How can AI help us become better writers?

Originally published on April 3, 2024 | Last modified on August 16, 2024

Overview

In this lesson, students compare AI and human-generated texts to analyze the affordances and limitations of large language models. They’ll score, guess origins, and discuss differences, then decide which aspects to adopt or avoid in their writing. The lesson culminates in a discussion of what aspects of the AI-generated writing students should emulate in their own writing and what aspects they should avoid.

AI & Writing

60 minutes

Digital Materials

Objectives

After this experience, students will be able to

Evaluate model texts using a rubric.
Hypothesize which texts are AI-generated using evidence to justify their position.
Identify the strengths and weaknesses of AI-generated texts in order to reflect on how they can improve their own writing.

Questions explored

How can we distinguish AI-generated text from human writing?
What are the strengths of AI-generated text? What are the weaknesses?
What elements are present in human writing that are not present in AI writing and vice versa?
How can we apply these learnings to our own writing?

Keyterms

Artificial Intelligence (AI)

The ability of computers to imitate human-like thinking, learning, and problem-solving.

Generative Artificial Intelligence (GAI, Gen AI)

AI that creates new content like text, images, music, and voice by itself, showing creativity.

Large Language Model (LLM)

A very big AI model that predicts many complicated sequences of text or code, using billions of data points. Some of these can produce text or code that looks more like what humans can do.

How does bias in AI affect the Global South?

This lesson explores the impact of biased data on AI systems with a focus on the Global South. Students will learn how biased data leads to biased results since AI reflects the data it is trained on. In particular, students will focus on the impact of this on countries that are generally less industrialized and have lower income levels than developed nations in the Global North.

How do computers see and understand visual information?

In this lesson, students explore AI “under the hood” by learning how sensory information is converted into data and used for mathematical operations that make an AI “intelligent”. Students will learn that visual data has features like brightness, and distance (like between parts of a face) and is used in applications like facial recognition. Along the way, students will be able to connect these technical processes to social consequences such as bias, and learn to treat AI with skepticism.