AI Data Analyzer

Learning objectives

• Explain the main ai/ml decision that AI Data Analyzer is designed to support.
• Use Enable AI narrative analysis, Analysis focus to test how different assumptions change the scenario.
• Interpret Traditional analysis in plain language and connect them to an action or conclusion.
• State one limitation, risk, or governance consideration before using the result in a real decision.

Run modes

• Supported modes: Browser
• Demo type: Interactive browser demo
• Primary launch surface: index.html

Before you start

Starts immediately in browser with no installs, no API keys, and classroom-safe defaults.

This helps set classroom expectations before students click into the live experience.

Core context

Look for the dataset fields, missing values, and the question the analysis is trying to answer.

Observe which charts or summaries change the interpretation of the data.

Concepts covered

What students should note

Note that students should state the decision supported by the analysis, not just describe the chart.

List of steps

• Choose the run mode that fits the class: Browser.
• Review the default assumptions before changing anything.
• Change one or two inputs, then use `Analyze Dataset`.
• Read Traditional analysis first, then compare any supporting metrics, charts, or AI text.
• Capture one insight, one limitation, and one action recommendation.

Input variables explained

• `Enable AI narrative analysis` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.
• `Analysis focus` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.
• `CSV dataset` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.

Decision buttons explained

• `Analyze Dataset` is the main action that computes, compares, or generates the next result from the current inputs.

Outputs explained

• `Traditional analysis` is the factual baseline because it summarizes the dataset numerically before interpretation.
• `AI interpretation` is the narrative layer that helps students discuss meaning, but it should always be validated against the traditional output.

What to notice

• The strongest classroom comparison is between what the data objectively says and what the narrative claims it means
• Change the analysis focus and watch how the same data can produce different but not equally useful interpretations
• Ask students which conclusion is evidence-backed and which one is a hypothesis needing validation

Discussion and reflection

• What business or technical decision would you make differently after using AI Data Analyzer?
• If you changed one assumption and ran `Analyze Dataset`, which output moved the most and why?
• What would you still want to validate with real data, policy, or expert review before acting on the result?