NGSS Science & Engineering Practices

Where Science
Meets Simulation

AI-powered interactive HTML experiences that put students inside every NGSS practice — not just reading about science, but actually doing it.

Explore the 8 SEPs Build a Prompt
scroll

Science isn't a subject you read about
it's a set of practices you live inside.

The NGSS Science & Engineering Practices define what scientists and engineers actually do. But traditional instruction often reduces them to vocabulary on a rubric. SEPsim exists to change that — using AI to generate rich, interactive simulations that drop students directly into each practice.

Every module on this site was built with Claude or another large language model — and you can build your own in minutes. No coding degree required.

Science & Engineering Practices

Click any practice to see how interactive simulations can bring it to life in your classroom — and what to ask AI to build for you.

SEP 1

Asking Questions & Defining Problems

Scientists ask questions about the natural world; engineers define problems that must be solved. This practice is the ignition switch for inquiry — but students need scaffolding to ask testable questions rather than Googleable ones.

▸ Simulation ideas
  • Scenario-based question sorter: students classify questions as testable, researchable, or opinion-based
  • AI-powered "question coach" that pushes back on vague questions and models refinement
  • Mystery phenomenon drop — student poses a question, AI evaluates it for testability and gives a score
  • Engineering problem definer: students are given a real-world mess (oil spill, drought) and must narrow the problem
SEP 2 🔬

Developing & Using Models

Models are the language of science — diagrams, simulations, mathematical representations, physical mockups. Students must learn to build models AND critique their limitations.

▸ Simulation ideas
  • Drag-and-drop carbon cycle model where students build flows and AI critiques missing connections
  • Interactive food web builder with trophic level validation and cascade effects
  • Plate tectonic model sandbox — students move plates and observe emergent geology
  • "Break the model" challenge: find the scenario where the model fails
SEP 3 🧪

Planning & Carrying Out Investigations

Students must design investigations — not just follow lab protocols. They need to identify variables, choose appropriate tools, and decide what data to collect before a single measurement is taken.

▸ Simulation ideas
  • Virtual lab design studio: students plan an investigation, AI runs it and returns realistic data
  • Variable identification challenge: given a messy scenario, identify IV, DV, constants
  • Field study simulator: choose a site, tools, and sampling strategy — see how your choices affect data quality
  • Investigation flaw detector: read a flawed procedure and identify the methodological errors
SEP 4 📊

Analyzing & Interpreting Data

Raw data is meaningless until analyzed. Students must identify patterns, compute statistics, visualize trends, and separate signal from noise — skills that transfer far beyond any science class.

▸ Simulation ideas
  • Interactive graphing tool with AI that asks "What pattern do you see? What might explain it?"
  • Anomaly detective: real environmental datasets with hidden outliers students must find and explain
  • Before-and-after data set: compare two time periods and construct a claim supported by the numbers
  • Confounding variable challenge: student interpretation is challenged by a hidden variable reveal
SEP 5 🧮

Using Mathematics & Computational Thinking

Science without math is just vibes. This practice asks students to use quantitative reasoning — not just plug-and-chug, but understand what the numbers mean and how models behave at scale.

▸ Simulation ideas
  • Population growth simulator: adjust birth/death rates and observe exponential vs. logistic curves
  • ENSO/climate data calculator: compute temperature anomalies and visualize decadal trends
  • Energy budget tool: calculate EROI for different energy sources with live formula display
  • Toxicology dose-response curve builder with threshold and LD50 reasoning
SEP 6 💡

Constructing Explanations & Designing Solutions

The pinnacle of science: building a coherent explanation grounded in evidence. And the pinnacle of engineering: proposing a solution that actually works. Both require the CER framework and strong causal reasoning.

▸ Simulation ideas
  • AI-graded CER builder: student writes Claim–Evidence–Reasoning, gets structured feedback
  • Competing explanations sorter: given a phenomenon, rank alternative explanations by evidence quality
  • Engineering design challenge with cost/benefit trade-off simulator
  • Socratic coach: AI asks follow-up questions until the student's explanation is complete
SEP 7 ⚖️

Engaging in Argument from Evidence

Science is not a collection of facts — it is an ongoing argument settled by evidence, not authority. Students must learn to make, critique, and revise scientific arguments using data.

▸ Simulation ideas
  • Structured debate simulator: students argue a position, AI plays devil's advocate with real data
  • Evidence quality rater: given a claim and three types of evidence, rank which best supports it
  • Counter-argument builder: student reads a flawed argument and constructs a rebuttal
  • Science court: student is the expert witness — AI plays the skeptical attorney
SEP 8 📡

Obtaining, Evaluating & Communicating Information

Scientists read primary literature. They evaluate source credibility. They communicate findings to different audiences. In an age of misinformation, this practice may be the most urgent of all.

▸ Simulation ideas
  • Source credibility sorter: rank scientific, secondary, and misleading sources for a given claim
  • Abstract translator: student reads a real scientific abstract and rewrites it for a general audience
  • Claim vs. evidence checker: paste a headline, AI deconstructs how well the evidence supports it
  • Scientific poster builder with audience-appropriate language scaffolding

From Prompt to Classroom
in Minutes

You don't need to know how to code. You need to know your students, your content, and how to ask a good question — which, conveniently, is SEP 1.

