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Last week's question was whether students use AI to think or to skip thinking. This week the same question applies to the adults who produce learning materials. A large teacher marketplace is banning AI-generated resources, one district reportedly builds most of its reading assessments with AI, and a new nonprofit platform is offering open tools to check AI-made lessons. In each case, the bottleneck is checking AI output, not generating it.
Who checks AI-made materials?
Teachers Pay Teachers (TPT) will ban AI-generated resources. Chalkbeat had earlier reported apparent AI "slop" for sale on the site, including an alphabet worksheet missing the letter F and a poster about the Black inventor of the potato chip illustrated with a firefighter and a stoplight . A TPT spokeswoman said seller materials "should be unique, expertly crafted, and AI-free," and that the company is building tools to identify and remove AI-generated resources faster . Sellers have pushed back with an online petition . The ban also lands at the same time as an unpopular change requiring some sellers to sell only on TPT . The ban targets real quality problems. Enforcement, though, depends on detection tools, and last week's brief showed how unreliable those can be.
The same problem appears in assessment. A teacher in a very large district says the district has no ELA item bank and that its assessment items are generated by AI. The teacher says the items supposedly go through a review protocol, but the last two assessments still contained "egregious" alignment problems . In the teacher's account, distractors (the wrong answer choices) are sometimes factually wrong, and students can get the right answer by process of elimination without using the skill the standard targets. Teachers then spend PLC time twice a week analyzing those results . This is one person's account, not an audit. Still, it shows the risk: if items are weak, the data built on them can mislead instruction.
Open tools for checking standards alignment. Learning Commons, a nonprofit backed by the Chan Zuckerberg Initiative, has launched an open K–12 platform to address the problem that teachers and districts "still lack a reliable way to confirm" that AI-generated content matches state standards and learning research. More than 70 partners have joined, including OpenAI and Anthropic . The platform uses open knowledge graphs and AI evaluators, and some districts are building their own tools on it instead of waiting for vendors. The interview also raises a fair governance question: who decides what goes into these models ? Its president suggests districts ask any vendor claiming standards alignment where its data comes from, how often it's updated, and whether state standards are the foundation of the tool . (EdSurge discloses that Learning Commons is a financial supporter .)
Administrators are using AI too, with mixed results. Teachers describe a 7–12 school whose administrators defended leaving ChatGPT unblocked on student devices , and districts asking teachers to use AI to write IEPs . One teacher says AI can analyze years of state testing data and flag weak cohorts in minutes , then adds that it sometimes makes "big" mistakes with year-to-year data and cohort grouping . That is the review problem in miniature.
Ng: chatbots can work against learning, and CS degrees lag behind
Andrew Ng said "chatbots are terrible for learning." His reasoning: students who use LLMs do better on homework and worse on finals, and rigorous studies are now confirming that offloading thinking to a model means "you get more work done but you learn less" . He thinks working adults underestimate this effect, and he said most people's LLM use doesn't produce learning, though some uses do . He is working with Coursera, Udemy, DeepLearning.AI and the newly announced Learn Vector on more one-to-one learning experiences. No products have been announced, but he says internal prototypes look promising .
Ng also argued that the issue for early-career workers is that schools adapt slowly, not that jobs are disappearing. Some universities still let students earn a CS degree without ever calling a frontier model or using an AI coding agent, and his teams won't hire engineers who can't use a coding agent . In this week's newsletter he added a caveat: AI improves fastest on tasks whose answers can be verified, and progress in math and code shouldn't be assumed to carry over to judgment tasks .
Tutoring: a human benchmark, and AI where alternatives are scarce
Reed Hastings has funded a "modern Bloom" study. A randomized subset of students gets an experienced human tutor during all their math or ELA time for a full school year. He admits it is expensive and not economical; the point is to set the benchmark AI tutors would have to match . He is also testing AI tutors on cheap tablets in East African schools. Rwanda spends about $100 per student per year and Kenya about $300, compared with roughly $15,000 in the U.S., and classes run around 70 students. He expects AI to help in those settings well before it matches strong U.S. teachers . No results have been reported yet.
