Has AI made AI-powered employment assessments irrelevant?

Online pre-employment assessments have become a cornerstone of modern hiring. Employers increasingly ask job candidates to complete tests—ranging from coding challenges to personality questionnaires—to verify skills and qualities beyond what a résumé shows. In fact, more than half of organizations use pre-hire assessments as part of their hiring process. These tests promise a more objective, data-driven way to identify qualified talent, often allowing companies to consider non-traditional candidates who lack typical credentials but can prove their abilities (More companies are using preemployment assessment tests – WTOP News).

This report explores the different types of online assessments (technical, cognitive, personality, etc.), why employers use each, and how effective they are. It also examines the rise of candidates using AI tools (like ChatGPT) to cheat on assessments, why they do it, and what risks this poses. Finally, we discuss strategies employers can adopt to counteract AI-assisted cheating—improving test design, proctoring, and alternative evaluation methods—to ensure hiring remains fair and effective.

Types of Online Assessments and Why Employers Use Them

Technical Skills Assessments (Work Samples & Job Simulations)

What they are: Tests targeting job-specific skills or tasks. These can include coding challenges for software roles, sample writing or editing exercises for content roles, digital marketing case studies, design portfolio reviews, or role-play simulations for sales and customer service. Often, they are effectively work sample tests—requiring candidates to perform tasks similar to the actual job.

Why employers use them: To verify that candidates truly have the hands-on skills their resume claims. With technical assessments, employers “want candidates to prove they can do the job” in practice, rather than relying solely on credentials (More companies are using preemployment assessment tests – WTOP News). For example, tech companies commonly use coding platforms (HackerRank, Codility, etc.) to have developers solve programming problems under time constraints, ensuring they can actually write correct code. Amazon is known to use online simulations and work-style assessments that present job-related scenarios and problems, evaluating how candidates handle them (Pre-employment assessment: Your guide to hiring superstars).

Retailers like Walmart give situational judgment tests asking how an applicant would respond to customer situations or work with co-workers (Pre-employment assessment: Your guide to hiring superstars). These assessments help predict on-the-job performance by sampling the candidate’s real capabilities. A well-designed work sample or simulation has high face validity (it feels relevant to the role) and can be a strong predictor of job success. Research shows that work sample tests are among the top predictors of future performance (3 Things You Must Know About Selection, According to Schmidt & Hunter) since they closely mimic actual duties.

Effectiveness: Technical assessments can filter out candidates who interview well but lack practical skills, thereby improving the quality of hire. They are especially useful for roles where specific tool proficiency or problem-solving ability is essential. For instance, State Farm (insurance) uses a PI Cognitive Assessment to gauge a candidate’s capacity to learn and adapt (important for grasping new concepts on the job), and H&M uses logical reasoning tests to ensure candidates can handle the analytical aspects of retail decisions (Pre-employment assessment: Your guide to hiring superstars). However, effectiveness depends on realism and relevance of the test. If the assessment is too abstract (e.g. brainteaser puzzles) or overly difficult, it may screen out good candidates who simply aren’t test savvy.

Still, when aligned with job requirements, these tests are valued—92% of employers in one survey said the biggest benefit of pre-employment testing was better quality of hire (Just Released: The 2019 Pre-Employment Testing Benchmark Report | Criteria Corp) (note: this figure comes from a testing vendor’s survey and reflects a positive view among their clients). Employers also report that skills assessments let them consider candidates without traditional qualifications: 36% of HR professionals say someone who scores high on a skills test but lacks the usual experience is very likely to advance in the hiring process (More companies are using preemployment assessment tests – WTOP News). In short, technical/work-sample tests help employers make merit-based decisions and find hidden gems, which is why 76% of companies use some form of skills testing in hiring (Pre-employment assessment: Your guide to hiring superstars).

Cognitive Ability Tests (Aptitude and Reasoning Tests)

What they are: Standardized assessments measuring general mental abilities—such as numerical reasoning, verbal comprehension, logical thinking, spatial reasoning, or problem-solving speed. Examples include IQ-like tests, the Wonderlic test, or vendor-specific cognitive aptitude tests (Criteria’s CCAT, SHL’s General Ability Test, etc.). Some are traditional Q&A; others might be game-based puzzles targeting memory or pattern recognition.

Why employers use them: Decades of research in industrial-organizational psychology shows that cognitive ability (often called general intelligence or GMA) is one of the single best predictors of job performance across many occupations (3 Things You Must Know About Selection, According to Schmidt & Hunter). High-level reasoning skills indicate a candidate’s capacity to learn quickly, solve novel problems, and handle complex tasks.

