Preventing Multitasking in Virtual Interviews: How AI Detects External Assistance During Hiring 

Introduction 

Virtual interviews have revolutionized the hiring scene. From tech giants to early-stage startups, everyone is now leveraging the power of remote assessments. They’re faster, more flexible, cost-effective, and allow recruiters to tap into a global talent pool. But this evolution hasn’t come without challenges. One of the most pressing issues in this space is multitasking or cheating during virtual interviews. 

Think about this: a candidate sitting at home for a video interview might have notes pasted on their monitor, another person feeding them answers off-camera, or even ChatGPT open in another window. In fact, according to a recent survey conducted across India and Southeast Asia, 38% of candidates admitted to using some form of external help during virtual interviews. 

That’s a big red flag for organizations looking to hire credible, qualified talent. 

The question is, how can recruiters tell if a candidate is truly performing under pressure or just faking it? 

This is where AI-powered interview platforms come into play. With advanced behavior tracking, gaze detection, audio monitoring, and anomaly detection, AI is helping recruiters catch multitasking and external assistance in real-time. In this blog, we’ll dive deep into the technical mechanics behind it, explore industry stats, explain the tools used, and provide tips for recruiters to implement these solutions effectively. 

Why Multitasking in Virtual Interviews Is a Problem 

In a traditional in-person interview, recruiters can read body language, observe behavioral cues, and ensure that candidates aren’t being assisted. But in the virtual space, that layer of supervision disappears. And unfortunately, many candidates exploit this gap. 

Multitasking in interviews often includes: 

• Reading off prepared scripts or notes. 

• Googling answers or using AI tools like ChatGPT. 

• Getting whispered cues from someone off-camera. 

• Switching screens to refer to documentation. 

• Receiving real-time prompts through earphones or chat apps. 

Such practices result in biased assessments, overestimated skills, and hiring decisions that backfire. 

According to LinkedIn’s 2024 Global Hiring Trends report, 56% of HR professionals say their top concern with virtual interviews is verifying candidate authenticity. 

Let’s break down how AI can now solve that. 

How AI Detects Multitasking and External Help During Virtual Interviews 

AI doesn’t just “watch” the candidate, it analyzes behavior patterns, audio data, screen interactions, eye movement, and biometrics to detect inconsistencies that human recruiters often miss. Here are the key technologies that make it possible: 

1. Gaze Tracking and Eye Movement Detection 

One of the most advanced tools used in AI-driven virtual hiring is gaze analysis. Using computer vision, the platform tracks a candidate’s eye movements to understand where they’re looking during the interview. 

AI can detect: 

• Frequent glances to the side (possibly reading notes). 

• Looking down repeatedly (at a phone or notebook). 

• Prolonged stares away from the screen (indicative of second monitors). 

• Non-verbal eye behavior like scanning or scanning-and-return patterns. 

This data is mapped in real-time and compared against expected behavioral patterns based on the interview type. 

Fact: Gaze tracking systems today boast an accuracy rate of over 92% in detecting off-screen distractions, as reported in a study by the Journal of Human-Computer Interaction in 2023. 

Example: If a candidate consistently breaks eye contact every time a complex question is asked, AI flags it as “external assistance suspected.” 

2. Facial Expression and Micro expression Analysis 

Facial recognition software has evolved to read micro expressions involuntary facial reactions that occur within milliseconds. These cues reveal anxiety, confusion, surprise, or confirmation-seeking behavior, all of which can hint at unnatural or assisted responses. 

AI flags: 

• Delayed facial reactions. 

• Eye narrowing or looking for approval before speaking. 

• Sudden head tilts or pauses after hearing a prompt. 

• Mismatched emotion-to-verbal response ratio. 

This is particularly helpful when candidates are being coached off-camera or pausing to hear from someone else before answering. 

3. Audio and Speech Anomaly Detection 

AI-powered hiring tools today include Natural Language Processing (NLP) and audio pattern recognition to catch audio-based cues such as: 

• Background whispers or multiple voices. 

• Irregular pauses and delayed responses. 

• Shifts in tone or pitch between sentences (indicating assistance). 

• Keyboard sounds that don’t match typing behavior (e.g., rapid searches). 

Some tools also use voice fingerprinting to differentiate between the candidate’s voice and any secondary voice present in the room. 

Stat: AI systems using multi-voice audio detection have reached 96% precision, making them reliable even in noisy environments. 

4. Browser Lockdown and Screen Activity Monitoring 

To combat cheating through device manipulation, many proctoring platforms now implement secure browser environments that: 

• Lock the test/interview window. 

• Detect screen switching or tab changes. 

• Monitor copy-paste activity. 

• Capture system notifications or background app usage. 

• Flag second screen/virtual machine usage. 

This data is recorded as part of the interview audit trail and used for post-interview analysis or real-time flagging. 

5. Behavioral Biometrics and Anomaly Detection 

Every person has a unique way of typing, moving the mouse, responding to questions, and even breathing. AI builds a behavioral profile using metrics like: 

• Typing cadence. 

• Voice modulation. 

• Click and scroll behavior. 

