Practical training on AI mistakes
Most people can't tell the difference. That's how made-up facts, wrong numbers, and bad advice end up in emails, school papers, health decisions, and work with your name on it. Applied AI Education Group teaches everyday people and organizations how AI gets things wrong, how to spot it, and simple habits that catch it before it causes trouble.
For individuals, families, businesses, schools, and community organizations. Based on research into how these tools fail in everyday use, not in polished demos. Education and training only. Not an audit, certification, or compliance service.
Why this matters now
And it's showing up everywhere: in homework, in emails, in answers to health questions, and in work that goes out with someone's name on it.
The problem
These tools don't look anything up. They guess what words should come next, based on patterns in everything they were trained on. Ask about something common and the guess is usually right. Ask about something specific, recent, or local, like a medication, a law in your state, or a company's return policy, and the tool fills the gap anyway, in the same confident voice, with a fact or a source that doesn't exist. That's how they're built. It's not a bug that's about to be fixed.
Made-up answers get past people because they don't look wrong. Clean sentences and a tidy layout are exactly what we all use to decide whether something can be trusted. Those instincts are good at catching sloppy writing. They're not built to catch polished writing that's just plain false.
Waiting for a better version won't solve this. Better tools will be wrong less often, and more convincingly.
The core material
Most AI tips treat "making things up" as one problem that'll go away as the tools get better. It isn't one problem, and it won't just go away. Everyday use, whether you're checking a symptom, writing a cover letter, planning a budget, or drafting a report, turns up a handful of different mistakes, and they tend to feed off each other the longer you keep going.
Each one has its own warning signs. Sessions cover all seven, with live examples on the tools you already use.
Several of these come from how the tools work, not from a bug that more data will clean up. They've survived every new version and every fix the companies have released so far.
Smooth writing and a sensible layout are exactly what make us trust something. That's why being smart or experienced doesn't protect you on its own.
What you'll learn to do
Everyone's heard that AI makes things up. Catching it in your own email or report, when you're busy and it reads perfectly fine, is a skill. It takes practice, not a lecture.
You'll learn a five-step check that's quick enough to use when you're in a hurry, and specific enough to catch the three main ways AI answers go wrong. It fits on an index card.
Bring something you actually made with AI: an email, a letter, a school assignment, a work report. You'll check it yourself, during the session, and find the problems on your own instead of watching someone point them out on a slide.
For bigger or more important work, the routine covers three moments: before you ask the AI anything, while you're working with it, and once you've got a finished draft. No single check catches everything. The overlap is what makes it work.
This won't make you better at your job, and it isn't meant to. You already have what it takes to spot a wrong number, a fake source, or advice that doesn't add up. What's usually missing is knowing where to look, and having practiced before it really mattered. That's the part I teach.
No method catches everything. The goal is to make catching mistakes a habit instead of luck.
Sessions
Live, in person or online. Every session uses real AI answers, including yours, instead of slides about AI in general.
Your people bring real things they've made with AI. They learn the failure patterns, then go through their own work and find the problems themselves. They leave with a routine they can use the next morning and show a coworker.
The same material for anyone who uses AI at home, at school, or at work and wants to stop trusting answers they can't stand behind. No tech background needed. I won't tell you to stop using these tools, just where they break.
Who this is for
Parents, students, job seekers, retirees: anyone using AI to write a letter, look into a health question, plan a budget, or help with homework. You don't need to be technical.
Small business owners, office staff, real estate agents, consultants: anyone who drafts with AI and puts their name on the result. The examples are tailored to your field.
Owners and managers who want to know what their organization is exposed to when staff use AI, and what a realistic fix looks like.
Schools, libraries, nonprofits, health systems, and local government, where a wrong answer can reach the public, a patient, or a permanent record, and is hard to take back.
About
Applied AI Education Group is the teaching practice of Hector V. Ramos, PhD.
His research looks at how these tools fail when people use them for real things, not in demos. Some mistakes come up often enough to teach directly. Others don't, and no checklist would've caught them. That's why the sessions build judgment instead of handing out a list.
The teaching practice exists because the research points to something practical. The gap isn't rules or policy. It's skill. Most people using these tools have never been shown how they fail, and have never practiced catching it when they're short on time. That's what the sessions do.
Get in touch
Most sessions start with a short call about how you or your group actually use AI today, what's already gone wrong, and what a session should focus on.