Episode 3: What AI Actually Is - And Why That Understanding Changes Everything

Part 1 Understanding AI in the Classroom 

AI arrived in schools overnight, and teachers were never given a roadmap. In this episode, Dr. Brandon Naylor breaks down what AI actually is, how tools like ChatGPT, Claude, Gemini, and Copilot generate language, why they sometimes sound confident while being wrong, and what that means for real classrooms. Through stories from teachers across the U.S. and around the world, he reveals the universal mix of hope, caution, and responsibility educators feel when using AI for the first time. You’ll learn six practical habits for evaluating AI‑generated content, understand why teacher judgment is still the strongest safeguard, and walk away reassured that you’re not behind and you’re not expected to master a constantly evolving technology overnight. This episode gives educators and administrators clarity, confidence, and a grounded path forward in a moment that has felt anything but slow.

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You're listening to The Educators Beacon, a podcast for educators who want honest, practical conversations about the tools, challenges, and ideas shaping teaching today. No hype, no jargon, just real talk for the people who show up for students every single day. I'm Dr. Brandon Naylor, and in today's episode, uh what AI actually is and why that understanding changes everything. This is the first of a two-part conversation with me. Artificial intelligence didn't arrive in schools with a training plan or a slow rollout. It arrived all at once, in inboxes, in student essays, in staff meetings, and the quiet pressure to keep up. Today I'm going back to the very beginning of that story to understand what this technology actually is, how it actually works, and why understanding that changes how you use it. I'm not here to sell you on AI, and I'm not here to scare you away from it. I'm here to give you some additional clarity, the kind I know teachers were never given when these tools first showed up. Thank you for being here. Uh, so this is episode 3, part one of 2, and I want to tell you why I split this conversation in half. When I sat down to talk about AI in schools, I realized I was trying to cover two very different things in one breath. What this technology actually is and what it has felt like to live through its arrival. Those deserve separate room to breathe. Today we're doing the first one. We're going to get grounded in what AI actually is, how it works, and where it came from in your school year. No mysticism, no jargon, just clarity. If you've ever typed something into Chat GPT or Claude or Gemini and thought, I don't actually know what just happened and I don't know if I can trust it, this episode is for you. And if you've already got a decent handle on the mechanics, stay with me anyway because I'm going to tell you some stories from teachers around the world that I think will surprise you. And I'm gonna give you some specific things you can try this week. Let's get into it. Education historically moves at the speed of a glacier. Policies take years, curriculum cycles stretch across decades. Anything new usually goes through committees, pilots, revisions, long arcs of professional development. That's just how our field works. And honestly, there are good reasons for that pace. We're dealing with children's learning, and you don't want to move fast and break things when the things in question are kids. AI didn't follow that pattern. AI has been more like a lightning strike, illuminating everything at once, reshaping expectations overnight, and leaving teachers to navigate the brightness without a map. Here are the numbers, because I think it helps to know we're all navigating this together. A national survey of more than 2,200 US teachers conducted by the Walton Family Foundation and Gallup in 2025 found that roughly 60% of K through 12 teachers now use AI tools in their professional practice. That number nearly doubled in a single year. One year, in a field where change usually unfolds across a decade, that is an extraordinary, almost unprecedented rate of adoption. And the optimism behind that growth makes sense. The same research estimates that teachers who use AI regularly could save the equivalent of six weeks a year by offloading tasks like lesson drafting, material creation, and administrative writing. If workload is your single biggest professional challenge, and for most teachers it is, six weeks back sounds like a lifeline. But here's what I need you to sit with. A separate large-scale survey by the Royal Society of Chemistry in 2024 found that while 44% of teachers had tried AI tools, only 3%, 3% said those tools had significantly reduced their workload. Six weeks promised, 3% delivered. That gap is not a fluke, it's a signal. And the signal isn't that the tools don't work. The signal is that teachers have not been given the time, training, or structural support to make them work. AI is not plug and play. It requires judgment, revision, oversight, alignment with your curriculum, and your context. It requires time to learn and time to experiment, and time is the one resource teachers have never had in abundance. This is not a technology problem, it's a support problem. Okay, so let's actually define the thing. Because I found that once teachers understand what's happening under the hood, a lot of the mystery and a lot of the anxiety starts to fall away. Artificial intelligence is not magic, it's mathematics. At its core, AI refers to computer systems designed to perform tasks that typically require human intelligence, understanding language, recognizing patterns, making decisions, generating content, uh modern generative AI tools, the ones most relevant to your classroom, work by processing enormous amounts of text and learning statistical patterns within that data. So whenever you're typing a question or a prompt into ChatGPT or Claude or Gemini or Copilot, here's what's actually happening. The system is predicting word by word the most statistically likely helpful response based on billions of examples it has processed. It does not think the way you think, it does not know facts the way a textbook knows facts. It generates probable language. Researchers at Stanford University studied this directly and found that large language modules can produce fabricated or incorrect information with high confidence, a pattern now widely known as hallucination. One study found that these tools produced fabricated scientific details in