AI in EdTech Weekly

New trials show that AI tutors only help when students actually use them, and that designs which hold back answers matter. Meanwhile, an audit finds every AI-in-education meta-analysis it examined to be flawed, and buyers in New York City, state agencies, and individual districts are tightening the rules on what gets into classrooms.

Teachers Pay Teachers will ban AI-generated resources, a teacher reports a district where AI writes most ELA test items, and a CZI-backed nonprofit offers open tools to check AI content against standards. Also: Andrew Ng on how chatbots can hurt learning, Reed Hastings on AI tutors, and how higher ed is putting AI to work.

New trials and pilots show the same pattern: AI improves learning when students use it as a tutor and hurts it when students let it do the work. Meanwhile OpenAI shipped GPT-6, removed a sharing tool many teachers relied on, and New York moved further toward screen limits.

EdReports found almost no evidence that AI features in curricula improve outcomes. Microsoft's survey shows guidance and training far behind adoption, and Sal Khan says his own proactive tutor still has to prove itself. Meanwhile districts, teachers, and students are setting their own terms for using AI.

Andrew Ng, Justin Reich, and Dan Meyer each describe the same pattern: AI use raises homework performance while weakening what students learn. Meanwhile, vendors are building guided study modes and shared evaluation infrastructure, and a federal review of E-Rate puts school connectivity on the table.

New school restrictions, Khan Academy’s proactive tutor, and district and higher-ed evidence point to a common shift: AI is being judged by preserved effort, visible thinking, and implementation capacity rather than novelty alone.

An independent two-year Tennessee trial found no measurable added math gain from Khanmigo access, while PISA’s global decline and new age-tiered policies sharpen the distinction between technology exposure and learning.

New York City’s age-tiered AI restrictions, MIT and Oxford assessment redesigns, and the launch of agentic tools all converge on a practical question: what can a learner still explain, transfer, and create without the system?

A teacher-led Gemini trial reports math gains while Tennessee evidence shows optional Khanmigo use is thin. As districts tighten procurement and educators redesign assessment, the dividing line is becoming guided practice, visible reasoning, and unaided transfer—not access alone.

This brief examines the shift from AI features to durable learning, as new federal edtech guidance arrives alongside direct-to-student rollouts, Alpha School’s expansion, and growing pressure on educators and institutions to prove what AI actually improves.