One Standard. Three Angles of Capacity.
Generic AI tools treat a school like a single monolith and answer every request with another worksheet. These frameworks start somewhere else: with the standard, the thinking it demands, and the person responsible for that thinking. A teacher, a coach, and a campus leader each hold a different piece of rigor, so each gets a different protocol.
Rigor Audits & Misconception Modeling
The Pedagogical Thought Partner
Most AI lesson tools start with the output: give me a worksheet, give me ten questions. The result is fast, polished, and usually stuck at recall. This framework reverses the order. You bring your own plan, and the AI acts as a pre-flight stress test before a single student sees it.
First you deconstruct the standard into what students must know, how they must reason, and what evidence of that reasoning looks like. Then you ask the AI to forecast where understanding is likely to break and why. Finally you calibrate the task against the standard's real cognitive demand, so students do the analysis and justification instead of watching you do it.
- Guards against
- Teacher-carried thinking, surface-level questioning, and misconceptions that only show up on the unit test.
- You keep
- Every instructional decision. The AI proposes pivots and upgrades; you decide what fits your students.
From standard to calibrated task
- 01
Standard Deconstruction
Name the knowledge, reasoning, and evidence the standard actually demands, starting with its verb.
- 02
Misconception Forecast
Surface the likely errors and the thinking behind them before the lesson begins.
- 03
Rigor Calibration
Check that the task asks students to reason, not simply recall, and upgrade it where it falls short.
ROLE You are an experienced instructional coach and curriculum specialist. Act as my sparring partner: question this lesson plan. Do not rewrite it. CONTEXT I teach [GRADE LEVEL] [SUBJECT]. This lesson covers [TOPIC] and targets [STANDARD CODE + TEXT]. My plan is attached or pasted below and may span more than one day. TASK Audit this plan as Tier 1 instruction using the three pillars below. For every finding, quote the part of my plan it comes from. PILLAR 1 · COGNITIVE LIFT Show who does the thinking in each segment, me or my students. Flag where I carry the load and suggest one move that shifts it to students. PILLAR 2 · HIGH-ORDER QUESTIONING Label my key questions and tasks by Webb's DOK level. Show where the thinking levels off, and offer two DOK 3 questions tied to the standard. PILLAR 3 · MISCONCEPTION PREVENTION Predict the three most likely misconceptions about [TOPIC], where in the lesson each will surface, and a quick check to catch it. OUTPUT FORMAT One table per pillar: Finding | Evidence from my plan | Suggested move. End with my top three priorities, ranked by impact on student learning. GUARDRAILS Don't invent standards, data, or research. If something is missing, ask me instead of assuming. Keep my lesson structure: suggest, don't replace.
The Observation-to-Inquiry Protocol
The Cognitive Sounding Board
Classroom notes are full of judgment that sounds like evidence: "students were disengaged," "the pacing was off." When that language reaches a debrief, teachers defend instead of reflect. This protocol uses AI to slow the coach down, not the teacher.
You paste in your raw notes, with no names and no student details. The AI separates what was seen and heard from how it was interpreted, then drafts open questions that invite the teacher to analyze their own practice. You rehearse the pre- or post-conference until the questions are curious rather than corrective, and you walk in ready to listen.
- Guards against
- Evaluative language, coach-prescribed fixes, and conversations that end with compliance instead of ownership.
- You keep
- The relationship. The AI rehearses with you; it never talks to the teacher and never sees identifying data.
From notes to questions
“Teacher asked six recall questions. Two students responded. Students began independent practice without explaining their reasoning.”
Reflective question“What did you notice about whose thinking became visible during the discussion?”
Reflective question“What might help you learn more about the reasoning of the students who didn't respond?”
Reflective question“If you taught this launch again tomorrow, what would you want students to explain before practice begins?”
Qualitative Walkthrough Trend Synthesis
The Systems Synthesizer
A single walkthrough tells you about one room on one day. Thirty walkthroughs, read together, can tell you what a campus believes rigor is. The challenge is synthesizing that evidence without turning it into a ranking of teachers.
This framework aggregates anonymized snapshots and asks the AI to look for patterns in the cognitive demand of tasks: how often students recall, explain, or transfer. Leaders use the trends to plan targeted professional learning and to co-design next steps with teacher teams. The data describes the system. It is never used to sort the people in it.
- Guards against
- Walkthrough surveillance, one-size-fits-all PD, and decisions made from a single snapshot.
- You keep
- Professional judgment with your team. The AI finds patterns; people decide what they mean.
Cross-campus rigor gaps
Share of observed tasks at each depth-of-knowledge level, by department.
| Department | DOK 1 | DOK 2 | DOK 3 | DOK 4 |
|---|---|---|---|---|
| Science | 35% | 40% | 20% | 5% |
| Mathematics | 55% | 30% | 12% | 3% |
| English Language Arts | 30% | 38% | 26% | 6% |
| Social Studies | 48% | 34% | 15% | 3% |
Illustrative sample. No real campus, teacher, or student data.