AI is assisting teachers in the classroom by taking over repetitive tasks like grading and optimizing coursework, helping students with exam prep, and more
for so many reasons—would seem to be: JUST HIRE MORE TEACHERS! https://www.nytimes.com/... Amy J. Ko / @amyjko : This is a very utopian view of AI in education. Some is appropriate—there are big opportunities for some aspects of learning—but it ignores the way that software can amplify disparities, helping students with strong self-regulation skills, harming others. https://www.nytimes.com/... Hoon / @hoonparadise : Every few times a year a puff piece like comes out that promises how education will be “revolutionized” by AI and other such nonsense. This is one such article. https://www.nytimes.com/... Raju Narisetti / @raju : A lot of journalistic naivety when it comes to loving #AI. This is how platforms-love was in tech journalism, until recently. @nytimes: The Machines Are Learning, and So Are the Students https://www.nytimes.com/...?
Context & Ripple Effects
This late-2019 NYT piece framed AI as classroom relief — automated grading, optimized coursework, exam-prep help — and was immediately met with pushback from commentators like Amy J. Ko, who warned the software amplifies disparities by favoring students with strong self-regulation skills, and Hoon, who read it as another recurring 'education will be revolutionized' puff piece.
Six years on, the skeptics' argument has become the industry's problem to manage: adoption is now measured at scale across US schools and colleges as tech companies invest heavily, and vendors have moved to buy institutional legitimacy directly — [[a:891528|Microsoft, OpenAI, and Anthropic are funding American Federation of Teachers training hubs for 400,000 teachers]] — turning what critics dismissed as hype into a union-endorsed deployment channel.
First-order effects
- Teachers gain automation of repetitive work — grading, coursework optimization, exam prep — while the burden of judging when the tool helps versus harms falls on individual classrooms with no shared standard.
- Students with strong self-regulation capture most of the benefit, exactly the disparity mechanism Ko flagged: the same tool that accelerates one learner's exam prep leaves another without the scaffolding to check its output.
Second-order effects
- Vendors responded to credibility gaps not with better evidence but with distribution deals — the AFT partnership converts the teachers' unions from potential opponents of automation into paid adoption partners, pre-empting the resistance Hoon's critique represented.
- The disparity critique is forcing its way into product and policy: districts adopting these tools now face the question raised by coverage of AI short-circuiting learning processes — the real risk isn't cheating but eroding the skills needed to use generative AI adeptly — making pedagogy, not price, the procurement battleground.
Third-order effects
- If the vendor-funded, union-distributed model holds, AI in schools consolidates around a few foundation-model companies embedded in teacher training itself — a structural dependency where the firms selling the tools also certify the teachers on them.
- The recurring cycle Hoon identified — periodic utopian coverage followed by backlash — points toward regulation and curriculum standards becoming the durable arbiters, as schools already debating ready-made AI curricula decide what gets taught about the technology alongside teaching with it.
The trend: Education AI is shifting from press-release promises to institutionally brokered deployment, with vendors purchasing union partnerships and equity critiques hardening into the central condition of adoption.