Scores, ranks, answer keys, and an impossible review queue.
Item analytics can show a weak topic. They do not reliably show the first place a learner's reasoning stopped holding.
MySquire is designed to help coaching teams move beyond answer keys: isolate the first unsupported step, test the likely cause, guide a correction, and check the idea on a fresh problem.
Ask one question that separates the explanations.
Faculty can review a handful of scripts deeply—or move on with the syllabus. MySquire is being built for the space between those choices: fast, evidence-aware reasoning repair for every eligible error.
Item analytics can show a weak topic. They do not reliably show the first place a learner's reasoning stopped holding.
A short repair for the learner and an actionable pattern for faculty—without pretending one wrong answer reveals a mind.
Each step earns the right to make the next claim. When the evidence is weak, the system asks or abstains.
Preserve the unaided work and identify the first transition that cannot yet be justified.
Separate the observable error from possibilities such as a slip, a forgotten condition, or a rule-level gap.
Choose a short question whose answers distinguish the leading explanations instead of forcing a label.
Give the minimum useful explanation, require reconstruction, and move to a fresh no-hint problem.
Give faculty the pattern, source evidence, confidence, affected learners, and one bounded reteach check.
Walk through the complete reasoning-repair loop. The scenario is deterministic and illustrative, so every claim remains inspectable.
An answer key can mark this incomplete. The harder question is whether the missing branch was a slip or reflects a reusable rule gap.
Supported transitions are checked with bounded rules before an explanation is phrased.
Plausible causes stay separate until another response provides useful evidence.
Unknown is a valid result. Unsupported certainty should route to faculty, not the learner.
Faculty can inspect the work, probe, confidence, and suggested action before relying on it.
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 diagnosis?
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.
MySquire is early. The boundaries are intentional, and the hard questions belong in public.
No. The first product is a bounded post-mock workflow. It starts from an attempted solution, withholds unsupported certainty, and checks whether a repaired idea transfers to another problem.
Usually not. The same answer can come from a conceptual gap, a forgotten rule, notation, or a slip. MySquire treats possible causes as hypotheses and uses a short probe before it presents a diagnosis.
No. During early pilots, every learner-facing diagnosis is intended to be human-reviewed. Faculty receive the evidence, confidence, affected learners, and a suggested action—not a black-box label.
The experience on this website is an illustrative, deterministic prototype. It demonstrates the intended reasoning loop; it is not a production AI tutor and does not store student data.
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.