrid programs. Over the same period, U.S. universities have awarded between five and six million short-term certifications\u2014ranging from three to twelve months in duration and covering topics such as digital skills or cloud computing\u2014which now outnumber traditional degrees. Short-term certification programs are growing faster than long-term degree programs: a market shift that higher education institutions did not choose, but to which they must adapt. <\/p>\n\n

The Manufacture Faces the Same Choice<\/h2>\n\n

The question Rice University asks about its admissions process is, almost word for word, the same one a watchmaking company should ask itself about the training of its artisans. Artificial intelligence already assists in the design of a case, simulates the polishing process, and suggests variations on a design even before human hands are involved. The time savings are real, and no one at a watchmaking house has any reason to forego them. But the foundation of a Master Watchmaker\u2019s or a Saddler\u2019s judgment does not lie in the speed of execution: it lies in the years spent making mistakes, starting over, and learning to recognize an error before it becomes visible. It is this ability\u2014to question a proposition rather than accept it\u2014that DesRoches safeguards in his reception room, and that writing safeguards, according to Palmer, in the very act of composing a sentence. <\/p>\n\n

No school today openly articulates the equivalent of Rice University\u2019s rule. Most leave the question open, having failed to ask it directly: which stage of a Companion\u2019s training must remain, by design and not by default, entirely manual\u2014not because a machine would do it any worse, but because that is precisely where the ability to judge what the machine proposes is developed. Without this explicit decision, the stewardship of a House\u2019s intangible heritage shifts gradually, driven by the pace of the fastest tool available, rather than at the pace chosen by the House itself. <\/p>\n\n

What Remains Unresolved<\/h2>\n\n

Rice University has made a decision regarding admissions; it has not made a decision regarding its overall educational approach, which it continues to open up to experimentation, course by course. A company that wishes to apply the same logic would have to accept the same discomfort: protecting a specific part of its training process while allowing the rest to evolve, without giving in to the temptation to freeze everything in place or delegate everything. The question that remains open is not whether artificial intelligence will accelerate the training of tomorrow\u2019s artisans\u2014it will. It is whether, in twenty years, these artisans will still be able to defend with their own hands a technique that a machine first suggested to them. <\/p>\n\n

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