| Author(s): | Chase Harris |
|---|---|
| School(s): | Gateway Regional High School |
| Contact Info: | [email protected] |
Basic Information
| Grade Level(s): | 9-12 |
|---|---|
| Content Area(s): | Science |
| Suggested Length of Lesson: | 1 class, 60 minutes |
Lesson Overview
In this lesson, students will learn how to analyze complex datasets using SchoolAI. They will explore how natural language processing (NLP) tools can support data interpretation, pattern recognition, and real-world decision-making. This lesson bridges core data literacy skills with emerging AI literacy.
Lesson Frame
NJSLS
Math:
NJSLS.MATH.HSS.ID.A.1: Represent data with plots on the real number line (dot plots, histograms, and box plots).
Science:
Computer Science:
Career Readiness:
AI Integration
Intended Outcome of AI Use:
Students will use AI tools to help interpret data by summarizing trends, asking clarifying questions, and generating hypotheses. The AI acts as a data literacy support partner, enabling students to approach complex datasets with greater confidence and depth.
AI Literacy or Skill Developed:
Prompt engineering: how to ask AI effective questions about data
Interpreting AI-generated summaries and checking for accuracy
Synthesizing AI feedback with critical thinking
ISTE Standards: 1.3 Knowledge Constructor, 2.5 Designer, 2.6 Facilitator
Materials & Tools:
AI Tool(s) Used: School AI
Materials or Tech Needed
Sample datasets (CSV or embedded in Google Sheets)
Graphic organizer for prompt/reflection writing
Google Slides or Canva (optional for visual presentation)
Instructional Sequence:
Opening / Activation of Prior Knowledge
Warm-up (5 minutes): “What is AI, and how do you think it can help us understand data?”
Quick review of basic data types, graphs, and trends (histogram, scatterplot, etc.)
Mini-lesson / Direct Instruction (10 minutes)
Teacher demo: Use an AI chatbot to ask questions about a sample dataset.
Example: “What trends do you notice in this data?” → SchooAI responds → Class critiques AI’s answer.
Explain how to craft better prompts (prompt engineering 101).
Learning Activity (40-45 minutes)
Students receive a dataset (e.g., climate change, school lunch choices, TikTok trends, teacher will make a list).
In pairs, they ask the AI 3–5 meaningful questions about the data.
Students record the AI’s answers, identify which are useful, and adjust prompts if needed.
Each student creates a short visual or written report summarizing what the AI helped them uncover.
Students will then present their findings to the class.
Students will then create a class list of useful prompts they were able to use to extract data, this list is then placed in the classroom for the students to refer back to throughout the year.
4**. Closure / Assessment / Reflection (5 minutes)**
Students will write on an exit ticket and answer the following:
1. What is one insight that AI helped you see in the data, that you may have not noticed.
2. What would you still double-check yourself before trusting AI’s conclusion?
Differentiation & Accessibility:
Strategies for diverse learners (ELL, IEP, 504, Gifted, etc.)
ELLs: Use translated instructions or allow prompting in native language with AI translation tools
IEP/504: Offer simplified datasets and pre-written prompts
Gifted: Allow exploration of additional datasets or create custom visualizations with Canva/Datawrapper
UDL: Multiple output methods (slides, posters, written report, audio)
Assessment:
Formative/ Summative assessment method(s)
Formative
Observation of student-AI interactions and prompt design
AI journal entry tracking insights/questions
Group discussion quality
Summative
Final product: Written or visual analysis of dataset
AI Prompt Reflection Sheet
Rubric assessing understanding of data trends, responsible AI use, and synthesis of insight.