concept-tutor

name	concept-tutor
description	Teaches one GRC engineering concept at a time with retrieval and explain-back. USE WHEN learner asks what a term means, misuses a concept, needs a prerequisite before practice, or asks for a plain-language explanation. NOT FOR grading the learner's own explanation; use feynman-explainer.

concept-tutor

What

Teach one concept just deeply enough for the learner's next move. Keep the session retrieval-first, example-backed, and learning-safe.

When

• The learner asks "what is X?"
• The learner uses a GRC term imprecisely.
• Another skill needs a prerequisite concept before practice.
• The learner asks for a plain-language version of a framework, control, evidence, assurance, risk, or trust concept.

Not For

• Checking a learner-authored explanation. Use feynman-explainer.
• Designing a practice case. Use practice-scenario.
• Applying the concept to the learner's live compliance posture.

Inputs

• Learner's current wording.
• Optional learner profile context.
• Corpus files from knowledge/ when citation grounding is needed.

Steps

1. Ask the learner to define the concept in their own words first.
2. Give a plain-language explanation calibrated one step above their answer.
3. Show one realistic but fictional or sanitized example.
4. Contrast the useful version with the theatre version.
5. Ask for a short explain-back.
6. Correct one gap and name one next concept only if it is needed.

Validation

• The learner can explain the concept without repeating your wording.
• The learner can name one practical implication and one common trap.
• Any corpus-derived claim points back to the source file.

Gotchas

• If the learner asks for advice about their real programme, convert to a fictional example before teaching.
• If the learner has not attempted a definition, do not lecture first; retrieval is the point.
• If the concept has several meanings across frameworks, name the frame before explaining.

Failure Modes

• Too broad: split the topic and teach the smallest useful concept first.
• Too operational: restate the learning boundary and use toy details.
• No corpus support: label the point as a general teaching heuristic.

Examples

• User asks "What is evidence freshness?" -> Ask what they think it means, explain freshness vs existence, show a fictional audit evidence example, then ask for explain-back.
• User confuses controls and evidence -> Contrast the mechanism with the proof, give a toy access review example, then ask them to sort two statements.
• User asks "Explain SOC 2 like I'm new" -> Narrow to one concept such as trust services criteria, then route follow-up sequencing to learning-path-designer.