roughly 17% of responses involving specialized content. So here's the single most important mental model I can give you. AI is fluent, but fluency is not the same as truth. Speed is not the same as accuracy. That polished, confident paragraph the tool just handed you deserves exactly the same professional scrutiny you'd give any instructional material. Probably more, given what we now know. UNESCO has been direct about this. AI systems reflect the biases and limitations of the data they were trained on. Left unchecked, AI systems reproduce existing patterns of inequality. AI is not natural, it carries the imprint of the world that created it. Your judgment, your read on your students, your community, your content isn't a nice to have on top of AI. It's the whole safeguard. See, for years my focus was instructional support, equitable education, and teacher well-being. The daily realities of school systems stretched thin. AI at first felt distant to me, something that belonged to engineers and futurists. That changed the moment. A teacher in Colorado asked if she could show me something during a break at a conference. She opened her laptop, typed a single sentence in a tool that I had barely heard of at the time, and within seconds the screen filled with a fully different uh differentiated reading passage, multiple lexile levels, uh vocabulary supports, comprehension questions, a writing prompt, all of it. She stared at the screen with a mix of awe and fear, like she was witnessing a breakthrough and a warning at the same time. She told me the tool had just done in 10 seconds what would have taken her two hours. And then she looked up at me and asked the question that has shaped every conversation I've had since. How am I supposed to know whether any of it's correct? That question stayed with me because what I heard in it wasn't fear of the technology, it was responsibility. She wasn't afraid of AI generating text. She was afraid of misunderstanding it, of trusting an output she couldn't fully verify, of making a decision that might ripple outward into a child's learning. That hesitation wasn't resistance, it was care. And I want to name that clearly for anyone listening who has felt the same unease. That instinct to pause and question is not a weakness, it's exactly the professional judgment that protects your students. The Colorado conversation is the one that grabbed my attention and I started ruminating on it, but it's not the one that convinced me this phenomenon was universal. For that, I had to go looking. And I want to walk you through exactly what looking involved because I think it matters for how much weight you put on what I'm about to tell you. In the months that followed, I conducted dozens of informal interviews with teachers, administrators, and instructional leaders, and every single one of those interviews and international conferences took place on Zoom. I spoke with teachers from Michigan, Ohio, Arizona, Georgia, North Carolina, Oregon, New York, Massachusetts, Florida, Montana, and Illinois. And I spoke with teachers from Australia, South Africa, Singapore, Canada, India, Korea, Spain, Kenya, the Netherlands, Switzerland, and the United Kingdom, all through Zoom calls that stretched late into the evening and at times early morning, depending on the time zone. Some teachers joined from their classrooms after dismissal, others joined from their kitchen tables and with their own kids doing homework in the background. Some joined from their cars, parked outside a grocery store because that was the only quiet place they could find. These weren't professional, academically charged, polished interviews. They were moments of honesty carved out of exhaustion. And across all of it, Michigan to Johannesburg, Arizona to Singapore, Ohio to Manchester, Montana to Seoul, Florida to Sydney, etc., one pattern emerged with absolute clarity. No matter where a teacher lived, no matter what the grade they taught, no matter how many years of experience they carried, their first encounter with AI felt exactly the same. A seventh grade English teacher from Michigan, I'll call her Miss Linton, told me that the first time she asked an AI to generate differentiated reading passages, she felt she'd gain a new instructional partner. But Miss Linton also felt uneasy, wondering how the tool produced such fluent text so quickly. That's not a contradiction. That's the accurate response. Intrigued, hopeful, and cautious all at once. I want to say something directly to Miss Linton and to everyone who has felt what she felt. The fact that you pause to wonder how the tool produced the text is evidence of your expertise. In fact, I heard the same instinct from a high school math teacher from London, I'll call him Mr. Harrow, who told me his habit of double-checking every AI-generated solution before handling it to students wasn't extra work. It was mathematical care. And I also heard it from a teacher in South Africa, Miss Diamani, who verifies every Isizulu term in AI tool generates by hand because the tool, in her words, is helpful, but it isn't fluent in her students' realities. Alright, let's get practical, because be thoughtful about AI only helps if you know what thoughtful actually looks like. Here are the specific practices I offer teachers, and I want to be clear up front. These are offered gently as suggestions, not requirements. If they bring you clarity or ease, use them. If something doesn't fit your context, trust your own judgment. You know your students better than any tool ever will. First, the slow read. After AI generates a passage, a problem set, or a translation, read it slowly, not to catch every flaw, but to notice where the text feels slightly off. Teachers constantly tell me that when they slow down, they can feel the wobble in the language long before they can name the actual error. That instinct isn't accidental. It's the product of years of expertise and it deserves to be trusted. Second, ask the AI to justify itself. A prompt like, explain why you chose this example, or show your reasoning for this solution, often reveals whether the model is following sound logic or drifting into approximation or hallucination. Teachers who try this tell me the explanation reveals more than the original output did. Third, ask for multiple versions of the same thing. When AI produces several different outputs for the same prompt, the differences reveal exactly where the model's reasoning is unstable and exactly where your expertise needs to step in. One literature teacher from Montana told me that comparing