Creative Force Dispatch
Mathematics in the Age of AI
Terence Tao — essay for the ICM 2026 Proceedings, posted August 17, 2026 (12 pages)
Tao does something unusual in this essay: he refuses the argument everyone else is having. Rather than debating whether AI will ever reach research-level mathematics, he simply assumes that it will, and then turns to the question that assumption makes urgent — what are the goals and values of mathematical research actually? What is it that mathematicians are doing when they do mathematics?
Using problem-solving as his lens, he works through what would remain if the answers themselves became cheap. The essay is short, readable, and written for a general mathematical audience rather than a specialist one. It is the rare piece on AI and mathematics that spends almost no time on capability claims and almost all of it on purpose. For anyone who teaches, the framing lands close to home. The question Tao is asking about his discipline is the question a mathematics teacher has always had to answer for a student holding a calculator, a solutions manual, or now a chatbot: if the answer is available, what are we here for?
CREATIVE FORCE: This is the clearest statement yet that mathematics was never primarily a machine for producing answers. When a Fields Medalist grants the machines the answers and finds the discipline still standing, what he is pointing at is the creative act — the framing of a problem worth solving, the taste that distinguishes an interesting question from a merely hard one, the sense of what a proof is for beyond its truth value.
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Report of MIT’s Ad Hoc Committee on AI Use in Teaching, Learning, and Assessment
Massachusetts Institute of Technology — institutional research report, released August 13, 2026; widely covered August 25–28
MIT convened a committee to look honestly at what AI has done to undergraduate teaching, and the committee came back with a finding the institution could not manage around: AI can now credibly complete most undergraduate assignments. From there the report catalogs the damage — faculty who cannot tell what students have actually learned, students who are more isolated than before, and a corrosion of trust running in both directions. One line has been quoted everywhere since publication: “Such an underground river of mutual suspicion is no foundation for a healthy classroom.”
What makes the report notable is that the recommendations are structural rather than defensive. Instead of tightening detection and proctoring, the committee proposes reconsidering the role of grading itself — exploring mastery-based and competency-based alternatives; deliberately rebuilding in-person social learning through collaborative classroom work and tech-free time together; and replacing a single institution-wide AI policy with adaptable frameworks that departments tailor to their own disciplines. The committee frames all of this as an opportunity for overdue transformation rather than a crisis to be contained. Coming from MIT, that framing carries weight well beyond Cambridge, and K–12 leaders will find the logic transfers with very little translation.
CREATIVE FORCE: Read against Tao, this report is the institutional half of the same argument. Tao asks what mathematics is for; MIT asks what conditions a place of learning must create once answers are no longer the currency. Their answer — trust, shared work, room to be visibly wrong in front of other people — is a precise description of the conditions creative mathematical thinking has always required, arrived at by a committee that was not setting out to talk about creativity at all.
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The Next Phase of AI in Education Is About Stewardship
Julia Fallon — guest opinion column, EdTech Digest, August 25, 2026
Fallon’s argument is that education leaders have been asking the wrong question. “What can AI do?” has run its course; the harder and more useful question is what responsible stewardship of these tools looks like inside a real institution with real students. She offers four questions to put to any AI initiative before it is embedded in practice: What problem are we solving? What evidence will we collect? What guardrails and governance exist? Is learning or workload actually improving? None of them is exotic, and that is the point — they are the ordinary discipline of institutional judgment, applied to a domain where the pace of change has made that discipline feel optional.
She is candid about why it feels optional. “The speed of AI creates enormous pressure to have answers,” she writes, and that pressure is precisely what pushes leaders to measure success by adoption metrics instead of by whether anything got better for students or teachers. It is a short piece, and the most immediately actionable of the three.
CREATIVE FORCE: Her first question — what problem are we solving? — is problem posing, and problem posing is where mathematical creativity begins. A leader who cannot name the problem an AI tool addresses is in the same position as a student who has been handed a procedure without a question, and the outcome is the same in both cases: fluent activity that produces nothing anyone needed.
For practitioners, this is the piece that converts the week’s bigger arguments into something you can use on this week. The four questions work as an agenda item, a screening rubric for a purchasing decision, or a faculty meeting protocol. Treating technology adoption as a craft judgment rather than a checklist is the same move we ask of teachers with curriculum — and it is worth noticing that stewardship, like teaching, is a practice you get better at by doing it deliberately.
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