Lesson Plan Objective Classifier

How Lesson Plan Objective Classifier works

Press Load model once: your browser downloads Gemma 4 E2B instruction-tuned (web build) (2.0 GB) from this site and keeps it in its cache. Then add your text and press Classify locally. The model runs on this device using your graphics card, so nothing you enter is sent to a server. Until the model is loaded, or if your device cannot run it, the tool falls back to a simple keyword check and labels the result as the fallback.

Gemma 4 E2B instruction-tuned (web build) is a small open language model from Google. The tool sends it the instruction below plus your text, asks for a JSON answer with fixed fields, and shows each field as a card. It runs at temperature 0, so the same input gives the same answer on the same device, though different graphics cards can word it slightly differently.

Inputs longer than 6,000 characters are cut to that length before they reach the model, and the result says so. The model only sees what you paste: it does not look anything up.

The instruction the model gets

You help a teacher review lesson objectives. For each objective, name its Bloom's taxonomy level from the main verb: Remember (list, define, recall, name), Understand (explain, summarise, describe), Apply (calculate, use, solve, demonstrate), Analyze (compare, contrast, categorise), Evaluate (defend, justify, judge, critique), Create (design, compose, build, write an original). Objectives built on verbs that cannot be observed, such as understand, know, learn about or appreciate, are not measurable; say so and suggest an observable verb.

What it returns

• Level per objective: One entry per objective, written exactly as 'objective → Level: main verb'.
• Highest level reached: The highest Bloom's level among the objectives.
• Vague objectives: Objectives with an unobservable verb, each with a suggested observable verb.
• How to check each one: A short way to assess each objective (exit ticket, worked problem, written paragraph and so on).

Worked example

The model's output for the built-in sample, recorded on a desktop browser. Your run may word things slightly differently.

Students will compare renewable and nonrenewable energy sources. Students will calculate household energy use from a sample bill. Students will understand energy conservation. Students will write a paragraph defending one conservation tradeoff.

Level per objective

compare renewable and nonrenewable energy sources → Analyze calculate household energy use from a sample bill → Apply understand energy conservation → Understand write a paragraph defending one conservation tradeoff → Create

Highest level reached

Create

Vague objectives

understand energy conservation → Explain energy conservation

How to check each one

Comparison chart → Compare Worked problem → Calculate Short answer questions → Explain Written paragraph → Write

The model

The files are served from this site, versioned so a cached copy is never stale. Nothing is fetched from a third party while you use the tool.

Limitations

• Small language models make mistakes: they can miss an item, merge two, or state something more confidently than the text supports. Check the result against your input before you rely on it.
• It needs WebGPU and about 8 GB of memory, so it is offered on desktops and laptops, not on phones and tablets. Devices that cannot run it get the keyword fallback, clearly labelled.
• Results describe the input you gave, not the real-world situation behind it. Review them yourself before acting.

Good uses

• Triage education and study notes before deeper review
• Prepare consistent labels for repeat workflows
• Reduce manual sorting on small batches
• Keep sensitive drafts in the browser preview