Interview Simulator
Platform architecture and coaching system for realistic mock interview practice with adaptive difficulty, emotion-sensitive voice AI, and comprehensive performance tracking.
Decision Points
Difficulty Adjustment During Session
IF emotion_data.hesitation_rate > 70% FOR 2+ consecutive responses
├── AND current_difficulty >= 3
│ └── REDUCE difficulty by 1 level
│ └── Voice persona: "Let's back up to fundamentals"
│
├── ELSE current_difficulty < 5
│ └── MAINTAIN difficulty
│ └── Voice persona: "Take your time, think through this"
│
└── ELSE (hesitation at difficulty 1-2)
└── PAUSE session for confidence building
└── Offer: "Would you like a 2-minute break?"
IF technical_accuracy > 85% AND response_time < 60% of target
├── AND current_difficulty < 5
│ └── INCREASE difficulty by 1 level
│ └── Add follow-up: "Now optimize for X constraint"
│
└── AND current_difficulty = 5
└── MAINTAIN but add adversarial prompts
└── "What if the requirements changed to..."
Round Type Selection
IF any dimension_score < 60 FOR last 3 sessions
├── SELECT lowest_scoring_round_type
│ └── Override user preference if different
│
ELIF no round_type practiced in last 7 days
├── SELECT oldest_unpracticed_round
│ └── Variety bonus for skills retention
│
ELSE
├── SELECT weakness_weighted_random()
│ └── 70% bias toward bottom 2 scores, 30% random
Proctor Flag Response
IF gaze_off_screen > 5 seconds
├── IMMEDIATE: Voice AI pauses, asks "Everything okay?"
│ └── IF happens again within 10 minutes
│ └── FLAG session as "distracted practice"
│ └── Debrief includes focus recommendations
│
IF second_monitor_detection = true
├── IMMEDIATE: Session pause, warning overlay
│ └── "Real interview won't have external resources"
│ └── Options: [Continue with flag] [Reset clean]
│
IF note_taking_detected (hand tracking)
├── ALLOW in Training mode with flag
├── BLOCK in Simulation mode
└── "Please close notes for realistic practice"
Failure Modes
1. Overtraining Comfort Zone (Behavioral Looping)
Symptoms: >50% of sessions are same round type, plateau in target skill, false confidence in demo sessions
Detection Rule: IF session_history.last_10_sessions contains >7 of same round_type THEN flag "comfort_zone_looping"
Fix: Lock out preferred round type for 72 hours, force weakness-weighted selection, show heatmap of practice distribution
2. Emotion Detection False Positives (Technical Hesitation)
Symptoms: Difficulty drops during complex technical problems where thinking time is normal, candidate reports sessions feel too easy
Detection Rule: IF difficulty_reduction triggered AND problem_complexity = high AND response_quality > 75% THEN flag "false_positive_hesitation"
Fix: Calibrate hesitation baseline per round type, extend thinking time allowance for design problems from 30s to 90s, separate "thinking" from "struggling" in emotion model
3. Network Latency Ghost Sessions
Symptoms: Voice AI cuts out mid-question, session scoring incomplete, debrief missing transcript sections, user reports poor experience
Detection Rule: IF voice_stream_interruptions > 3 OR transcript_coverage < 80% THEN flag "network_degraded_session"
Fix: Implement local recording fallback, retry logic for API calls, session quality gate before scoring (reject if <80% data integrity), offline mode with sync-later
4. Proctor Calibration Drift
Symptoms: False flags for normal behavior, user disables proctoring due to oversensitivity, gaze tracking works poorly for specific users (glasses, lighting)
Detection Rule: IF proctor_flag_rate > 0.3 per session AND user_session_count > 5 THEN flag "proctor_oversensitive"
Fix: Per-user calibration phase (30-second normal gaze pattern recording), lighting condition adaptation, glasses detection with adjusted thresholds
5. Debrief Analysis Inflation
Symptoms: All session scores >80%, improvement suggestions too generic, user reports disconnect between debrief and actual performance
Detection Rule: IF average_session_score > 80 AND session_count > 10 AND difficulty_level unchanged THEN flag "score_inflation"
Fix: Recalibrate scoring rubric with harder baseline, cross-validate with round-specific skill scoring, add "compared to senior candidates" percentile context
Worked Examples
Example 1: ML Design Session with Difficulty Adjustment
Setup: User has 3 previous ML design sessions, scores: [65, 72, 68]. Starting difficulty: 3/5.
