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Add timed coding mock interviews and evidence-based postmortems #65

Description

@rosshd

Parent: #56

Depends on: #45, #46, #47, and the interview-profile, problem-catalog, attempt-lifecycle, adaptive-selector, safe-runner, and solution-evaluator children of #56.

Problem

Teaching mode and interview mode have different goals.

A tutor should scaffold, repair gaps, and schedule review.

An interviewer should clarify requirements, observe reasoning, limit intervention, introduce follow-ups, manage time, and defer most feedback until the exercise ends.

No current issue defines that distinct assessment behavior.

Goal

Add an explicit timed mock-interview mode with realistic interviewer interaction, durable coding evidence, and a structured postmortem.

Session setup

  • Select target level, language, duration, and focus.
  • Choose an eligible unseen or sufficiently spaced problem.
  • Explain the assessment rules, timebox, tool boundary, and how to stop.
  • Let the learner opt into accessibility accommodations.
  • Create a dedicated attempt marked as mock interview.

Interview phases

  • Problem introduction and clarification.
  • Learner plan and approach discussion.
  • Implementation.
  • Testing and debugging.
  • Complexity explanation.
  • One or more bounded follow-up changes when time permits.
  • Learner questions and closure.

Phase timing guides the interviewer but does not force abrupt transitions that invalidate useful evidence.

Interviewer policy

  • Ask natural clarification questions without creating graded checks accidentally.
  • Do not teach, praise excessively, reveal the pattern, or give unsolicited hints.
  • If the learner explicitly asks for help, record the intervention and provide only the configured level of assistance.
  • Distinguish silence, thinking time, confusion, navigation, and an answer.
  • Keep state and timing claims authoritative.
  • Allow the learner to stop the interview and optionally switch into teaching mode through an explicit transition.

Postmortem

  • Summarize strengths and the highest-leverage improvement areas.
  • Separate algorithm choice, correctness, complexity, testing, debugging, communication, and response to follow-ups.
  • Cite attempt evidence rather than generic impressions.
  • Identify which feedback is certain, uncertain, or based on evaluator judgment.
  • Recommend a small next practice sequence through the adaptive selector.
  • Do not assign mastery solely from the mock-interview score.

Acceptance criteria

  • Mock-interview mode is explicit and cannot start accidentally from normal tutoring.
  • Timing, phase, and intervention events are durable and resumable where appropriate.
  • The interviewer does not leak hints or teaching content before an explicit help request.
  • Clarification dialogue does not pollute graded-answer state.
  • Follow-ups modify constraints or requirements without silently replacing the original problem.
  • Stopping or switching to teaching mode is explicit and preserves evidence.
  • The postmortem references concrete attempt evidence and separates evaluation dimensions.
  • Runner or provider failure invalidates affected assessment evidence instead of penalizing the learner.
  • Tests cover strong, struggling, silent, help-requesting, interrupted, and tool-failure sessions.
  • Live dogfood includes at least two target levels and records human review of realism.
  • make check passes.

Out of scope

  • Human interviewer marketplaces.
  • Live video or audio analysis in the first version.
  • System-design and behavioral mock interviews.
  • Claims that one mock score predicts hiring outcomes.

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