Employers use cognitive tests to identify candidates who have the raw brainpower to succeed, especially in roles requiring critical thinking or technical learning. For example, consulting and finance firms may use aptitude tests to gauge the quantitative and analytical skills of applicants. State Farm’s use of the PI Learning Indicator (which covers verbal, numerical, and abstract reasoning) is specifically to evaluate “overall learning capacity and aptitude” for quick on-the-job learning (Pre-employment assessment: Your guide to hiring superstars). Cognitive tests are also used to streamline high-volume hiring; for instance, government agencies or large corporations might screen thousands of applicants with an online reasoning test to identify the top scorers for further interviews.

Effectiveness: When properly validated, cognitive assessments can significantly improve hiring accuracy. High cognitive scores correlate with better training outcomes and job performance, particularly for complex jobs ([PDF] Cognitive Ability Tests) (Is Cognitive Ability the Best Predictor of Job Performance? New …). They also allow more inclusive hiring by focusing on ability over background – companies like Deloitte have used cognitive testing alongside blind résumé reviews to find capable candidates from non-traditional educational paths (Procurement recruitment Industry Insights and · Langley Search).

However, cognitive tests must be used carefully to avoid adverse impact. Critics note that some aptitude tests can exhibit biases (e.g. minority groups scoring lower on average, due to educational disparities or cultural bias in test questions (A critical review of the use of cognitive ability testing for selection …)). A good practice is to ensure the test content is job-related and validated to predict performance and to combine cognitive tests with other measures. Many employers blend cognitive scores with other criteria rather than using a hard cutoff, to avoid over-reliance on one metric. Despite these caveats, the broad predictive power of cognitive ability is a major reason employers continue to use these tests as part of a balanced assessment battery (3 Things You Must Know About Selection, According to Schmidt & Hunter).

Personality and Psychometric Tests

What they are: Questionnaires assessing behavioral traits, personality profiles, or work style preferences. These might measure the “Big Five” personality dimensions (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism), or more job-specific traits like integrity, risk tolerance, or teamwork style. Common examples include the Myers-Briggs Type Indicator (MBTI), DiSC profile, Hogan Personality Inventory, or newer tests from vendors like Predictive Index, Criteria, or Culture Index. Some assessments also evaluate motivations or values. For instance, Amazon’s Work Style assessment examines a candidate’s work preferences and whether they align with Amazon’s leadership principles (e.g. customer obsession, bias for action) (Pre-employment assessment: Your guide to hiring superstars).

Why employers use them: Personality and cultural fit tests aim to gauge whether a candidate’s character and preferences suit the role and company culture. Traits like conscientiousness (being responsible and organized) or agreeableness (collaborative attitude) can predict job performance in many roles, especially when technical skills are comparable. Employers use these tests to identify strengths and red flags that might not surface in a short interview. For example, a sales company might seek high extroversion and resilience in a candidate, while a detail-oriented job might prioritize high conscientiousness.

Culture fit is another motive: companies want to know if a person’s values and style mesh with the team. According to industry estimates, around 80% of Fortune 500 companies use some form of personality test during hiring or employee development. They are used as a tool to build higher-performing teams by complementing skill assessment with insight into a person’s communication style, motivation, and likely behavior on the job. For example, healthcare provider DaVita reportedly uses personality assessments to ensure new hires will contribute positively to their work culture (Pre-employment assessment: Your guide to hiring superstars).

Effectiveness: When used appropriately, personality tests can add predictive value and reduce turnover by identifying candidates whose dispositions fit the role. Research supports that certain traits – like high conscientiousness and low propensities for counterproductive work behavior – correlate with better job performance and reliability. Personality measures are not as strong predictors as cognitive ability or work samples individually, but they provide incremental insight (3 Things You Must Know About Selection, According to Schmidt & Hunter). One advantage is that these tests typically have no “right or wrong” answers, so candidates can’t simply memorize correct answers ahead of time – this makes them somewhat resistant to gaming by AI (more on this later). They can also increase perceived fairness by giving candidates another way to shine (a shy but highly diligent person might not wow in an interview, but their assessment could reveal strong conscientiousness).

On the downside, personality tests have faced criticism: candidates might try to fake answers to appear ideal (e.g. everyone knows to endorse “I am always on time” and similar desirable statements). Good test design uses consistency checks to detect faking. Another concern is validity – not all personality tests are scientifically sound (for example, MBTI is popular but often questioned by psychologists for hiring use). That said, many employers report positive outcomes. In one survey, 46% of employers (nearly half) said they use personality or psychological assessments in hiring, indicating a broad faith that these tools help predict which hires will thrive. Personality assessments, when well-designed, can reveal “strengths and areas for improvement” of candidates (Pre-employment assessment: Your guide to hiring superstars) and help hiring managers make more holistic decisions beyond hard skills.

