Assessment Integrity Candidates Respect
CoderPad pairs AI-aware, project-based assessments with layered detection and fair monitoring—so you can trust the signal without treating candidates like suspects.
Key Outcomes for Fraud & Cheating Detection
- Harder to Game, Better Signal Open-ended, multi-file projects are intentionally difficult to one-shot with AI and expose the reasoning you actually hire for.
- Real-Time Detection at Scale Catch suspicious behavior across high-volume campaigns with automated alerts (IDE exit, external paste), code playback, and workflows to flag or auto-reject.
- Respectful, Transparent Monitoring Balance integrity with experience: evaluate real-world skills (including how candidates use AI) rather than relying on heavy-handed surveillance.
- Fewer False Positives/Negatives Real projects add depth and complexity that reveal true capability—reducing “perfect test, poor onsite” outcomes.
Why does this matter now?
Traditional MCQ/Leetcode tasks are easily handled by AI; single-file, single-answer challenges are widely compromised. Integrity has to be designed into both content and controls.
Solve your Top Challenges in Fraud & Cheating Detection
| Cheating & Integrity Challenges | CoderPad Solutions |
|---|---|
| AI tools trivialize MCQ/Leetcode; content leaks | Multi-file, job-relevant projects that require human reasoning and explanation. |
| “Silent paste” / help from others is hard to spot | Code similarity checks, code playback, IP tracking, and IDE exit tracking highlight anomalous behavior. |
| Large university or early-talent drives make manual review impossible | Robust cheat mitigation & detection across hundreds of candidates with scalable workflows. |
| Heavy proctoring hurts candidate experience | “Surveillance vs. Reality” approach—evaluate real skills (including AI collaboration) while applying appropriate monitoring. |
| Need evidence, not suspicion | Optional webcam proctoring with AI image analysis, plus audit trails via playback and pad summaries. |
Core Features for Fraud & Cheating Detection
- Integrity by Design (Projects) Open-ended, multi-file projects that are cheat-resistant by design and assess how candidates use AI safely and effectively.
- Multi-Layered Detection Code similarity detection, playback to spot abnormal copy/paste, IP tracking, and IDE exit tracking for MCQ and coding tasks.
- Proctoring Options Automated webcam screenshots analyzed by AI to flag suspicious behavior when required.
- Activity Insights & Auditability Real-time suspicious-activity alerts (navigate away, external paste), searchable playback, and pad summaries for fast review.
- Secure Test Mechanics Anti-copy statements, per-question timers, and test randomization reduce answer-sharing and lookup value.
+96% of tests started are completed
4,000+ customers across 165 countries
Unified screen to interview workflow
FAQs
-
No. We evaluate how candidates use AI (prompting, tool choice, verification) within realistic projects—skills that matter on the job.
-
Code similarity, code playback, IP tracking, IDE exit tracking, and optional webcam proctoring with AI image analysis.
-
Yes, robust cheat mitigation & detection and workflows scale to hundreds or thousands of candidates.
-
Our “Surveillance vs. Reality” approach balances integrity with realism; Meta’s candidate satisfaction remains high using these practices.