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Lesson Overview
In this lesson, students interact directly with several LLMs to explore differences in model output and performance. Through a guided investigation of LLM responses to different prompts, students learn that LLM output is 1) a probabilistic (answers are different nearly every time) and 2) not based on ground truth. They also learn to build different prompts or prompt frameworks for different purposes.
NOTICES:
- Students <13 years old should not engage this lesson due age restrictions.
- This lesson introduces skills that benefit from the critical thinking activities introduced in the next lesson in the sequence, Lesson 4.4.
Total Lesson Time: 50 minutes
Learning Objectives:
- Prompts can be structured and designed to produce output that achieves different writing objectives, e.g., summarization vs. conversation.
- LLMs cannot produce a ground truth.
- Prompts can include prior LLM output to build memory.
Vocabulary Introduced: prompt
Pacing:
- Opening (5 min)
- Demo & Mini-Lesson: prompting frameworks (10 min)
- Iterative prompting journal activity (15 min)
- Discussion (10 min)
- Closing (10 min)
Planning Guide
Preparation Needed: 15-20 minutes
Prep Needed for Teaching In-Person:
- Communicate with school admin and families, well in advance of implementing this lesson, to let them know that you will be using LLMs in class, specifically models such as chatGPT and Gemini.
- Give students access to chatGPT, Gemini, and any other LLMs you would like them to explore.
- Give students access to the prompt frameworks folder for reference during independent work.
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Activity Usage
Copyright held by MIT STEP Lab
License: CC-BY-NC under Creative Commons
These materials are licensed as CC-BY-NC 4.0 International under creative commons. (For more information visit https://creativecommons.org/licenses/by-nc/4.0/). This license allows you to remix, tweak, and build upon these materials non-commercially as long as you include acknowledgement to the creators. Derivative works should include acknowledgement but do not have to be licensed as CC-BY-NC. People interested in using this work for for-profit commercial purposes should reach out to Irene Lee at ialee@mit.edu for information as to how to proceed. Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Attribution:
This unit was created by Katherine (Kate) Moore of MIT for the Everyday AI PD project, which created the Developing AI Literacy (DAILy) 2.0 curriculum.