Other Assessment Types

Beyond the big three above, employers deploy a variety of other online tests: situational judgment tests (SJTs) present hypothetical work scenarios and ask candidates to choose or rank responses, to evaluate judgment and ethics. Job knowledge tests quiz applicants on domain-specific knowledge (e.g. a medical coding test for a healthcare billing role). Emotional intelligence questionnaires measure interpersonal skills.

Gamified assessments (like Pymetrics games) evaluate cognitive and emotional traits through quick online games, aiming to make the experience engaging while collecting behavioral data. For instance, Unilever famously partnered with Pymetrics to have entry-level candidates play neuroscience-based games assessing traits like memory, risk-taking, and concentration as an initial screen (Unilever‘s Practice on AI-based Recruitment | Highlights in Business, Economics and Management). These newer methods are still aimed at the same goals: predicting job performance, at scale and in a bias-mitigating way.

Why employers use them: Each type serves a purpose. Situational judgment tests are used to gauge practical decision-making and are popular for customer service or managerial hiring (Walmart’s candidate assessment includes situational questions to see how one handles difficult customers or teamwork challenges (Pre-employment assessment: Your guide to hiring superstars)). Game-based assessments (like those by Pymetrics) are used to attract younger candidates and possibly reduce adverse impact by measuring traits in novel ways (Unilever found that using games plus video interviews helped increase the diversity of hires by 16% while cutting time-to-hire by 75% (Procurement recruitment Industry Insights and · Langley Search)). These tests also signal a company’s innovative employer brand. Ultimately, all these assessments—whether a straightforward math quiz or an AI-powered game—are tools to help employers hire based on ability and fit rather than just credentials or gut feeling.

How Employers Benefit from Assessments (Effectiveness and Bias Considerations)

Employers adopt online assessments to improve the hiring outcome, and many report significant benefits. A 2022 survey by the Society for Human Resource Management (SHRM) found 79% of HR professionals say scores on skills tests are as important or more important than traditional hiring criteria like education and experience (More companies are using preemployment assessment tests – WTOP News). In practice, this means a stellar test performance can propel a candidate forward even if their résumé is non-traditional.

Employers cite better quality of hire as a key advantage: in one benchmark report, 92% of organizations using pre-hire tests felt they improved quality-of-hire (Just Released: The 2019 Pre-Employment Testing Benchmark Report | Criteria Corp) (bear in mind this stat comes from Criteria Corp, an assessment provider, so it may be rosier than an independent analysis). Additionally, assessments can lead to faster, more cost-effective hiring by automating early screening. Unilever’s AI-based assessment process (which combined Pymetrics games and on-demand video interviews scored by AI) reportedly saved 70,000 person-hours of interviewing time in a year (The Amazing Ways How Unilever Uses Artificial Intelligence To …) (Creating an Inclusive Recruitment Process -Tips for Avoiding Bias), a 75% reduction in recruitment time (Procurement recruitment Industry Insights and · Langley Search). The efficiency gain frees up recruiters to focus on top candidates and later-stage interviews.

Another touted benefit is increased diversity and fairness. Because well-designed tests focus on abilities, they can reduce conscious or unconscious bias that might occur in résumé screening. Emily Dickens of SHRM noted that assessments when used properly, give candidates a clearer sense of the job and culture, and can open doors for more diverse candidates to succeed in the process. About 23% of HR professionals in the SHRM survey said the diversity of their hires improved after implementing assessments (More companies are using preemployment assessment tests – WTOP News). A high-profile example is again Unilever: by removing college pedigree and relying on games and structured interviews, they saw a marked increase in the diversity of their incoming cohort (16% jump in diversity metrics) (Procurement recruitment Industry Insights and · Langley Search). However, it’s important to critically assess such claims—some data on diversity improvements come from vendors or the companies themselves. (For instance, Pymetrics often publicizes success stories like Unilever’s; while likely true, these sources have an interest in highlighting positive outcomes.)

Despite these advantages, assessments are not a magic bullet and can introduce their own biases or issues if not carefully managed. Source bias is one consideration: much of the positive ROI data (time saved, turnover reduced, etc.) comes from case studies by assessment providers marketing their tools. These reports might downplay situations where testing didn’t yield benefits or mention the rigorous implementation needed to get those results. Independent academic research consistently supports the predictive validity of cognitive tests and structured work samples (3 Things You Must Know About Selection, According to Schmidt & Hunter), but also warns of adverse impact and the need for validation. If an assessment is poorly constructed or irrelevant to the job, it could screen out qualified candidates unjustly (one blog notes that if a candidate “has all the right hard and soft skills but doesn’t pass the personality test,” an overly rigid process might wrongly reject them (Pre-employment assessment: Your guide to hiring superstars). Furthermore, over-reliance on any one test score can be problematic. Best practice is to use assessments as one component of a multi-faceted selection process – for example, combining test results with structured interviews, rather than replacing interviews entirely.

