We’re living through one of the fastest shifts higher education has ever seen. New tools appear every month. New terminology floods every inbox. And here’s something worth knowing: Even the engineers building these systems will tell you that no one fully understands all of this.

That’s not a warning. It’s a relief.

Because if you’re reading this, you’re already doing exactly what matters most—staying curious, staying engaged, and refusing to let the noise drown out the signal.

For community and technical colleges, the stakes feel particularly high. These are institutions that sit closest to their communities, their local employers, and their workforce pipelines. They’re tasked with preparing students for jobs that are evolving in real time, while also keeping faculty confident, supported, and grounded in the midst of constant change.

There’s never been a moment with more buzzwords, more hype, or more pressure to figure it out.

So let’s slow things down.

The goal of this article isn’t to make you an expert. It’s to give you a foundation—enough clarity to ask good questions, have meaningful conversations with your teams, and feel confident exploring what AI might mean for your institution.

You don’t need a computer science degree to talk about AI. You just need a starting point.

Let’s build one together.

The Terms That Actually Matter

Walk into any conference session about AI and you’ll hear a dozen acronyms thrown around like everyone already knows what they mean. Most people don’t; they’re just nodding along.

Here are the core concepts worth understanding, explained in plain language.

Large Language Model (LLM). You’ve probably heard names like ChatGPT, Claude, or Gemini. These are all built on what’s called a large language model.

Think of an LLM like the most well-read person you’ve ever met—someone who has consumed millions of books, articles, websites, and documents. They can talk about almost anything with impressive fluency. But here’s the catch: They’ve only ever read about the world. They haven’t lived in it. They don’t have personal experience, intuition, or professional judgment the way a nurse, a welder, or a financial aid counselor does.

An LLM doesn’t think. It predicts. When you ask it a question, it’s essentially calculating: Based on everything I’ve seen, what words are most likely to come next?

That’s powerful for drafting, brainstorming, explaining, and reorganizing ideas. But it’s not a replacement for the expertise that lives in your faculty and staff.

Model Training. Before an AI can do anything useful, it has to be trained—and training is exactly what it sounds like.

Imagine running an automotive program where you could expose every student to every engine ever made, every repair manual ever written, and every diagnostic scenario ever documented, all before they ever touched a real vehicle. That’s essentially what happens during model training, except at Internet scale.

The AI processes massive amounts of text, learning patterns in how language works: how sentences are structured, how ideas connect, how questions typically get answered.

Here’s the important part: Once training ends, the model becomes static. It doesn’t keep learning from every conversation it has. When a student submits an essay to ChatGPT, that essay doesn’t get absorbed into the model’s memory and used to train future responses. The AI isn’t growing from your institution’s data unless it’s been specifically designed to do so—and the vast majority of tools educators use don’t work that way.

Tokens. AI doesn’t read language the way humans do. It breaks everything down into small chunks called tokens—usually a few characters or a short word. The phrase "community college" might become three or four tokens, depending on the system.

Why does this matter?

Because tokens are the currency of AI. They determine cost (most AI services charge by the token), speed (more tokens mean slower responses), and even environmental impact (processing tokens takes energy). When people talk about making AI more efficient, they’re often talking about using fewer tokens to accomplish the same task.

For practical purposes, just know this: Shorter, clearer prompts generally work better—and cost less—than long, rambling ones.

Context Window. This is one of the most important concepts to understand, and one of the least discussed.

A context window is the amount of information an AI can hold in its head during a single conversation. Think of it like a plate at a buffet: There’s only so much food you can fit on the plate at once. Once it’s full, you have to leave something behind to make room for something new.

Early AI models had tiny plates—maybe a few paragraphs of text. Newer models have much larger ones, capable of holding entire documents or even multiple files. But there’s always a limit.

Why does this matter for colleges?

Because if you’re asking an AI to help with a long document—say, a program review or a grant proposal—it might “forget” what was at the beginning by the time it reaches the end. Or if you’re building a tool for students that’s supposed to remember their progress across a semester, you’ll need to think carefully about how much information the AI can actually hold at once.

The context window defines what the AI can “see” in any given moment. Anything outside that window might as well not exist.

Hallucinations. This term sounds dramatic, and it kind of is.

A hallucination is when an AI confidently produces something that’s incorrect, invented, or completely made up—and presents it as fact.

Here’s why it happens. Remember, an LLM is a pattern-prediction machine. It’s not checking facts against a database. It’s generating text that sounds right based on patterns it learned during training. Sometimes those patterns lead to accurate information. Sometimes they lead to plausible-sounding nonsense.

Think of it like a student who studied hard for the wrong exam. They’ll still answer confidently, but their confidence doesn’t mean they’re correct.

For educators, the takeaway is straightforward: AI is incredibly useful for drafting, brainstorming, reorganizing, and explaining concepts. It’s far less reliable for factual recall unless you verify the output independently. Teach students the same principle. The goal isn’t to avoid AI—it’s to use it critically.

Agents. Most AI tools today are reactive. You ask a question, . . . you get a response. One input, one output.

Agents are different. An agent is an AI system that can take a goal and work through multiple steps on its own to accomplish it.

Imagine a student worker in your advising office who you could tell: “Pull the enrollment data for last fall, compare it to this fall, summarize the key differences, and draft a paragraph I can include in my board report.” A capable student worker would know how to break that into steps and execute them without you holding their hand through each one.

That’s what an agent does—except faster, and at scale.

For colleges, agents open up possibilities in advising workflows, institutional research, financial aid processing, and student support. Instead of manually running five reports and copying data between systems, an agent could handle the entire sequence based on a single request.

We’re still early in this space, but agents are where much of the real operational efficiency will come from in the next few years.

Custom Bots. A custom bot is simply an AI assistant that’s been tailored to a specific purpose.

Instead of using a general-purpose chatbot that tries to do everything, a custom bot is scoped to one job: answering questions about financial aid policies, helping students navigate the registration process, guiding faculty through a new LMS feature, or supporting a specific course.

The value here is consistency and focus. A well-built custom bot behaves predictably, uses the right institutional terminology, and stays in its lane. It’s not trying to be everything to everyone—it’s trying to do one thing well.

For colleges exploring AI adoption, custom bots are often the most practical starting point. They’re manageable, they’re measurable, and they don’t require faculty to rethink their entire pedagogy overnight.

Why This Matters for Community Colleges

Community colleges occupy a unique space in higher education. They serve students who are often balancing work, family, and school. They partner directly with local employers to build workforce pipelines. They launch new programs faster than most four-year institutions can revise a syllabus.

That agility is a strength, but it also means the pressure to keep up with AI feels more immediate.

Here’s the reality: AI is already reshaping how employers think about entry-level skills, how students expect to receive support, and how administrative work gets done. Ignoring it isn’t a viable strategy. But neither is panic.

The good news is that understanding the language is half the battle.

You don’t need to become a technologist. You don’t need to master every tool. You need to feel confident enough to ask questions, explore use cases, and have honest conversations with your teams about what’s worth trying and what’s not.

If you’re reading this article, you’ve already taken the most important step: leaning in instead of tuning out.

If your institution is thinking through how to make AI more accessible, more equitable, and more confidence-building for faculty and students—and you’re looking for someone to talk through what’s working at other colleges—contact Zach Kinzler, Director of Strategic Partnerships, Higher Education, BoodleBox.