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Prepare questions that distinguish textual evidence from unsupported inference.
Explore this workflow →Explore a supplied dataset, ask analysis questions and generate charts using natural-language instructions.
Open Julius AI ↗Official getting-started help ↗For: Educators demonstrating data inquiry and eligible older learners with approved access. Documentation reviewed November 7, 2026.
Teacher demonstrations of data cleaning, aggregation and visual interpretation.
Inferring causes from a chart or accepting analysis without checking the underlying data.
Prepare a small public CSV containing dates and temperatures, its source and stated units.
Use this sample prompt or task input as a starting point. Adapt the subject and level to your learners.
Using this public weather CSV, check date and temperature columns first. List missing values, then plot mean temperature by calendar month, excluding missing temperatures. Label units and show the monthly counts; make no causal claim.
Verify sample counts, numerical results and axis choices. Generated charts can look convincing while representing the wrong column or time period.
Inspect units, missing values and calculations; keep identifiable grades and student records out of examples.
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This workflow is an editorial suggestion based on documentation, not a hands-on test or a claim of learning gains. Check school approval, current pricing, and student account eligibility before a classroom rollout. Use public or permitted samples and keep identifiable student information out of unapproved services.