Another potential pitfall is the test-taker experience. Long or opaque online assessments can frustrate applicants. A candidate who finds a test excessively difficult or unrelated may question the company’s culture or drop out. High dropout rates have been observed if assessments are too time-consuming or unengaging (Pre-employment assessment: Your guide to hiring superstars). This means employers must balance thorough evaluation with respect for candidates’ time. Transparency about why the test is used and why it’s relevant can help – candidates are more accepting of assessments that obviously tie to job skills, and less so of ones that feel like meaningless hurdles.

In summary, when implemented thoughtfully, online assessments allow employers to hire more efficiently and on merit, often improving new-hire performance and even diversity. But organizations must continuously monitor their assessment data for bias, ensure tests are valid and job-related, and be wary of overhyping vendor-provided stats without context. Used in combination with human judgment, assessments are powerful; used in isolation or without care, they can introduce new biases or incentivize candidates to find ways around them – which leads to our next topic: the emerging challenge of candidates using AI to beat these very assessments.

The Rise of AI-Assisted Cheating by Candidates

As online assessments have grown in prevalence, so have candidates’ attempts to game them. In the past, cheating might mean googling answers, hiring a proxy test-taker, or copying questions to find answers on forums. Today, the game changer is generative AI. Tools like OpenAI’s ChatGPT (and similar large language models) can solve coding problems, write essays, and even answer some logic or knowledge questions at a level that can outscore an average applicant. This has led to a spike in candidates using AI to assist or outright cheat on hiring tests, raising alarm for employers.

One striking example was reported on Reddit: 80% of applicants for a particular software engineering role tried to use ChatGPT to answer the company’s online test, many by literally pasting a screenshot of the question into the AI. The result? They all submitted the same wrong answer because ChatGPT failed to read crucial instructions in the image, exposing the cheating when the hiring manager saw the identical mistakes. In that case, over half the candidates scored extremely poorly (under 25% correct on a multiple-choice quiz) because they blindly trusted AI outputs without even verifying the answers or running the code. The manager noted the irony: these were applications for a software job, yet many candidates showed more savvy in using ChatGPT than in basic problem-solving or attention to detail (e.g. missing that the AI’s answer didn’t make sense) (If you are going to use ChatGPT to cheat on your interview test, at least run the code or check the reasoning question. : r/overemployed).

Not all AI cheating is so easy to catch. In a controlled experiment, tech interview platform interviewing.io found that nobody out of 37 interviewers could tell when a candidate was quietly using ChatGPT during a live coding interview. Candidates who fed the interview question to ChatGPT often produced “perfect” solutions, and interviewers have become accustomed to well-prepared candidates, so nothing seemed amiss. The experiment highlighted that on typical coding problems (drawn from popular interview questions), ChatGPT could solve them correctly 73% of the time when they were standard problems, and even 67% of the time if the question was slightly modified from a known problem. Only when completely novel, custom problems were asked did the AI struggle (dropping to 25% correct). This suggests that a determined candidate could leverage AI to get through many early-round technical screens undetected, especially if companies reuse common questions. As one industry commentator put it, “Up until a year ago, it was pretty hard to cheat on those [coding tests]. Now, you can literally just put it in ChatGPT and get an answer in real-time”. The polish of AI-generated answers even creates a new bias: interviewers might actually favor the AI-aided candidate’s flawless solution and unintentionally penalize an honest candidate who took longer or made a few minor mistakes while genuinely solving the problem (Tech Interviewers Can’t Tell When Candidates Use ChatGPT to Cheat Coding Rounds – Business Insider).

It’s not just coding tests. Candidates are also using AI to cheat on written assignments, short answer questions, and even personality tests. There have been reports of applicants pasting essay prompts or situational questions into AI chatbots to generate high-quality responses. Some try to use AI to select the “ideal” answers on personality questionnaires (though as noted, without a clear right answer, this is harder to do systematically). The concern for employers is that AI can enable underqualified candidates to present themselves as highly capable, at least on paper or in initial screenings. Resume and cover letter writing have already been transformed by AI – many job-seekers now use ChatGPT to produce tailored résumés or cover letters full of keywords. Recruiters “already know that the résumés they are reviewing may not have been written by the person” (What HR can do about candidates using ChatGPT to cheat their way into a job), and consider that an extension of long-standing practices (people using professional resume writers or templates). But cheating on assessments raises deeper issues because those tests are meant to be an objective measure of the candidate’s own skills. If AI is answering for them, the very point of the assessment is undermined.

