Scores, ranks, answer keys, and an impossible review queue.
Item analytics can show a weak topic. One wrong option still does not explain which next check would be useful.
Reasoning intelligence for learning
Most AI gives students answers.MySquire learns why they get them wrong.
Not by guessing from one response. MySquire keeps possible causes open, asks a discriminating probe, checks bounded mathematics, repairs the reasoning, and measures what survives without hints.
Long-term reasoning copilot · current experience is a synthetic v0 · no learner data is saved
Response 014
Algebra · prototypeFind all real valuesx² = 49
Observed · recordedOption A · x = 7
Not observedWhy option A was selected
keep openRecording omissionplausible
Principal-root confusionplausible
Next move
probe firstHow many real values satisfy y² = 25?
Designed for
Class 11 JEE mathematicsPost-mock reviewFaculty-guided pilotsDelayed transfer checks
01 / Problem
The gap after every test
A score says what went wrong. It rarely explains why.
Faculty can investigate a handful of wrong responses deeply—or move on with the syllabus. MySquire is being built for the space between those choices: fast, evidence-aware repair for released error patterns.
Evidence for the next best teaching move.
A checked repair path for the learner and an actionable pattern for faculty—without pretending one wrong answer reveals a mind.
02 / Method
From error to next action
A bounded loop. Not an open-ended chatbot.
Each step earns the right to make the next claim. When the evidence is weak, the system asks or abstains.
Read the response honestly
Start from the selected MCQ option and name only the observable mathematical pattern.
Keep causes provisional
Separate the observable error from possibilities such as a slip, a forgotten condition, or a rule-level gap.
Ask one useful probe
Choose a short question whose answers distinguish the leading explanations instead of forcing a label.
Check, repair, then fade help
Verify a constrained mathematical check, deliver a reviewed repair, and move to a fresh no-hint problem.
Turn evidence into action
Give faculty the pattern, source evidence, evidence state, affected learners, and one bounded reteach check.
Interactive investor v0
One wrong option. Two possible reasons. One checked repair.
Walk through an MCQ-first synthetic flow. Released IDs cross a private symbolic boundary; uncertainty remains inspectable.
- 1Response
- 2Candidates
- 3Probe
- 4Check
- 5Repair
- 6Transfer
- 7Action
Synthetic single-choice response
Find all real values of x that satisfy x² = 49.
The recorded response is wrong. That fact can identify an observable pattern, but it cannot identify why the learner chose it.
Released item branch-01clinic-content-v0.1.1
03 / VisionLong-term product vision
A reasoning model that improves with every reviewed mistake.
With permission, MySquire will learn from response patterns, discriminating probes, verified repairs, faculty corrections, and unaided transfer—not from one-shot learner labels.
Roadmap
Roadmap, not a current production capability.
Learn recurring patterns
Build a longitudinal memory of which reasoning errors return and under what conditions.
Predict likely struggle
Estimate where a learner may fail next, then test that prediction before acting.
Choose the intervention
Decide when to probe, repair, fade help, abstain, or route to a teacher.
04 / Payoff
One evidence trail, two views
Useful for the learner. Actionable for faculty.
Fix the reason marks keep leaking.
- Start from the learner's selected option
- Receive a bounded hint before a solution
- Prove the repair on an unaided variant
Know what to reteach—and who still needs help.
- Inspect source response and uncertainty
- Group only evidence-supported patterns
- Override, escalate, and recheck later
05 / Trust
Uncertainty is part of the product
Know what was checked, inferred, and left unresolved.
Math before language
Released candidate sets are checked against the original equation before a repair receipt is shown.
Competing hypotheses
Plausible causes stay separate until another response provides useful evidence.
Designed to abstain
Unknown is a valid result. Unsupported certainty should route to faculty, not the learner.
Human-visible trail
Faculty can inspect the response, probe, evidence state, and suggested action before relying on it.
06 / Validation
Targets, not traction
We will measure learning after the assistance disappears.
These are the outcomes a paid pilot is designed to test. They are not presented as achieved results.
Do repaired errors recur on an unaided variant?
Can the student apply the corrected idea after a delay?
Does the action packet reduce net review effort?
How often do faculty override or escalate a proposed repair?
Three design partners
Help shape the first post-mock reasoning clinic.
We are looking for a small number of JEE coaching teams that run regular tests, care about repeated errors, and are willing to evaluate the workflow against their current review process.
Straight answers
Before we work together.
MySquire is early. The boundaries are intentional, and the hard questions belong in public.
Is MySquire a homework solver?
No. The investor v0 is a bounded post-mock workflow. It starts from a recorded MCQ response, withholds unsupported certainty, and checks whether a repaired idea transfers to another problem.
Can one wrong answer reveal a misconception?
Usually not. The same option can come from a conceptual gap, a forgotten rule, a selection slip, guessing, or timing. MySquire keeps causes provisional and uses a short probe before it chooses or abstains from a reviewed repair path.
Does this replace faculty?
No. The v0 produces an inspectable evidence state and illustrative next action—not a black-box learner label. External mathematics review and partner-backed faculty workflow are release gates before any learner pilot.
What is live today?
The website runs an original synthetic six-item content release across two algebra protocols. The featured flow calls a bounded symbolic verifier, shows explicit abstention, stores no learner response data, and is not a production AI tutor.
What would a design partner provide?
A regular mock-test cadence, de-identified questions and attempts under an agreement, a mathematics faculty reviewer, and willingness to compare repeat-error and workflow outcomes.