Understanding the Engine: What Large Language Models (LLMs) Are—and Are Not

In our last few articles, we’ve discussed how AI can save teachers time and empower students. But have you ever wondered what's truly happening behind the scenes when a chatbot instantly generates a lesson plan or summarizes a dense article?

It's all powered by something called a Large Language Model (LLM).

Instead of getting lost in technical jargon, let’s use a simple analogy: Think of an LLM not as a brilliant human, but as the world’s most sophisticated predictive text engine.

Imagine the autocomplete on your phone, which suggests the next word you might type. Now, scale that up to a system that has "read" trillions of words from books, articles, and websites—the entire internet, in a sense. An LLM's core job is to predict the next most probable word in a sequence. It does this over and over, building sentences, paragraphs, and full articles, one word at a time.

This powerful capability allows it to do amazing things in education. But to use it effectively, we must understand its strengths and, more importantly, its critical limitations. 

What LLMs Are: A Superpower for Specific Tasks

LLMs are like a lightning-fast research assistant that never sleeps. They excel at tasks that involve identifying patterns and generating text based on a vast dataset.

A Master Summarizer: Give it a long research paper and ask for a summary. It can quickly find the key points because it has seen millions of summary patterns.

A Creative Brainstormer: Need ideas for a science project or a poem? It can pull from a huge database of human creativity to offer a starting point.

A Language Translator: It has learned patterns between languages and can translate text with remarkable fluency.

In the classroom, these capabilities are a superpower. They can unlock information, spark creativity, and help students work more efficiently.

What LLMs Are NOT: The Critical Limitations

Here's the most important part of this discussion. An LLM is not a reasoning being. It doesn’t "know" or "understand" in the human sense. It simply processes patterns. This leads to two critical limitations that every educator and student must understand:

1. The Hallucination Problem: It Can Confidently Make Things Up.

Because an LLM's goal is to predict the next most probable word, it can sometimes create plausible-sounding but completely false information. This is known as a "hallucination."

For example, if you ask it to cite a source for a fact it can't find in its training data, it might invent a book title, an author, and a publication date. The text will look perfectly real, but the information is fabricated. In education, this can be disastrous, leading to misinformation and compromised academic integrity.

2. The Bias Problem: It’s a Mirror, Not a Judge.

LLMs are trained on data created by humans, which means they learn all of our societal biases. If their training data includes stereotypes or outdated information, the AI will reflect and sometimes amplify them.

For instance, if the model is asked about certain professions, it might generate responses that lean on gender or cultural stereotypes simply because that’s what was most common in its training data. It doesn't have a moral compass; it just follows patterns.

The Educator's New Mandate: Teach Critical Literacy

Understanding these limitations changes everything. The goal isn't to ban LLMs, but to teach students to interact with them with a healthy dose of skepticism and critical thinking.

Think of it like using the internet in the early days. We had to teach students not to believe everything they read on a website. Now, we must teach them not to believe every word an AI generates.

Here’s how you can guide students:

• Fact-Check Everything: Always verify information from an LLM with trusted sources.

• Ask "Why?": Challenge the AI's output and ask for the reasoning behind its answer.

• Recognize Bias: Encourage students to evaluate the responses for stereotypes or a lack of diverse perspectives.

The true learning happens when a student uses an LLM as a starting point for inquiry, not an endpoint for a quest for an answer.

 

In a world filled with powerful AI, the most valuable skill is no longer just finding information—it's critically evaluating it. By demystifying the engine behind the tools, we empower our students to be informed and responsible users, not just passive consumers.

What steps are you taking in your school or university to teach students about AI's limitations? Share your strategies in the comments. 

Disclaimer: The views and opinions expressed in this article are solely those of the author and do not necessarily reflect the official policy or position of any educational institution, organization, or employer. This content is intended for informational and reflective purposes only.