Why are candidates doing this? Several reasons emerge, based on candidate surveys and anecdotal reports:

• Desperation or Competitive Pressure: In a tight job market – say after a wave of tech layoffs – candidates might feel immense pressure to land a job. If they fear they can’t compete, they may turn to any tool available to gain an edge (Tech Interviewers Can’t Tell When Candidates Use ChatGPT to Cheat Coding Rounds – Business Insider). A historically difficult interview process (like algorithmic coding tests) can drive even normally honest candidates to cheat if they believe “everyone else might be using AI too” or the odds are stacked against them.
• Lack of Confidence/Imposter Syndrome: Many cheaters are not outright unqualified, but insecure. A study of cheating behavior found that most people who cheat rationalize it as an exception – they know cheating is wrong but feel their situation justifies it (e.g. “this test doesn’t reflect my real abilities”). In a poll, only 5% of candidates said they cheated simply due to being lazy or unprepared, while a much larger group – 34% – felt justified in cheating because they were frustrated with or mistrustful of the hiring process. They might think the assessments are unfair or irrelevant, so using AI is a way to “level the playing field” or skip a pointless hurdle. Another chunk admit a lack of confidence in their own skills, even if they might actually be capable (Why Do Candidates Cheat? Uncovering the Root Causes and How to Address Them – CoderPad). For example, a candidate who knows the basics of coding might still use AI on a coding test out of fear that their solution won’t be optimal, or because nerves make it hard to think clearly under time pressure. AI becomes a crutch to compensate for self-doubt.
• Misaligned Incentives / Process Frustration: Some candidates view hiring assessments as bureaucratic formalities rather than meaningful evaluations. If an online test feels like a checkbox exercise (especially if poorly designed or if they’ve seen identical questions on Glassdoor), they might not feel morally wrong in outsourcing it to AI. According to one HR survey, many candidates don’t perceive using outside help as “cheating” in the same way employers do (Why Do Candidates Cheat? Uncovering the Root Causes and How to Address Them – CoderPad). Instead, they see it as doing what it takes to get through an arbitrary hurdle. In their mind, “I’ll prove myself in the real job, but this test is just gatekeeping me”. This mindset is especially common if the assessment seems only loosely related to actual job skills or if they’ve had negative experiences with hiring processes (e.g. overly long, never getting feedback, etc.).
• Opportunity and Ease: The sheer availability of AI tools makes cheating temptingly easy. Not long ago, to cheat on a coding test you’d have to find a friend or hire someone to solve it in real-time, which is risky and costly. Now, a candidate can copy-paste a question into a free AI chatbot and get an answer in seconds. The barrier to attempt cheating is low. If the platform isn’t proctored, there’s little chance of immediate consequences, so some candidates might think “why not try and see if it helps me pass?” The Reddit anecdotes show even candidates who use AI poorly (pasting screenshots without checking output) are attempting it en masse (If you are going to use ChatGPT to cheat on your interview test, at least run the code or check the reasoning question. : r/overemployed). The ease of use creates a moral hazard: candidates who might not actively seek to cheat will still use AI “just to check their work” or “to brainstorm,” sliding down a slope from help to cheating.

Implications for hiring: AI-assisted cheating threatens to erode the integrity of the hiring process. If many candidates use AI, companies may start doubting the validity of their test results—excellent test scores may no longer equate to an excellent candidate. The worst-case scenario is making a hire who looked great on assessments but actually cannot perform the job. There have already been reports (shared in recruiting communities) of new hires who passed technical screens with flying colors but then fell apart in practical work, raising suspicions that they cheated to get the job. This creates wasted time, sunk costs in training, and potential harm to team productivity if an incapable person slips through. Even before hire, there’s wasted effort: every “fake” candidate that makes it to an interview by cheating is taking up recruiter and manager time that could have been spent on genuine candidates.

Moreover, widespread cheating can force companies to rethink their processes in ways that are inconvenient for everyone. For example, some employers are considering a return to more in-person evaluations or proctored exams to ensure the person actually has the skill. The Reddit thread where an interviewer caught a candidate using ChatGPT via reflection in their glasses sparked the question: “Are we going back to 7-hour on-sites with whiteboards?” (AI in the interview : r/ExperiencedDevs). It was half-joking, but underscores a real tension: if remote/unsupervised assessments can’t be trusted, employers might impose more onerous interview formats (multiple on-site rounds, fully proctored testing sessions, etc.), which also make the candidate experience worse. It’s a lose-lose if not addressed – cheating candidates ruin the usefulness of efficient hiring tools, which then makes hiring slower and more stressful for honest candidates and recruiters alike.

