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Lesson Overview
In this lesson, students ≥13 use LLMs (i.e., chatGPT, Gemini, etc.) (teachers demo for students <13) to engage in a series of investigations that reveal how different LLMs can output misleading and/or biased information. By exploring and discussing these limitations, learners realize that biased output can be the result of patterns from the data, “hard-coded” or programmed directives from the developers, or personal bias.
NOTICE: LLM output is unpredictable and can be inappropriate for users under 13. We do not recommend students <13 years old independently use LLMs. Instead, teachers can demo the investigation for these students.
Total Lesson Time: 45 minutes
Learning Objectives:
- LLMs output text based on patterns learned from data.
- LLMs can generate incorrect or nonsensical information.
- Issues of bias and fairness in training data can lead to biased or discriminatory outputs.
Vocabulary Introduced: Large Language Model (LLM), input, output
Pacing:
- Opening (5 min)
- LLM Investigation (20 min)
- Reflection & Discussion (10 min)
- Closing (10 min)
Planning Guide
Preparation Needed: 15-20 minutes
Prep Needed for Teaching In-Person:
- Post or print LLM Investigation questions for learners to reference for independent work
- Prepare to project, post, or print discussion questions for whole group to reference
- Reference materials to determine which LLMs are appropriate for the demonstrations
- Practice the demonstrations before implementation to ensure output is aligned with expectations
- If learners are permitted to access and interact with LLMs, provide access to the appropriate LLMs. If not, prepare to demonstrate the interactions so leaners can experience the exchange between user input prompts and generated output without interacting with the LLM themselves.
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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 [email protected] 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.