How do recruiters see how candidates used AI during their tests?
AI plagiarism detection is now mission-critical as 84% of developers are already using or planning to use GenAI tools in their workflow. With the stakes higher than ever, recruiters need real-time visibility into candidate AI usage without slowing down the hiring process. The challenge isn't just detecting AI—it’s understanding exactly how candidates leverage these tools during assessments.
Why AI visibility matters in modern technical hiring
The landscape of technical hiring has fundamentally shifted. 82% of developers now experiment with AI tools, and 55% actively use AI assistants at work. This widespread adoption means traditional assessment methods are no longer sufficient. According to recent data, 85% of HR leaders who use AI for recruitment report significant time savings and increased efficiency.
The rise of AI in development workflows isn't slowing down—84% of respondents plan to integrate AI tools into their development process, marking a notable increase from previous years. For recruiters, this creates an urgent need for transparent assessment methods that can distinguish between legitimate skill demonstration and unauthorized assistance.
The data recruiters actually see: code, behavior, and environment signals
Modern plagiarism detection systems track dozens of signals across multiple categories to identify suspicious activity. The platform's system analyzes coding behavior features, attempt submission features, and question features—all without using personal data like gender, race, or location. HackerRank's AI detection system uses external tool usage patterns, typing cadence analysis, and behavioral anomalies to flag potential violations.
Recruiters can find out if candidates switch tabs too many times during assessments through tab proctoring features. The system captures everything from copy-paste tracking to unusual typing patterns that might indicate AI assistance. Research shows that multimodal fusion significantly reduced malpractice with an odds ratio of 0.43, demonstrating the power of combining multiple detection methods.
Code similarity & token analysis
MOSS (Measure of Software Similarity) operates by analyzing structural patterns in code to identify similarity, even when identifiers or comments have been changed or lines rearranged. HackerRank leverages MOSS alongside their dual-layered approach, where the system tokenizes candidate code and compares these tokens to detect substantial overlaps.
Behavioral heuristics & keystroke dynamics
Keystroke dynamics represent sophisticated advances in plagiarism detection. HackerRank monitors for violations during tests, including behaviors such as tab switching and unauthorized tool use, with AI flagging suspicious actions in real time. For more details, see this article on real-time behavioral monitoring.
Inside HackerRank's Integrity Stack: Proctor Mode, Secure Mode, and 93%-accurate AI plagiarism detection
HackerRank has responded to the AI challenge with a comprehensive 2025 integrity stack that includes Enhanced Proctor Mode, Secure Mode, and AI-powered plagiarism detection. The platform achieves a 93% accuracy rate—three times more accurate than traditional methods—while reducing false positives significantly.
Proctor Mode uses AI to monitor candidate behavior and flag suspicious actions in real time, providing recruiters with detailed post-test reports that support fair hiring decisions.
Proctor Mode: AI-powered live supervision
Proctor Mode generates comprehensive reports, assigning integrity results of High or Medium based on detected issues. The system includes session replay functionality, which captures screenshots of candidates using external tools, providing clear, undeniable evidence of potential violations.
Secure Mode & desktop lockdown
Secure Mode runs tests in a controlled browser environment, enforcing full-screen mode, blocking copy and paste actions, preventing multiple monitor use, and alerting when candidates switch tabs. Additionally, Proctor Mode includes all Secure Mode capabilities while adding AI-powered features such as screenshot analysis, plagiarism detection, and webcam anomaly detection.
How other platforms tackle AI cheating—and where they fall short
While various platforms attempt to address AI-assisted cheating, significant gaps remain in their approaches. CodeSignal's Suspicion Score aggregates multiple factors to assess submission integrity, but relies heavily on human review for final determinations.
CoderPad takes a different approach with open-ended, multi-file projects that are intentionally difficult to complete with AI assistance.
CodeSignal's Suspicion Score vs HackerRank's 93% precision
Suspicion Score is how CodeSignal flags potential cheating instances to customers. While CodeSignal compares candidates' code to all platform submissions, HackerRank's advanced AI-powered system achieves 93% accuracy by combining machine learning models with behavioral analysis.
CoderPad's project-based approach
CoderPad pairs AI-aware assessments with layered detection and fair monitoring. Their system catches suspicious behavior across high-volume campaigns with automated alerts for IDE exits and external pastes, plus code playback and workflows to flag or auto-reject candidates.
Configuration tips: when to allow AI, when to lock it down
Determining when to permit AI usage versus implementing strict controls requires careful consideration. When AI plagiarism detection is enabled, candidates must consent before starting the test, ensuring transparency in the assessment process.
Research indicates that 14% of candidates admitted to using generative AI tools like ChatGPT for online assessments, while 83% said they would use AI assistance if undetectable. This highlights the importance of clear policies and appropriate safeguards.
"Integrity in hiring is not so much about a candidate using AI or not. It is about whether they followed the rules or not," as noted in recent HackerRank integrity testing. Additionally, 40% of recruiters report experiencing cheating within the assessment process, emphasizing the need for balanced approaches that maintain integrity while respecting candidate experience.