There’s also a security dimension: When candidates paste company-proprietary questions or data into AI tools, they could inadvertently leak sensitive information. Many companies explicitly forbid sharing internal content with external AI services (AI in the interview : r/ExperiencedDevs). A candidate feeding a coding assignment (which might include a snippet of the company’s codebase or a problem unique to the company) into ChatGPT is effectively uploading it to a third-party system, which could violate NDAs or security policies. Thus, AI cheating isn’t just an integrity issue, but potentially a data privacy concern for employers (one reason even AI companies like Anthropic now ask applicants not to use AI on their assignments (Anthropic’s AI Irony – Anthropic’s AI Irony – Tekedia Forum – Tekedia)).

In summary, candidates using AI to cheat is a growing reality driven by pressure, perceived unfairness, and low risk. It threatens to dilute the effectiveness of assessments and can result in bad hires or a breakdown of trust in the system. Recognizing this trend, employers are now looking for ways to adapt and secure their hiring process.

Counteracting AI-Assisted Cheating: Strategies for Employers

Employers are not powerless in the face of AI-enabled cheating. To maintain the integrity of their assessments, organizations are adopting a multi-pronged approach: improving test design, bolstering proctoring and security, and exploring alternative evaluation methods that are harder to game. Below are several solutions and best practices:

1. Smarter Test Design: Design assessments that are less susceptible to AI solving. This can include:

• Use Unique or Adaptive Questions: Avoid using stock questions that have public solutions. Interviewing.io’s experiment showed that ChatGPT struggled with unique problems (only 25% success on custom questions, vs 73% on common ones) (Tech Interviewers Can’t Tell When Candidates Use ChatGPT to Cheat Coding Rounds – Business Insider). By crafting questions that aren’t easily found online, or by tweaking known questions, employers can reduce the chance that a quick AI query yields a perfect answer. Some companies now maintain large question banks and serve each candidate a random subset, so it’s harder to simply lookup answers or train an AI on the exact questions.
• Dynamic, Open-Ended Tasks: Emphasize assessments that require creativity or multi-step reasoning. For example, instead of a pure multiple-choice quiz (which an AI could answer with high accuracy), use case studies or project-based tasks that require a written explanation of why the candidate solved it the way they did. An AI might produce a solution, but the candidate may falter if asked to discuss it in depth. One approach is “work-sample with follow-up”: have the candidate complete a brief task, then in a live or recorded follow-up, ask them to explain their approach or make a small change. This often reveals whether they truly understood the solution or just regurgitated an answer. As one recruiter put it, ask open-ended questions about how they’ve done something; if the person “cannot speak to or explain” their solution, it’s a red flag (What HR can do about candidates using ChatGPT to cheat their way into a job).
• Game-based and Interactive Assessments: Certain types of tests are intrinsically harder for current AI to complete. For instance, real-time puzzles or games that require continuous human interaction can stump an AI that isn’t in the loop. An assessment expert noted that dynamic problem-solving scenarios (like interactive simulations or game-based tests) “cannot be completed by Generative AI” because they demand real-time human decision-making (Why Assessments are More Important than Ever in the Era of AI | Criteria Corp). Similarly, questions that involve interpreting images, diagrams, or audiovisual elements can be challenging for text-based AI (unless the candidate has access to advanced multimodal AI, which is less common). Some companies include a section with visual or interactive components precisely to foil text-driven cheating.
• No Single Right Answer Format: Incorporate exercises like coding design questions or architectural decisions where there isn’t one correct answer but rather a rationale that matters. AI might produce a plausible answer, but if grading is based on the quality of the explanation or the novelty of the approach, a copied answer may not score top marks. Personality and behavioral assessments already have this property: they measure consistency or fit, not correctness, which means “cheating” is less clear-cut. A candidate could ask an AI how to answer to seem, say, more conscientious, but good personality tests often have reliability scales to detect inconsistencies if someone is trying to game it. Emphasizing assessment elements where originality counts (for example, asking for past experiences or personal reflections in a video response) can surface the human element and make pure AI generation less effective.

2. Enhanced Proctoring and AI-Detection: Supervise and analyze test sessions to catch cheating in the act or flag suspicious results. Solutions include:

