build1 publisherOne report MIT's expert committee wants AI literacy in introductory courses now, at a school where only about a quarter of surveyed students felt prepared for AI. Its June 2026 report finds faculty-student trust breaking down and asks each course to set learning goals and assessments before it writes AI rules.
Reality
- Evidence40
- Adoption35
- Hype gap+20
- Incentives
- Insufficient
- Confidence45
The University of Phoenix study treated generative-AI use as the outcome and students' grades as a predictor, so it describes who picks up the tools. The authors say it cannot show what the tools do to a grade.
Reality
- Evidence57
- Adoption34
- Hype gap+16
- Incentives62
- Confidence51
The University of Virginia's dean of libraries argues that accuracy is only one test of an AI answer, and sorts replies into four types that each permit a different kind of checking. The demonstration is two Google searches.
Reality
- Evidence42
- Adoption20
- Hype gap+15
- Incentives45
- Confidence55
Golnaz Arastoopour Irgens and Sarah Burriss want students asking where an AI tool's data came from and who built it, inside lessons teachers already teach, and their paper offers the design without a classroom test.
Reality
- Evidence38
- Adoption12
- Hype gap+28
- Incentives72
- Confidence46
Stanford's education chapter measures how many American students are guessing at their school's AI rules. The written policy is the thing product teams have to design around.
Reality
- Evidence60
- Adoption78
- Hype gap+8
- Incentives35
- Confidence64
The Alberta Machine Intelligence Institute will develop and deploy Canada's National AI Literacy Initiative over the coming year, on programming modelled after courses it has offered publicly for the past decade.
Reality
- Evidence45
- Adoption20
- Hype gap+18
- Incentives70
- Confidence50
Priya Kumar of Penn State and high school student Khushi Kharuna surveyed 35 campers aged 11 to 15 at two library camps Kharuna designed and taught. The comparison the campers drew was with their school coding classes.
Reality
- Evidence45
- Adoption15
- Hype gap+20
- Incentives60
- Confidence55
The panel's argument is a measurement one: clicks and streaks track attention rather than learning, and generative AI widens the gap by improving a student's output while leaving the underlying skill where it was.
Reality
- Evidence44
- Adoption
- Insufficient
- Hype gap+16
- Incentives56
- Confidence51
Charleston County wrote its own AI policy because South Carolina has not published guidance, and phase two is training. The demos that teach students to distrust models expire as the models improve.
Reality
- Evidence54
- Adoption66
- Hype gap+18
- Incentives63
- Confidence57