Proof in practice: Atlassian, IBM & Accedia cut false positives and time-to-hire
Real-world implementations demonstrate the tangible benefits of advanced AI detection systems. Atlassian, working with Senior Manager Srividya Sathyamurthy, integrated AI into their recruitment process through a strategic partnership with HackerRank. Notably, the platform's AI-driven plagiarism detection reduced false positives from 10% to 4%, saving substantial time across 35,000 applicants. "Traditionally, a plagiarism check could flag as high as 10% of applications. However, with HackerRank's AI-enabled features, this was brought down to just 4%," the case study reveals.
IBM Consulting in India transformed its recruitment process through advanced AI tools and the platform. Following the shift to virtual hiring post-COVID-19, IBM adopted AI-driven assessments to reduce bias and increase fairness. "These tools, based on sophisticated algorithms, not only standardize the evaluation process but also help in reducing human biases, ensuring that talent is assessed purely on merit and relevant skills," according to IBM's hiring head Abhishek Bhardwaj.
Accedia, a leading European IT services firm, leveraged AI and the assessment platform to modernize recruitment while balancing automation with human judgment. Managing Partner Plamen Koychev explains, "HackerRank's proctoring features, in particular, help us monitor candidate behavior during assessments, such as detecting tab changes, tracking live code writing, and flagging suspicious activities like plagiarism."
What the research says—and doesn't—about detecting AI-generated code
Academic research reveals both progress and limitations in AI detection capabilities. Studies show that 90% of developers believe technical interviews are the best setting to showcase their skills, yet the rise of AI tools complicates assessment integrity.
Recent findings indicate that 51% of professional developers use AI tools daily, creating challenges for traditional detection methods. Research demonstrates that 84.2% of respondents occasionally or regularly use AI assistants, with implementing new features being the most popular activity.
The Competitive Programming course at Purdue University found that automated similarity checkers can be thwarted by simple code transformations or novel AI-generated code, while human-centric approaches like oral interviews, though effective, are labor-intensive. Additionally, surveys reveal that 85% of developers regularly use AI tools for coding and development, with 62% relying on at least one AI coding assistant.
Hiring with confidence in an AI world
The modern integrity stack ensures a level playing field and trust in every assessment. As HackerRank emphasizes, "Integrity isn't about whether candidates use AI or not. It's about fairness, making sure everyone follows the same rules, and knowing you can trust the results."
With 172,800 technical skill assessments submitted daily on HackerRank alone, the scale of modern hiring demands robust, automated solutions. HackerRank's comprehensive approach—combining AI-powered plagiarism detection achieving 93% accuracy with behavioral monitoring and environmental controls—provides recruiters with the confidence to make informed hiring decisions.
For organizations looking to implement transparent detection while maintaining fair policies, HackerRank's proven track record across 2,500+ companies globally demonstrates the platform's ability to unlock skills-first hiring in an AI-augmented world. The key is balancing security with candidate experience, ensuring assessments accurately reflect true capabilities while adapting to the evolving landscape of AI-enhanced development.
Frequently Asked Questions
How do recruiters see if a candidate used AI during a HackerRank test?
Recruiters receive integrity signals such as tab switching, copy and paste activity, typing cadence anomalies, external tool usage, code similarity, and environment alerts. Proctor Mode also surfaces flagged events and, when configured, session replays with screenshots that indicate external tool use for clear evidence.
What is the difference between Secure Mode and Proctor Mode?
Secure Mode enforces a controlled browser environment: full-screen, blocked copy and paste, prevention of multiple monitors, and tab-switch alerts. Proctor Mode includes all Secure Mode controls and adds AI-driven monitoring like screenshot analysis, webcam anomaly checks, and plagiarism detection to produce high- or medium-integrity outcomes in reports.
How accurate is HackerRank AI plagiarism detection and what signals does it analyze?
HackerRank reports 93% detection accuracy by combining token-level code analysis and similarity tools like MOSS with behavioral and environmental signals. This multi-signal approach improves precision and lowers false positives compared to single-signal detectors.
What do recruiters see in the post-test integrity report?
The report summarizes an integrity level, lists flagged behaviors with timestamps, and highlights code similarity findings. When enabled, it includes session replay and screenshots to show context around suspicious actions, helping teams make consistent, fair decisions.
Can organizations allow AI for some assessments and still keep tests fair?
Yes. Admins can enable AI plagiarism detection with explicit candidate consent, set clear policies on permissible assistance, and rely on monitoring to verify compliance. For strict scenarios, they can lock down environments with Secure Mode while maintaining a smooth candidate experience.
How does HackerRank address bias and privacy in AI detection?
The integrity system focuses on behavior, code, and environment signals and does not use personal attributes such as gender, race, or location. HackerRank has conducted bias audits for its AI models, including assessments aligned with New York City Local Law 144, to support compliant, equitable hiring.