• Automated Proctoring Software: A range of tools now exist to monitor candidates during remote tests. These can lock down the testing browser (preventing switching tabs or copy-pasting text) and use the candidate’s webcam and microphone to watch for cheating behaviors. Advanced platforms like Glider even employ AI to track facial movements, eye gaze (to see if candidates are looking off-screen to possibly read an answer), and listen for other voices or phone usage (What HR can do about candidates using ChatGPT to cheat their way into a job). If someone tries to use a second screen or another person is in the room feeding answers, the system can flag it. Such systems aren’t foolproof and raise privacy considerations, but they are increasingly being used for high-stakes assessments. For instance, CoderPad enabled webcam proctoring in its coding tests after seeing rampant pressure to cheat in some regions (Why Do Candidates Cheat? Uncovering the Root Causes and How to Address Them – CoderPad). Even simpler, some companies require candidates to share their screens or use screen recording during the test, so any copy-paste into another app or consultation of online resources can later be reviewed.
• Plagiarism and AI Output Detection: Just as universities use plagiarism scanners, employers can run submitted code or text answers through detection tools. Platforms like Coderbyte advertise features that scan code for matches to known AI-generated solutions or check if a solution closely mirrors what ChatGPT would produce (Detect candidates that cheat with AI / ChatGPT – Help Center) (Detect candidates that cheat with AI / ChatGPT – Help Center). These systems often compare a candidate’s code against a database of common answers and even against GPT’s outputs for the prompt, flagging high similarities. While AI text “watermarking” is not fully reliable yet, unusual patterns (like an answer using an identical unusual variable name or phrasing seen in GPT outputs) can be a giveaway. In the earlier Reddit example, all cheating candidates had the same index error and same variable name in their code (If you are going to use ChatGPT to cheat on your interview test, at least run the code or check the reasoning question. : r/overemployed) – a clear sign they likely all got the code from the same AI source. Employers can deliberately plant unique markers in questions (for example, a subtle twist that any human would mention but an AI might overlook) to catch copied answers. If 10 candidates make the exact same oddly specific mistake, you have evidence of misconduct.
• Human Monitoring and Audits: Automated tools aside, some employers revert to live proctored exams for crucial tests – that is, scheduling a video call where a proctor observes the candidate via webcam as they take the test in real-time. This is more resource-intensive but can be reserved for later-stage assessments or final qualification exams (for example, a final coding challenge before an offer, done with screen share and camera on). Additionally, companies can audit a candidate’s performance by comparing test results to interview behavior. For instance, if someone aces a technical test but then struggles to answer basic follow-up questions in an interview about the same topic, it might prompt the interviewer to probe further or even re-test them under supervision. Essentially, verify consistency: one tip is to incorporate at least one assessment element in a live setting (like a brief on-the-spot quiz or asking about a concept from the test) to validate that the person who took the test is the one interviewing and that they possess the knowledge.
• Policy and Attestation: While not a technical measure, making candidates formally acknowledge an honest-testing policy can dissuade some from cheating. For example, Anthropic (a leading AI company) now requires applicants to certify that they did not use AI assistance in their submitted work, stating they want to “evaluate your non-AI-assisted skills” (Anthropic’s AI Irony – Anthropic’s AI Irony – Tekedia Forum – Tekedia). Framing it explicitly sets the expectation. Candidates know if they lie and are later caught, it’s grounds for disqualification. This likely won’t stop determined cheaters, but it may give the more cautious ones pause, especially when combined with the other detection methods above.

3. Alternative Evaluation Methods: Reduce reliance on easily cheatable tests by broadening how you assess candidates. Some approaches include:

• Structured Interviews and Live Exercises: Instead of (or in addition to) take-home tests, use structured interviews where questions are tailored to elicit concrete examples and problem-solving in real-time. For coding roles, many firms are shifting some weight back to live coding interviews (with an interviewer watching or pair programming) since it’s much harder to clandestinely use AI when you’re expected to talk through your solution. While live interviews fell out of favor in some circles due to stress on candidates, they remain one of the best ways to ensure the person in front of you is doing the thinking. Even outside of coding, structured behavioral interviews can test a candidate’s knowledge (“Tell me how you would approach X scenario” or “What does Y concept mean in context?”) in a way an AI can’t help with on the fly.
• Project-based Assessment with Presentation: Another strategy is to give a longer-term take-home project (something that might take a couple of days) but then require the candidate to present their work and answer in-depth questions about it. For example, a data analyst candidate might be asked to analyze a dataset and submit a report. They could use AI to generate charts or code, but in the follow-up presentation, the hiring team can ask why they chose certain methods, what happens if assumptions change, etc. If the candidate can’t explain or doesn’t truly understand the work, it becomes evident. This approach treats AI similarly to how calculators are treated – it’s okay if they use tools to help, as long as they grasp the solution and can take ownership of it. It flips cheating on its head: if a candidate did use AI, they need to have learned from it enough to defend the result. If they did, perhaps that’s not the worst outcome (they demonstrated quick learning); if they didn’t, the oral exam exposes them.
• Holistic Assessments (Beyond Tests): Consider other signals of skill that are harder to fabricate. Portfolios of past work, contributions to open-source projects, or published writings can be verified and give insight into a candidate’s real abilities. While these can also potentially be assisted by AI, they typically cover a longer period and are subject to public or peer scrutiny (e.g., a GitHub code repository with history is harder to fake entirely). Reference checks and back-channel recommendations are traditional but still useful tools to catch discrepancies; for instance, a reference might reveal that a candidate who aced your coding test often struggled to code independently in their last job, hinting something is off. Some companies now use trial periods or contract-to-hire stints: rather than rely on a test, they bring the candidate on for a short contract assignment and see actual performance. It’s hard to “cheat” when doing real work in a real environment with team oversight – whether the skill is there or not. Of course, this isn’t feasible for every hire, but for key roles, it can be the ultimate assessment.
• Embrace AI (Carefully): An outside-the-box idea is to allow candidates to use AI in certain assessments but design the evaluation around that. For example, prompt candidates that they may use any tool available to solve a complex problem and that you are interested in their strategy. Some forward-thinking employers have posed problems where using coding assistance or AI is expected, then during the debrief they ask the candidate to discuss how they leveraged tools. The logic here is: that on the job, people might use AI ethically to be more productive (AI-as-copilot concept). If someone can skillfully use AI to solve a problem and then articulate the solution, that itself could be a valuable skill (showing resourcefulness and adaptability). However, this approach requires a careful distinction between using AI as a helper and outsourcing all thinking to AI. It’s a fine line and not every company will be comfortable blurring it in hiring. Most currently prefer to isolate the candidate’s own ability. But in roles where AI tools will be used daily, evaluating a candidate’s ability to correctly use AI (e.g. prompt engineering, verifying AI outputs, integrating into their workflow) could become part of the assessment.

