Quarter Page

Quarter 1 — Know Yourself

Aug 6 to Oct 8, 2026

Main focus

Portable learner profile, Before/After baseline, and personalized learning agent

Quarter 1 establishes the course culture, tool norms, pass-path expectations, and first major AQR build. Students gather evidence about how they learn, preserve an ordinary-Gemini Before Project baseline, and use what they learn to build more useful personalized support.

The learner profile is the source material for the tool. Students use it to create a personalized learning agent, Gemini Gem, NotebookLM or another source-grounded workflow where useful, or a structured fallback support system. They then test what changes from the Before Project baseline. The goal is useful customization, not a generic chatbot.

What students do

The quarter stays practical and artifact-based.

By the end of Q1, students should have a portable learner profile, a useful personalized support workflow, real proof-of-progress habits, and Before/After evidence showing what personalization and testing changed.

Students build

a simple pass-path spreadsheet model
an ordinary-Gemini Before Project baseline saved for later comparison
a portable learner profile built from surveys, inventories, reflections, and self-data
personalized learning-agent instructions tied to profile evidence
a working support tool or structured fallback workflow
an After Project comparison, one real test, one revision, and a short explanation of what changed

Math / QR lift

modeling the pass path and testing scenarios in a simple spreadsheet
interpreting self-data without confusing confidence, preference, habit, mood, and ability
questioning what a scale, survey, or rating actually measures
organizing evidence into a usable profile
testing and revising a tool based on evidence

Sequential QR focus windows

Two short-focus themes support the main project.

These are not separate full units or two hidden courses running at once. One focus window usually carries the first half of the quarter and the second carries the next half, with short callbacks later.

Focus window

What are we measuring? Self-data, scales, units, and meaning

What exactly is being measured, counted, rated, or compared?

Students examine the meaning and limits of their own survey results, ratings, comfort checks, and self-data before using those results to make decisions about support.

Focus window

Misleading visuals, claims, and AI confidence

What is this trying to make me believe?

Students check graph tricks, dashboard claims, fake precision, and confident AI output while they build and test their learning agents.