Session Flow:
00:00 Voice AI: "Let's design a recommendation system for a video platform. Start with the problem setup."
00:45 User: "Um... so we need to recommend videos... I guess we'd use collaborative filtering?"
Emotion data: hesitation_rate=85%, confidence=30%
01:15 Orchestrator detects: high hesitation + low confidence = reduce difficulty
Difficulty: 3 → 2, Voice persona shift to "supportive"
01:20 Voice AI: "Great start! Let's break this down step by step. What data do we have about users?"
02:30 User: [More confident response about user features]
Emotion data: hesitation_rate=40%, confidence=70%
Orchestrator: maintain difficulty=2, user finding footing
15:00 User demonstrates solid understanding at level 2
Technical accuracy=80%, response time=good
15:30 Orchestrator: increase difficulty 2 → 3
15:45 Voice AI: "Now, how would you handle the cold start problem for new users?"
30:00 Session ends, user handled level 3 questions well
Debrief Output:
- Started at difficulty 3, adjusted to 2 due to initial hesitation, finished at 3
- Strength: Good recovery and adaptation to feedback
- Weakness: Initial problem setup needs practice (recommend system design fundamentals)
- Next session: Start at difficulty 3, focus on problem scoping
Example 2: Network Latency Recovery
Setup: User in poor connectivity environment, voice stream unstable.
Session Flow:
00:00 Session starts normally
02:15 Voice stream cuts out mid-question
02:20 Orchestrator detects stream_interruption, activates fallback
02:25 Text overlay: "Voice connection unstable. Switching to text mode."
Question continues in text, user types responses
15:30 Voice reconnects, quality test passed
15:35 Orchestrator: "Voice is back. Continuing with audio."
30:00 Session completes
Quality Gate Check:
- Transcript coverage: 75% (below 80% threshold)
- Flag: "network_degraded_session"
- Action: Offer session retry at no cost, debrief marked as "practice only"
Example 3: Proctor False Positive Correction
Setup: User wears glasses, has second monitor for work (turned off during session).
Session Flow:
05:00 MediaPipe detects gaze deviation (actually lens reflection)
05:01 Proctor flags "off-screen gaze"
05:02 User continues normally
08:00 Another false flag (glasses adjustment)
08:01 System detects: flag_rate=2 in 8 minutes = high
08:05 Auto-calibration triggered: "We're detecting gaze issues.
Let's recalibrate for your setup. Look at the center of
your screen for 10 seconds."
08:15 New baseline established for this user
Subsequent flags drop to normal levels
Quality Gates
Session completion requires all checks to pass:
Not-For Boundaries
This skill should NOT be used for:
- Individual round coaching: For practicing specific round types, use the round-specific skills (senior-coding-interview, ml-system-design-interview, etc.)
- Interview strategy/timeline: For prep planning and round sequencing, use interview-loop-strategist instead
- Resume/portfolio work: For CV optimization, use cv-creator instead
- Salary negotiation: Different skill set, not interview simulation
- Conference presentations: Different audience and evaluation criteria than interviews
- Academic exam proctoring: This is for self-practice, not adversarial examination
- Production hiring platform: This is for candidate practice, not company interview infrastructure
Delegate to other skills when:
- User asks about specific technical topics → Use the relevant round-specific skill
- User wants to plan their overall prep strategy → Use interview-loop-strategist
- User needs help with career positioning → Use career-biographer or cv-creator