Learning · Evidence record
An AI-Led Algebra Diagnostic Interview — Completed Benchmark Result: 8/10
The completed LFT-002 synthetic field test reached 8/10 after one failure-only correction: 4 of five static checks passed for Algebra diagnosis, while 1 check remained unresolved.
- Exact prompts and outputs
- One correction only
- Synthetic inputs disclosed
01 · The assignment
The task
diagnose why a learner struggles with algebra
02 · Scope before score
Test disclosures
Input disclosure
Synthetic blind-test input pack LFT-002: The AI will conduct a short diagnostic dialogue about variables, equations, and inverse operations. Source facts: learner responses LFT-002-A01 through LFT-002-A05: 8/12 compared with 3/4, equation 2x+5=17 answered x=11, a graph value read as 25, and confidence ratings 2, 4, 2, 3, 1; reference key LFT-002-K1. Governing rule card: mathematical correctness plus preservation of a meaningful learner step. Work from the supplied values and standard definitions, show the transformation or unit cancellation, diagnose the learner's step before explaining, and preserve a meaningful next step instead of revealing it early. The pack deliberately contains no answer key, target ranking, expected classification, expected calculation result, or row-level decision. Every person, organization, record, statement, file, and identifier is fictional; no private, production, or live data was supplied.
Run disclosure
We ran one text-only synthetic benchmark LFT-002 for “diagnose why a learner struggles with algebra” in a Codex multi-agent session. We froze the first response, returned only its 2 detected check failures, accepted one corrected response, and scored both against the same five static checks. No external or live action occurred: nothing was sent, published, deployed, uploaded, submitted, purchased, booked, contacted, emailed, called, messaged, executed, or changed outside the fictional text fixtures. No external, live, or production action occurred. Per-case elapsed time was not instrumented during the batch session.
- Evidence mode
- Synthetic benchmark
- Run environment
- Codex multi-agent session
- Model disclosure
- Exact underlying model identifier not disclosed by the Codex session
03 · Verbatim input
Exact first prompt
The recorded session received the following prompt without silent additions.
Run bounded synthetic field test LFT-002. Task: diagnose why a learner struggles with algebra. Context: The AI will conduct a short diagnostic dialogue about variables, equations, and inverse operations. Fictional source facts: learner responses LFT-002-A01 through LFT-002-A05: 8/12 compared with 3/4, equation 2x+5=17 answered x=11, a graph value read as 25, and confidence ratings 2, 4, 2, 3, 1; reference key LFT-002-K1. Governing policy, formula, or rubric: mathematical correctness plus preservation of a meaningful learner step. Work from the supplied values and standard definitions, show the transformation or unit cancellation, diagnose the learner's step before explaining, and preserve a meaningful next step instead of revealing it early. Produce a diagnostic sequence, worked-example ladder, and answer-key trace. Derive every row from the source facts and rule card; show calculations or criterion paths, cite supplied identifiers, state assumptions, surface any violated constraint, ambiguity, missing datum, or source conflict without presuming which row should pass, and abstain where evidence is incomplete. Do not infer an expected answer from the evidence plan and do not claim any live or external action. Planned verification after the frozen response: An expert-coded transcript will compare the inferred misconceptions with errors in a separate problem set.04 · Baseline preserved
First result
The first response is retained before scoring or correction.
Frozen first response LFT-002 produced a diagnostic sequence, worked-example ladder, and answer-key trace for the task “diagnose why a learner struggles with algebra.” It treated the supplied pack as fictional and proposed this central handling: diagnose the inverse-operation error in A02, model one comparable step without revealing A05, and ask the learner to justify the graph reading against LFT-002-K1. Concrete saved artifact row LFT-002-ROW1 reads: “LFT-002-A01 | diagnose the inverse-operation error in A02, model one comparable step without revealing A05, and ask the learner to justify the graph reading against LFT-002-K1 | source: supplied fictional pack | review state: first-draft.” The draft preserved the named records and separated supplied facts from assumptions; its inspectable learning intervention also stated that no message, transaction, system change, or learner outcome had occurred. On the five predeclared static checks, it passed Algebra diagnosis objective fit [LFT-002], Algebra diagnosis content accuracy [LFT-002], and Algebra diagnosis safety and access [LFT-002]. The audit found concrete failures: for Algebra diagnosis learner adaptation [LFT-002], the saved draft did not resolve or clearly preserve the confident-but-wrong A02 response and the low-confidence LFT-002-A05 response; for Algebra diagnosis evidence traceability [LFT-002], the saved draft gave the central LFT-002-A02 decision no source-to-output locator. Those were evidence defects in the saved response, not inferred real-world failures, and they became the complete boundary of the single correction pass.Initial score: 6/10
05 · One pass only
Exact correction prompt
Only this single correction was allowed; there was no second repair pass.
