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Build question-asking training in plain language. Learners interview an AI character who's holding a hidden problem. The transcript is graded on which questions were asked, in what order, and whether the learner reached the correct diagnosis.
An interactive training activity where the learner interviews an AI character who is holding a hidden problem, a customer with a vague complaint, a patient with subtle symptoms, an engineer with a flaky bug. The learner must ask the right questions to surface what's really going on, and the transcript is graded on question quality, sequence, and final diagnosis.
From a plain-English description of the hidden problem and the diagnostic rubric, to a SCORM-ready interview simulation.
Tell your coding agent who the character is and what's really going on. The character only volunteers information that's appropriate for what's been asked.
Name the questions a skilled interviewer would ask, in roughly the right sequence. The AI grades each turn against this rubric, with credit for variation on the same intent.
"Make the customer minimize the symptom on first ask." "Add a red herring that points toward a wrong diagnosis." Iterate by talking, not by editing dialogue trees.
One command produces a SCORM 1.2 zip. Upload to any SCORM LMS. Diagnostic-quality scores post back to the gradebook automatically.
The same toolchain powers clinical-interview training, sales-discovery practice, technical-support troubleshooting, root-cause analysis, and qualitative-research interviewing.
Practice taking a focused history. The AI patient minimizes symptoms, contradicts themselves, or volunteers irrelevant detail, the way real patients do.
Train reps to ask questions that surface real business pain instead of features-and-functions checklists. The AI buyer rewards open-ended questions and stonewalls leading ones.
Practice the diagnostic conversation between support engineer and customer. The AI customer doesn't know the technical vocabulary, so the engineer has to ask the right questions.
Practice extracting reproducible bug details from a non-technical reporter. Graded on which questions narrow the search space and which waste time.
Train UX researchers and journalists to ask non-leading questions that uncover real user behavior, not the answers they were hoping for.
Practice the post-incident interview. The AI subject is defensive, vague, or over-eager to assign blame. The learner is graded on extracting facts without contamination.
When the interview ends, the AI scores each turn against the diagnostic rubric. Credit goes to the right kinds of questions in the right sequence, not just to a learner who stumbles into the right diagnosis through luck.
Update the rubric, hidden-problem pool, or character behavior any time. Re-export the SCORM package and the next attempt grades against the new rules.
Each turn is graded against your rubric, did the learner ask an open-ended question, was the sequencing logical, did they follow up on the implicit signal. Evidence quoted from the transcript.
The AI character holds information that is only revealed when the learner asks for it. Skipping a question costs the learner that information for the rest of the interview.
Different runs use different underlying diagnoses drawn from a pool. Learners can't memorize the right answer because the right answer changes.
Change the hidden problem, soften the character, add a red herring, or rewrite the rubric, just describe the change in your own words. Your coding agent rebuilds the character and re-exports the SCORM package without you ever editing a dialogue tree.
When you're happy, export as a SCORM 1.2 package for your LMS, or use the same HTML bundle standalone.
recent-spicy-meal. Wrong-diagnosis path now scores lower if not investigated further.Paste any prompt into your coding agent to get a complete diagnostic-interview simulation as a single self-contained HTML file. Adjust the character, hidden problem, or rubric in plain language.
A patient with non-classic chest pain who minimizes the symptom and volunteers a GERD-flavored red herring. Tests follow-up question quality.
Using /diagnostic-interviews, build a 14-turn chest-pain interview. Score on open-ended history, red-herring resistance, and cardiac vs GI differential.
An enterprise buyer who has a real deadline but doesn't volunteer it. Practice the discovery questions that surface real urgency vs polite interest.
Using /diagnostic-interviews, build a 10-turn enterprise discovery interview. Score on uncovering hidden urgency, decision-process mapping, and economic-buyer ID.
A non-technical customer reporting a 'sometimes broken' bug. Practice extracting reproducible details without overwhelming the customer.
Using /diagnostic-interviews, build a 12-turn flaky-bug support call. Score on reproduction-step extraction, frequency capture, and non-technical clarity.
An on-call engineer interviews a teammate who first noticed an outage. Practice surfacing timeline and signals without leading the witness.
Using /diagnostic-interviews, build an 8-turn incident-eyewitness interview. Score on timeline accuracy, signal capture, and non-leading questions.
A UX researcher interviews a participant who keeps trying to be helpful by guessing what the researcher wants to hear.
Using /diagnostic-interviews, build a 10-turn user-research interview with a please-the-researcher participant. Score on non-leading questions and observed-vs-desired behavior.
An investigator interviews a defensive employee after a near-miss safety incident. Practice extracting facts without contaminating memory or assigning blame.
Using /diagnostic-interviews, build a 12-turn post-incident interview. Score on factual extraction, non-leading questions, and blame neutrality.
Every diagnostic-interview activity exports as a standards-compliant SCORM 1.2 package. Upload the zip, assign it like any other course activity, and diagnostic-quality scores flow back to the gradebook automatically.
cmi.core.score.raw and cmi.core.lesson_statusAuthoring, hidden-problem mechanics, LMS compatibility, character realism, model choice, answered directly.
A diagnostic-interview activity is a training simulation where the learner interviews an AI character who's holding a hidden problem. The learner must ask the right questions to surface what's going on, and the transcript is graded on question quality, sequence, and final diagnosis.
A role-play is an open conversation graded against rubric objectives, the character could be anyone with any goal. A diagnostic interview is specifically about question-asking, the character is holding a hidden problem and the learner is graded on how efficiently they uncover it.
Yes. You can define a pool of underlying problems, and each learner attempt draws a different one. This makes the activity replayable and prevents learners from memorizing the right answer.
Each turn is graded against the rubric of questions a skilled interviewer would ask. The learner gets credit for question types and signals followed up on. The final score combines question quality with whether they reached the correct diagnosis.
Yes. Every activity exports as a SCORM 1.2 package, which works in Cornerstone, Moodle, Canvas, TalentLMS, Docebo, Brightspace, and every other SCORM-compliant LMS. Scores post back to the gradebook automatically.
Yes, that's the point. You can configure the character to minimize symptoms, contradict themselves, volunteer red herrings, or get defensive. The interview gets harder the more realistic the character's behavior.
The bundles run on whichever frontier model you configure, Claude, GPT, Gemini, or a self-hosted model. The model is swappable; the rubric, hidden-problem definitions, and grading logic are model-agnostic.
Diagnostic Interviews is in active development. It will install via your coding agent with a single command, the same way the existing role-play skill works today. Add your email below and we'll let you know the day it ships.