4.4 Investigating LLMs

In this lesson, students interact directly with several LLMs to evaluate model performance and think critically about implications of model output for their work, their peers, and for society.

4.3 Prompting

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.

4.0 Intro to LLMs Lesson

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.

Career Exploration and Impacts of AI Lesson plan with print Worksheet and Google Form Exit Ticket

Career Exploration and Impacts of AI for post secondary training, 2 year and 4 year degrees. Emphasis on continuing training or traditional education in STEAM careers whether it be an apprenticeship, certificate program, 2 year college, 4 year university or beyond. Emphasis on AI impacts in all fields. Combined Inventory of Me, AI’s Impact, and Roadmap to My Dream Job into a WS (2-3 day portfolio-like). They fill it in along with slides as they go.

Best Quesadilla

I changed the food to be more culturally relevant to our school and our New Mexican culture. Instead of designing an algorithm for the best PB&J, this lesson tasks students with planning the best quesadilla. Our school population is majority Hispanic students who live in Albuquerque and mostly come from the South Valley. They will have more of a connection to quesadillas than to PB&J sandwiches. Optimizing a quesadilla algorithm for stakeholders may be more relevant to their culture and family.

Quesadilla Ethical Matrix

For this lesson I changed the food listed to quesadillas (building on the previous lesson, Algorithms as Opinions), I also added a formative assessment to better help students connect to stakeholders in their lives and communities. Students who participate in the previous lesson (Algorithms as Opinions) will be able to carry it forward into a real-world connection by thinking about their out-of-school activity, the relevant stakeholders and their values.

Best Bocadillo

Making the best sandwich instead of PB & J for those who have not experienced PB & J. The reason I made this change is because there is a large Hispanic population in my area who do not make sandwiches with peanut butter and jelly and so a sandwich in general is more relevant to students of diverse populations such as Latinx, Asian, etc.

Ethical Matrix Lesson for Food Deserts

High school (HS)Students: Use ‘Teen’ instead of ‘Child’ Added ‘Store’ instead of ‘Doctor’ Teachers can talk with their students about topics relevant to their lives like: a)workers in the store; b)food available in local stores; c)accessibility of local stores; d) impact on people, supplies, and accessibility of stores

Culturally Sensitive Introduction to Supervised Machine Learning - Is it Frankenstein?

In this lesson students are giving the option to replace webcam images of themselves with webcam images of printed depictions of Dracula and Frankenstein. The training and testing images have been selected to help with the lesson's introduction to algorithmic bias.

Decision Trees - Alien Gathering

Alien Gathering is a modification of the lesson plan 0.4 Decision Trees. In this lesson students begin with an activity in which a decision tree is made for a "Family" of aliens. This activity is intended to be used in one of two ways. It can be used as a substitute for the activity "Pastaland" or as an additional lesson activity used prior to the "Pastaland" activity.

How do GANS Work (modified with cats and dogs)

I changed the rooster fighting to be more appropriate (with cats and dogs). A lot of students don’t understand rooster fighting, and I believe it is illegal in the US. Many students know that dogs and can’t don’t get along. They can still grasp what a generator and a discriminator do. I also changed the artist to Da Vinci

Alternative to Spread of Misinformation (School Schedule)

Currently, school uniforms do not apply to all schools. This alternative lesson focuses on the school schedule while using candies and chocolates.

Impact of AI on STEM Jobs

This is a lesson with SLIDES + SCRIPT that combines Planting the Seeds of STEM & AI’s Impact on My Future Jobs. If your pressed for time these lessons can be combined to reduce time constraints.

3.5 Redesign Youtube Lesson

In this lesson, students will redesign the YouTube recommendation algorithm to meet their needs and reduce bias. This is a culminating project that can span several days of work and spark student reflection on lessons learned from the curriculum.

2.3 AI Generated Art Lesson

In this lesson, students will explore various forms of AI-generated art. They will engage in a conversation about what is art and who can make art.

2.1 What are GANs? Lesson

In this lesson, students will learn that GANs can generate art such as photographs, paintings, handwritten poetry, music, and jokes (that are kind of funny! Maybe.)

1.3 Classifying AI vs. Generating AI Lesson

In this lesson, students experience the process of generation and classification as they mix colors on an online platform and observe that they can create a varied palette of colors with a few input colors. Students will learn about examples of AI systems that perform classification and generation and practice distinguishing between generating and classifying AIs.

1.1 Introduction to Supervised Machine Learning Lesson

This lesson introduces students to how supervised machine learning can be trained to classify complex datasets based on labeled data. Students will train their teachable machine models and learn that AI can learn from labeled data. They will also revisit the idea that training AI systems with an increasing amount of data does not necessarily mitigate bias if there’s not enough diversity in the data.

0.5 Investigating Bias Lesson

This lesson shows students that unfairly trained AI systems can be far from objective and neutral. Students will recognize that AI systems can be unfairly trained and that there are several strategies that AI designers can use to mitigate biases in AIs.

An example resource