Lesson 1.2 - Natural Language Processing

 

Scenario

Think about the following scenario as you complete this lesson:

After exploring generative AI, you’re starting to experiment with AI-powered tools in your classroom. You’ve used AI to generate lesson plans and create practice quizzes, and you’re excited about the time it’s saving. However, you’ve noticed that sometimes AI doesn’t fully understand what you’re asking, leading to responses that don’t quite match your expectations.

A colleague tells you that understanding Natural Language Processing (NLP) is key to improving AI-generated responses. They explain that NLP is what enables AI to understand, process, and respond to human language. The better you understand how NLP works, the more effectively you can craft prompts to get useful, relevant results.

 
 

Your Objectives

By the end of this lesson, you should be able to do the following:

  • Define Natural Language Processing (NLP) and its role in AI.

  • Identify key NLP tasks that impact AI-generated responses.

  • Recognize how NLP influences the effectiveness of AI prompts.

  • Apply NLP concepts to refine prompts for improved accuracy and relevance.

 
 

What is NLP?

Definition

Natural Language Processing, or NLP, is the field of AI that enables computers to understand, interpret, and generate human language. Think of it as the technology that allows AI to read, write, and even have conversations with people in a way that feels natural.

 

The Role of NLP in AI

NLP is like a translator between humans and machines. It breaks down human language—words, sentences, and meaning—so AI can process and respond appropriately.  With NLP, AI can:

  • Understand text and speech

  • Translate languages 

  • Summarize information

  • Answer questions

  • Analyze emotions in text 

  • Summarize long articles 

  • Generate human-like text

In short, NLP is what makes AI capable of interacting with people in meaningful ways. Without it, computers would only understand rigid commands, not the flexible and expressive way humans communicate​.

Why NLP Matters for Educators

As an educator, understanding NLP empowers you to craft more effective prompts that guide AI to generate accurate, relevant, and meaningful content tailored to your teaching needs.

 
 

Key NLP Tasks

Now that you have a solid understanding of NLP and its role in AI, let’s explore some important NLP tasks to see how they process prompts.

  • Tokenization breaks down sentences into smaller units called tokens, which can be words, phrases, or punctuation marks. This helps AI understand and analyze text in a structured way.

    • Prompt: "Write a lesson plan on fractions."

    • Tokenization: "Write", "a", "lesson", "plan", “on", “fractions", "."

  • Stemming reduces words to their root form by chopping off suffixes, while lemmatization converts words to their base dictionary form. This ensures that variations of a word (e.g., "running," "ran") are treated as the same word.

    • Prompt: “Provide strategies for classroom management.”

    • Stemming: “management” > “manage”

    • Lemmatization: “strategies” > “strategy”

  • Stop words (e.g., "the," "is," "and") are common words that AI removes to focus on meaningful content. This helps streamline analysis without losing context.

    • Prompt: "What is the best way to introduce addition to first graders?"

    • Stop Word Removal: "is," "the," and "to" are removed 

  • POS tagging assigns grammatical categories (e.g., noun, verb, adjective) to each word, helping AI understand sentence structure and meaning.

    • Prompt: "Create a graphic organizer for comparing fiction and nonfiction texts.”

    • Part-of-Speech Tagging: “Create” (verb), “a” (article), “graphic” (adjective), “organizer” (noun), etc.

  • NER identifies proper names, locations, dates, and other specific entities in text. This helps AI recognize key topics and references.

    • Prompt: "Write a letter to parents about upcoming state testing for 3rd graders."

    • NER: “letter” (communication format), “parents” (audience), “state testing” (education policy/event), “3rd graders” (grade level)

  • Dependency parsing analyzes the grammatical structure of a sentence by identifying relationships between words. This helps AI determine subject-verb-object connections.

    • Prompt: "Simplify this passage for struggling readers."

    • Dependency Parsing: "Simplify" (main verb) governs "this passage" (direct object), and "for struggling readers" is a prepositional phrase specifying who the adaptation is for.

  • Coreference resolution determines when different words refer to the same entity in a text. This helps AI maintain context across sentences.

    • Prompt: "Create an anchor chart for main idea and details. Make sure it includes examples."

    • Coreference resolution: “It" refers to "anchor chart.”

  • Sentiment analysis detects whether a piece of text expresses a positive, negative, or neutral emotion. This helps AI gauge tone and attitude.

    • Prompt: "Parents are frustrated about their child’s reading progress. How can I communicate with them effectively?"

    • Sentiment Analysis: Detects "frustrated" as a negative emotion

  • Text classification categorizes text into predefined labels, such as topic-based grouping.

    • Prompt: "Give me a reading passage for 2nd graders about community helpers."

    • Text Classification: Categorizes the prompt as a request for a "reading passage" and identifies "2nd graders" and "community helpers" as key attributes

 

Having reviewed some of the ways that machines process language, you can see why precise prompt wording is so important. Mastering prompt engineering enables you to guide AI effectively, enhancing your teaching and improving the learning experience for your students.

 

Click the button below to complete a knowledge check:

 
 

Impact of NLP on Prompts

You've just explored a range of powerful NLP tasks—from tokenization and stemming to sentiment analysis—that form the backbone of effective text processing. Now, it's your turn to put those skills to work with a fun drag-and-drop activity. Get ready to decide whether AI prompts are weak or strong, and see how well you can apply your new NLP insights in real-world scenarios.

 

Click the button below to apply your learning:

 
 

Conclusion

You now see how a deep understanding of NLP can empower you to fine-tune your AI prompts and create more dynamic learning experiences in your classroom. Every element you’ve learned—from coreference resolution to sentiment analysis—shows you that the nuances of language are the key to unlocking AI’s full potential. In the next lesson, you'll get hands-on with prompt engineering using ChatGPT, taking your skills even further as you transform theory into practice.

 
 

Lesson 1.2 - Assessment

 
 

Discussion Question

Connect with the education community by posting your discussion question responses in the comment section below.

Share what you learned from revising your lesson prompt.  In what ways did it change your lesson plan?

 
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