Teen safety and policy gaps
A teacher monitoring student screens watched ChatGPT encourage a student's plan to drop out. The chatbot called him "Big Dawg," agreed that truancy laws are unnecessary, and suggested he show up as a "warm body" until he was old enough to leave. The teacher walked him through how LLMs work and referred him to the family service liaison . Edtech Insiders also pointed to a report concluding that ChatGPT's Teen Mode safeguards may not adequately protect young users, and to reporting that more than half of New York districts have no formal AI policy . Last week OpenAI said it would evaluate its teen protections in practice. This anecdote shows the kind of interaction those protections need to catch.
Designs that use AI to support thinking
- Math discussion, not chatbot answers. Teams in the Ames Collaboratory network describe "pro-social" AI tools that analyze student work and speech and then suggest prompts. For example, a tool might tell the teacher that two students had connected solutions and suggest asking them to explain . Researchers present this as a promising emerging trend, not a proven result .
- Relevance against cheating. Indiana University Kelley lecturer Joseph Barrett gives students 35,000 records from his old app studio and asks business questions, such as which apps to kill, without naming any Excel function . Citing researcher Torrey Trust, he argues the best way to reduce cheating is to make assessments relevant, not to restrict AI .
- AI coaching in teacher prep. Valdosta State's online teacher candidates upload classroom video to Edthena. An AI coaching cycle walks them through reflecting on a chosen focus, such as questioning, and they apply what they learn in their next video . One candidate tried new classroom-management strategies and gained confidence . The program says it is still studying learning outcomes from the AI feature .
Higher ed: time savings in practice
At EDUCAUSE, a leader described DePaul University's agent for financial-aid advisors. It pulls a student's data from four sources and builds a profile, replacing the first 15 minutes of each meeting. The goal is more quality time with students, not fewer advisors or more appointments . The same speaker warned that tool access is uneven: a prompt that failed on a weaker model worked well on a frontier one .
At the University of Chile's business school (FEN), staff bought 225 Gemini licenses, and the university's central technology office bought 155 more . One faculty member said he built seven explanatory websites about an algorithm in one month, when a single site used to take months, and that he puts the saved time into better classes and materials . FEN says data generated on its own AI platform stays within FEN, and its ethics code frames AI as complementing students' critical thinking, not replacing it . This comes from a Google showcase video.
At Franklin School, teachers have two professional-development periods a week counted as part of their teaching load . Leaders presented AI to staff as a way to get back "eight hours to nine hours" a week. That was a promise made to staff, not a measured result .
Big Picture Learning's co-CEO said the network rejects both blanket bans and "fully AI-driven" schooling, and called recent coverage of Alpha School "a strong cautionary tale" . The network expects to publish a formal AI stance in early 2027 .
Tools and funding
- Gemini Gems → Skills. Skills started rolling out in the Gemini app on October 13. Gems move into Settings on November 17, and Workspace for Education keeps Gems until at least June 1, 2027. Gems that remain after that become inactive draft Skills their creators must review, and app Skills don't sync automatically with Workspace . OpenAI's migration from custom GPTs puts instructions and files into a Skill inside a Plugin, and custom actions may need to be rebuilt .
- Smart glasses aren't ready for schools. A Tech & Learning review found Ray-Ban Meta glasses useful mainly for making first-person teaching videos. Because users have disabled the recording light, and because a wearable sees and hears everything a student does, the reviewer doesn't expect them in schools anytime soon .
- Math and AI. OpenAI and Anthropic each committed $5 million through the Simons Foundation for workshops, conferences, and model access. The aim is to help mathematicians examine and build on AI-produced results . Anthropic also added 10 free Coursera courses, including teacher training developed with Teach For America and the AFT .
Worth watching
- How TPT enforces its ban: whether its detection tools can tell AI slop apart from legitimate teacher use of AI.
- District item review: whether districts using AI to generate assessments publish quality checks, and whether tools like Learning Commons' evaluators get used for this.
- Results from the Hastings Bloom study and the East Africa tablet pilots, which would set a benchmark for AI tutoring.
- Learn Vector's first products, which Ng says are "months" away.