• Emotional sentiment scores. 

Deviations from baseline patterns suggest external influence or multitasking behavior. 

AI uses time-series anomaly detection models (like Isolation Forests and One-Class SVMs) to flag these instances. These models are trained on datasets of genuine vs. aided interviews. 

Fact: Behavioral biometric tracking increases fraud detection efficiency by up to 40% when combined with gaze and audio analysis. 

Real-World Example: AI in Action 

Let’s look at a real-world use case. 

Case Study: DreamTech Solutions — Remote Developer Hiring 

DreamTech, a US-based SaaS firm, hired 300 developers remotely in 2023. Before using AI monitoring, they found that 26% of candidates underperformed post-hiring despite stellar interviews. 

After implementing AI-based hiring tools with gaze, audio, and behavioral monitoring, they discovered that: 

• 31% of interviewed candidates showed signs of multitasking. 

• 17% were flagged for potential off-screen assistance. 

Post-implementation, their bad hire rate dropped to 7%, saving them nearly $200,000 annually in rehiring and training costs. 

Tips for Recruiters and Companies 

If you’re hiring virtually and want to integrate AI to reduce cheating, here’s how to do it smartly: 

Be Transparent 

Always inform candidates that AI is monitoring the session. It sets expectations and discourages dishonesty upfront. 

Customize Detection Thresholds 

Not every deviation is cheating. For example, some candidates may look away briefly while thinking. Adjust sensitivity based on role type. 

Always Use Human Review 

AI can flag, but it shouldn’t be judge and jury. Let recruiters review the evidence before disqualifying someone. 

Respect Privacy Regulations 

Ensure your AI tools comply with GDPR, CCPA, and local data laws. Store only what’s necessary and anonymize wherever possible. 

Educate Interviewers 

Train your HR team to interpret AI analytics meaningfully. Use the data as an enhancer, not a replacement. 

Interesting Facts You Probably Didn’t Know 

• The term “digital proctoring” first emerged from online exam environments, not hiring. 

• AI-based gaze detection was originally built for autonomous driving systems. 

• Candidates that perform well under monitored interviews statistically have 28% higher on-job retention rates. 

The Future of AI in Virtual Hiring 

The next wave of virtual interviews won’t just be about detecting fraud but creating immersive, gamified, and trust-centric experiences. 

Here’s what’s coming: 

• Digital Integrity Scores for each candidate, based on real-time behavioral analysis. 

• AR-powered interviews, where candidates appear in controlled virtual environments. 

• Blockchain-based video verification, which ensures that interviews can’t be tampered with or replicated. 

In short, AI will become the gatekeeper of trust in the hiring process. 

Final Thoughts 

Multitasking during virtual interviews may seem like a harmless shortcut to some candidates. But for companies, it poses a serious risk to hiring integrity, team performance, and long-term retention. 

By leveraging AI technologies like gaze tracking, behavioral biometrics, and anomaly detection, organizations can now detect dishonesty with remarkable accuracy,  and do so ethically and non-invasively. 

AI doesn’t just expose fraud; it empowers recruiters to make better decisions, faster. 

As the line between physical and virtual hiring continues to blur, embracing AI-powered solutions is not just a good idea, it’s a necessity. 

Want to level up your hiring process with AI-backed interviews? Let’s connect. We help you build trust, one hire at a time. 

FAQs 

1. Is cheating possible in an online interview? 

Yes, cheating is possible in online interviews. Candidates may: 

• Use hidden prompts or scripts. 

• Have someone else feed them answers. 

• Use AI-generated responses. 

• Look off-screen for help or search for answers in real-time. 

However, advanced tools like Aptahire now use eye-tracking, facial analysis, and behavioral cues to detect such irregularities and maintain authenticity. 

2. What is a red flag when doing virtual interviews? 

Common red flags in virtual interviews include: 

• Frequent eye movement away from the screen. 

• Unnatural delays in answering questions. 

• Overly rehearsed or generic responses. 

• Voice not matching facial expressions or lips. 

• Background noise or whispers indicating assistance. AI hiring platforms like Aptahire flag these behaviors to alert recruiters. 

3. How to detect cheating in an interview? 

Cheating can be detected using: 

• Eye and head movement tracking. 

• Voice stress analysis to detect nervous or inconsistent tones. 

• Plagiarism checks for scripted or AI-generated answers. 

• Real-time monitoring tools that watch for external help, screen switching, or dual logins. 

4. What is the hardest part of a virtual interview? 

The most challenging aspects include: 

• Building rapport without physical presence. 

• Maintaining eye contact through the camera. 

• Tech glitches like lags or mic issues. 

• Reading non-verbal cues accurately. For candidates, it’s about staying natural and focused; for interviewers, it’s ensuring fairness and engagement. 

5. Is it possible to detect cheating in an online exam? 

Yes, and it’s getting easier with technology. Tools now offer: 

• Proctoring software with webcam and screen monitoring. 

• AI alerts for suspicious behavior like looking away often or someone entering the room. 

• Keystroke dynamics to detect if someone else is typing. AI-backed platforms can automatically flag and record anomalies for review.