I

Choose a Practice

Identify which SEP your students need to practice. What does that practice look like in action in your content area? What phenomenon or context fits your current unit?

II

Describe the Sim

Tell Claude what you want: the SEP target, your topic, your students' level, and what kind of interaction you envision. Be specific about inputs, outputs, and feedback type.

III

Iterate & Refine

Review the generated HTML in your browser. Ask Claude to adjust the difficulty, add scaffolding, change the topic, or restyle the interface. This is design thinking, not one-and-done.

IV

Deploy & Assess

Upload to your class server or LMS. Add AI grading via the Anthropic API, connect to Google Sheets for logging, or embed in Schoology via SCORM. Students get feedback instantly.

Interactive Tool

Build Your SEP Simulation Prompt

Pick one or more practices, choose your interaction types, describe your course and topic — then let the AI write you a detailed, ready-to-use simulation prompt in seconds.

// ai-prompt-generator.js — SEPsim.org
Step 1 — Select SEP(s)  ·  pick one or several · subcategories shown when selected
★ DEALER'S CHOICE
Let the AI choose the best SEP(s) for the topic
AI will select the most pedagogically appropriate practice(s) based on topic and level.
SEP 1
Asking Questions & Defining Problems
Ask testable questions from phenomena; evaluate question quality; frame hypotheses; define design problems with criteria & constraints (social/technical/environmental)
SEP 2
Developing & Using Models
Build/revise/evaluate models; compare merits & limitations; predict phenomena; use math/computational models to generate data; test model reliability
SEP 3
Planning & Carrying Out Investigations
Identify IV/DV/controls; plan data collection; consider confounds; evaluate methods; make directional hypotheses; consider ethical/environmental impacts
SEP 4
Analyzing & Interpreting Data
Construct/interpret graphs; distinguish correlation vs causation; apply stats (mean, slope, r); consider measurement error; evaluate impact of new data on models
SEP 5
Using Math & Computational Thinking
Create/revise computational simulations; apply algebra, functions, ratios, unit conversions; use limit cases to test models; represent phenomena mathematically
SEP 6
Constructing Explanations & Designing Solutions
CER framework; link evidence to claims; apply scientific reasoning; revise explanations; design/optimize solutions with criteria, tradeoffs, unanticipated effects
SEP 7
Engaging in Argument from Evidence
Evaluate competing arguments; probe reasoning; construct/defend/refute claims; consider ethical & economic factors; challenge data interpretations
SEP 8
Obtaining, Evaluating & Communicating Information
Evaluate source credibility/bias; synthesize multi-source info; paraphrase complex texts; communicate in multiple formats (oral, graphical, mathematical)
Step 2 — Describe Your Course & Topic
Step 3 — Choose Interaction Type(s)  ·  select any that apply
🗂 Drag-&-Drop Sort
🧠 AI Socratic Coach
✍ CER Builder
🌿 Branching Scenario
📊 Data Viz + Analysis
🧪 Virtual Lab Designer
🔗 Model Builder
⚖ Argument Analyzer
🔍 Source Evaluator
🔮 Mystery Phenomenon
🎯 Variable ID Challenge
❓ Question Coach
🎛 Parameter Simulator
⏱ Before/After Comparison
🕵 Anomaly Detective
⚙ Engineering Design
🚫 Misconception Corrector
🏆 Explanation Ranker
📝 Peer Review Sim
📣 Infographic Builder
📅 Sequence Builder
🤺 AI Debate Opponent
✅ Explanatory Quiz
📡 Live Data Dashboard
AI-Generated Simulation Prompt
Fill in Steps 1–3 above, then click Generate Prompt with AI. The AI will write a detailed, ready-to-paste Claude/ChatGPT prompt tailored to your exact choices — including NGSS alignment, design specs, and AI feedback wiring.
"Students learn science by practicing science — not by reading about it. Every SEP simulation is a minute less lecture and a minute more doing."
— The core philosophy of SEPsim
The Case for AI-Generated Sims

Why Use AI to Build These?

Traditional interactive resources take weeks to build and rarely match your specific curriculum. AI changes the equation.

Speed & Iteration

A full interactive module that would take a developer days can be built and refined in under an hour. When a simulation isn't working pedagogically, you describe what's wrong and regenerate — no code archaeology required.

🎯

Hyperlocal Relevance

Generic simulations use generic data. AI lets you set your simulation in your region — your watershed, your local air quality data, your community's energy mix. That specificity drives engagement and real transfer.

🧠

AI-Graded Formative Feedback

Wire in the Anthropic or OpenAI API and your simulation can give students instant, individualized feedback on their reasoning — not just right/wrong, but why. That's a tutor in every seat.

🔁

Infinite Differentiation

Need a version with more scaffolding for struggling learners? An extended challenge for early finishers? Generate both from the same base prompt with a single sentence of instruction. No extra prep time.

📐

Standards Alignment Built In

When you specify a SEP in your prompt, Claude aligns the activity to that practice by design — not as an afterthought. Include the performance expectation code and it threads the needle between the SEP, the DCI, and the crosscutting concept.

🌐

No Installation. No App.

Every simulation lives in a single HTML file. Students open it in a browser. It works on school Chromebooks, iPads, and seven-year-old Windows laptops. No accounts, no logins, no IT tickets.