4. Education and Culture: Foster an environment of honesty and communicate the purpose of assessments. Sometimes prevention is about reducing the motivation to cheat. Companies can be transparent with candidates about how the assessment helps match them to a suitable role, and that it’s in their best interest to be authentic. Emphasize that it’s okay not to get 100% on a hard test; tests are meant to find a good fit, and cheating would only place them in a role they might struggle in. While bad actors won’t care, those on the fence (the 34% who are frustrated but perhaps persuadable (Why Do Candidates Cheat? Uncovering the Root Causes and How to Address Them – CoderPad)) might decide to compete honestly if they feel the process is fair and humane. Additionally, if a candidate is caught cheating, organizations should have a clear policy (usually immediate disqualification). Word gets around – if the industry or community learns that certain assessments have robust anti-cheating and people do get caught, it can deter attempts.

Finally, data monitoring is key. Employers should track trends in their assessment results. A sudden uptick in perfect scores or identical answers could indicate cheating patterns and prompt a refresh of questions or methods. According to one analysis by an assessment company, candidate fraud in skills tests spiked 92% after the shift to remote hiring in the pandemic, and on average 23% of candidates show some form of cheating behavior (What HR can do about candidates using ChatGPT to cheat their way into a job) (The #1 way to address candidate fraud? Skill Assessments!). Knowing this, companies can no longer treat cheating as an anomaly; they need ongoing countermeasures and updates to stay ahead.

Conclusion

Online assessments remain a valuable tool for hiring – they provide a window into candidates’ abilities that resumes and unstructured interviews alone cannot. Employers utilize a mix of technical, cognitive, and personality tests to identify the best talent efficiently, and many have seen improvements in quality of hire and diversity as a result. However, the same technology that enables broad testing (internet-based, unproctored, on-demand assessments) also opens the door for candidates to use outside assistance. The advent of AI tools that can solve problems and craft answers with superhuman proficiency has brought the issue of cheating to the forefront.

Employers are now in an arms race to preserve the credibility of their hiring assessments. Real-world cases show AI-assisted cheating is not hypothetical – it’s happening at significant rates, with candidates rationalizing it due to the pressures and perceived unfairness in hiring. If left unchecked, this trend could undermine skills-based hiring by letting unqualified individuals slip through or forcing companies to abandon convenient online tests altogether.

The good news is that solutions exist. By designing better assessments (unique, interactive, and explanation-focused), securing the test-taking process (through proctoring and answer verification), and creatively rethinking evaluation methods (like live exercises and trial projects), employers can stay one step ahead of cheaters. In this evolving landscape, there is even an opportunity to integrate AI in a positive way – evaluating how candidates use AI responsibly – but only once proper safeguards ensure that doing so truly benefits the hiring decision.

Ultimately, maintaining a fair hiring process is in everyone’s interest. Candidates who truly have the skills want a level playing field where they can showcase their abilities without being undercut by cheaters. Employers want to trust the signals they get from assessments. By being proactive and vigilant, organizations can continue to leverage online assessments to validate skills while keeping the integrity of their hiring high. In a world where both companies and candidates have AI at their fingertips, the human elements – judgment, integrity, and adaptability – will remain critical to making hiring decisions that work out for the long term.