three AI-generated interpretations of a poem made her realize how much nuance the model missed, and how much depth she brought to the text without even realizing it. It reminded me, she said, that I still know how to read and still know how to judge what's right. Fourth, ask the AI to outline the conceptual sequence first before generating the full explanation. That means you say something like, before you explain this, list the steps you're going to follow, or give me the conceptual outline first, or show me the sequence of ideas you'll use. The AI then gives you a sample skeleton like to find the concept, identify the key components, explain how they interact, give an example, summarize the implications. Once you see that outline, you can quickly check does the sequence make sense? Is anything missing? Is the logic correct? Is the order appropriate for your students? If the outline looks good and checks out, then you can ask it to generate the full explanation. Fifth, read AI-generated text aloud instead of only reading it silently. I tell you, this one surprises people. And here's the thing, when you hear language out loud, your brain's actually doing something different than when you're just reading silently. It fires up different monitoring pathways so you end up catching stuff you'd normally miss, inconsistencies, weird gaps, little shifts in logic that your eyes would just slide right past on the page. And sixth, ask the AI to cite its sources as a diagnostic. So here's a little fun diagnostic trick for you. Basically, ask the AI to cite its sources. Now I'm not saying those citations are going to be reliable. Honestly, a lot of the time they won't be. But that's not really the point. The point is the attempt itself tells you something because fabricated citations, they don't travel alone. They usually come bundled with other fabricated details. So if the sourcing starts falling apart on you, take that as your cue. Slow down because the content probably needs a second look too. And here's my last piece of advice. Start with one small low-stakes task. And honestly, this might be the most important one. Start small, okay? Real small. Not a whole unit, not your whole curriculum. Just one warm-up question. One template, one rubric, that's it. I talked to a teacher in Manchester who told me she started by using AI to generate just a single warm-up question. Nothing more than that. And she said it made her feel more grounded instead of intimidated, you know? That one tiny step ended up being her doorway into everything else. Also, giving the AI a small task can give you a sense of how it operates first. So please give yourself permission here, okay? You don't have to master this overnight. Nobody does. Not a single one of us needs to. So remember the six steps. One, the slow read. Two, ask AI to justify itself. Three, generate multiple versions. Four, ask AI to outline conceptual sequence. Five, don't let it lead it, read it. Read AI-generated text aloud. Use AI as a thinking partner, not a source of truth. And finally, six, if the sources look fake, the answer's at stake. Ask AI to cite its sources and check them. I'd like to address any administrators listening. Recognize that adoption speed has far outpaced training and policy. So plan support accordingly. So let's be honest about the pace here. Roughly 60% of teachers are now using AI in some form, and that number nearly doubled in a single year. That's a remarkable rate of adoption. And it has significantly outpaced training and policy. If your support structures haven't caught up yet, you're not behind, you're just on time to catch up. Plan accordingly, plan generously. Build in dedicated time for teachers to learn and experiment with AI tools, rather than expecting mastery to happen in teachers' own unpaid hours. Please build in real protected time for teachers to learn and experiment with these tools. This can't keep living in evenings and weekends squeezed into teachers' own unpaid hours. If we want thoughtful, confident use of AI in classroom, we have to make room for it during the workday, not after it. Treat it with the same reverence you give professional development sessions or workshops. Treat teacher hesitation about AI as professional caution, not resistance to innovation. It is often the clearest signal that a teacher is thinking carefully about student impact. When a teacher expresses hesitation about AI, resist the urge to read that as resistance to innovation. More often than not, it's the opposite. It's professional caution. And it's usually the clearest sign that you have a teacher thinking hard about what's actually best for their students. That instinct deserves your support, not your suspicion. Model the same evaluative habits you want teachers to use. Question AI outputs, verify claims, and don't treat fluency as a proof of accuracy. And finally, model the exact habits you're asking teachers to build. Question AI outputs, verify claims before acting on them. Remember that fluency is not the same thing as accuracy. A confident sounding answer is not automatically a correct one. If leadership visibly practices this kind of scrutiny, it gives everyone else permission to do the same. I know how hard you already work to support your teachers. This isn't meant to add to that load. It's meant to lighten it. These four points really come down to three things time, trust, and a good example to follow. Give your teachers that, and this whole transition gets easier for them and for you. You're doing more good than you probably realize. So let's bring this together. AI is not magic. It's a fluent pattern predictor, genuinely useful, generally capable of saving you time, and generally capable of sounding confident while peing wrong. The teachers I've spoken with from Colorado to Michigan to South Africa to Switzerland were never afraid of the technology itself. They were afraid of misunderstanding it or using it in a way that might even unintentionally let a student down. In our next conversation, I'm going to go somewhere deeper into the emotional weight this transition has placed on teachers. But for now, here's what I want you to carry with you. You are not behind. You are not expected to know everything about a technology that reshaped itself three times since you started listening to this episode. AI may change the pace of certain tasks, it cannot and will not change the heart of teaching. Thank you for showing up for your students, and thank you for spending this time with me. This is Dr. Brandon Naylor. I'll see you soon.