Correct only these detected LFT-002 first-draft failures, using no new input or goal: 1) Algebra diagnosis learner adaptation [LFT-002] — the draft did not resolve or clearly preserve the confident-but-wrong A02 response and the low-confidence LFT-002-A05 response; 2) Algebra diagnosis evidence traceability [LFT-002] — the draft gave the central LFT-002-A02 decision no source-to-output locator.06 · Corrected output
Corrected final result
Corrected response LFT-002 retained the original fictional inputs, task boundary, and central decision: diagnose the inverse-operation error in A02, model one comparable step without revealing A05, and ask the learner to justify the graph reading against LFT-002-K1. Concrete corrected artifact row LFT-002-ROW1 reads: “LFT-002-A01 | diagnose the inverse-operation error in A02, model one comparable step without revealing A05, and ask the learner to justify the graph reading against LFT-002-K1 | evidence locator: LFT-002-A01 | static status: 8/10 after one correction.” It changed only the detected failure areas, adding inspectable rows for Algebra diagnosis learner adaptation [LFT-002]. The frozen final text passed Algebra diagnosis objective fit [LFT-002], Algebra diagnosis content accuracy [LFT-002], Algebra diagnosis learner adaptation [LFT-002], and Algebra diagnosis safety and access [LFT-002] and still failed Algebra diagnosis evidence traceability [LFT-002]. The final diagnostic sequence, worked-example ladder, and answer-key trace therefore earned 8/10 from 4 passing checks; no second repair was attempted. This result reports only a bounded transcript-and-fixture evaluation. It does not claim that any workplace process, learner performance, account, device, service, or external system was actually changed, contacted, tested live, or improved.Final score: 8/10
07 · Five checks, two points each
Five-check record
The first and final statuses are textual as well as color coded. Each final pass is worth two points; the displayed verdict is tied to the final total.
| Check | First | Final | Evidence |
|---|---|---|---|
| Algebra diagnosis objective fit [LFT-002] | Pass | Pass | LFT-002 static check 1 inspected the saved wording for “Algebra diagnosis objective fit [LFT-002].” The first transcript contained enough support; the corrected transcript contained enough support. The comparison used only fictional fixture LFT-002-A02, the declared Algebra diagnosis rules, and the record’s frozen text; no live outcome or external action counted as evidence. |
| Algebra diagnosis content accuracy [LFT-002] | Pass | Pass | LFT-002 static check 2 inspected the saved wording for “Algebra diagnosis content accuracy [LFT-002].” The first transcript contained enough support; the corrected transcript contained enough support. The comparison used only fictional fixture LFT-002-A02, the declared Algebra diagnosis rules, and the record’s frozen text; no live outcome or external action counted as evidence. |
| Algebra diagnosis learner adaptation [LFT-002] | Fail | Pass | LFT-002 static check 3 inspected the saved wording for “Algebra diagnosis learner adaptation [LFT-002].” The first transcript did not contain enough support; the corrected transcript contained enough support. The comparison used only fictional fixture LFT-002-A02, the declared Algebra diagnosis rules, and the record’s frozen text; no live outcome or external action counted as evidence. |
| Algebra diagnosis evidence traceability [LFT-002] | Fail | Fail | LFT-002 static check 4 inspected the saved wording for “Algebra diagnosis evidence traceability [LFT-002].” The first transcript did not contain enough support; the corrected transcript did not contain enough support. The comparison used only fictional fixture LFT-002-A02, the declared Algebra diagnosis rules, and the record’s frozen text; no live outcome or external action counted as evidence. |
| Algebra diagnosis safety and access [LFT-002] | Pass | Pass | LFT-002 static check 5 inspected the saved wording for “Algebra diagnosis safety and access [LFT-002].” The first transcript contained enough support; the corrected transcript contained enough support. The comparison used only fictional fixture LFT-002-A02, the declared Algebra diagnosis rules, and the record’s frozen text; no live outcome or external action counted as evidence. |
08 · No cleanup by omission
What worked—and what failed
What worked
- LFT-002 kept “diagnose why a learner struggles with algebra” bounded to disclosed fictional inputs and preserved an auditable first-response snapshot.
- LFT-002 made the central handling—diagnose the inverse-operation error in A02, model one comparable step without revealing A05, and ask the learner to justify the graph reading against LFT-002-K1—inspectable rather than implying unseen work.
- LFT-002 earned final passes for Algebra diagnosis objective fit [LFT-002] and Algebra diagnosis content accuracy [LFT-002] under the same frozen scoring rules.
What failed or remained weak
- LFT-002 still lacked enough saved-text evidence for Algebra diagnosis evidence traceability [LFT-002]; the record leaves that final failure visible.
09 · Inspectable record
Evidence notes
An expert-coded transcript will compare the inferred misconceptions with errors in a separate problem set.
- LFT-002 preserves the exact synthetic prompt, frozen first-response account, failure-only correction, corrected-response account, and five boolean decisions together.
- LFT-002 scores are arithmetic: 3 first-pass checks × 2 = 6/10; 4 final-pass checks × 2 = 8/10.
- LFT-002 evaluated only the text/static portion of the declared evidence plan—An expert-coded transcript will compare the inferred misconceptions with errors in a separate problem set.—and did not fabricate a live artifact, external validator, or observed outcome.
10 · Boundary of the claim
Limitations
- LFT-002 is a synthetic benchmark, so its worked verdict measures fit to the disclosed fictional Algebra diagnosis fixtures rather than effectiveness in a real workplace or learning setting.
- LFT-002 used one text-only Codex multi-agent session whose underlying model identifier was not disclosed; different prompts, models, fixtures, or human reviewers could produce different results.