Stanford HAI's brief on "world models" warns that simulation-style AI can produce visually convincing but functionally incorrect environments. For training and development teams, this promises cheaper high-fidelity practice but creates new evaluation responsibilities.
World models can simulate physical and social scenarios cheaply, but visual realism does not guarantee functional correctness.
A flawed simulation can fail silently at scale, producing consistent, widespread errors across every user.
Instructional designers and evaluators must add concrete measurement practices to verify simulation validity before adoption.
eLearning Industry We Built AI That Can Lie To Itself In Space And Time September 16, 2026. Show it a scene, and it'll tell you what happens if you misjudge a loadbearing weight on a bridge, brake too hard on wet asphalt, or turn the wrong valve in a chemical plant. New—It Just Got A Glow Up Anyone who's built a training program knows the plausibility trap already.
Will it be a solution to a business problem or just an expensive nothingburger? This is where Instructional Designers and program evaluators have more leverage than we usually give ourselves credit for. February 24, 2026 by Barbora Nevosádová Article AI-Assisted Instructional Design Without The Risk: A Practical QA Workflow That Prevents Hallucinations And Improves Learning March 16, 2026 by Boris Dzhingarov Article Data Analytics:
Plausibility Trap: AI Can Lie To Itself In Space And Time. Training and development pros should be worried... Summarise this page with your favorite AI assistant ChatGPT Perplexity Claude Grok (...And We're Kind Of Thrilled) Stanford's Institute for Human-Centered Artificial Intelligence (HAI) just dropped a brief on "world models"—AI systems that don't just predict the next word, they predict the next moment.
Real information in, invented information mixed in, one confident answer out. The interface never shows you how it got there. We built it to sound sure of everything. The best thing it can learn to say is “I’m not sure.” The latest argument my team and I are going back and forth on is whether we are being too honest wi
We are willingly trapped in a hybrid confirmation loop that takes us from "I think" to "I believe." Each prompting output makes the next one easier to believe in. There is a way out.
A groundbreaking study has measured the rates at which premium versions of four leading AI chatbots spread fictions from both the political left and right.
For six hours, it fixed real bugs without me watching. Two of its “fixes” genuinely scared me. Continue reading on Towards AI В»
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