TL;DR
Take-home assignments were already struggling before AI came along. Now they are completely broken. Candidates have always disliked them due to time demands and low response rates, and AI tools can now complete most coding assignments in under 5 minutes.
- 59% of hiring managers suspect candidates use AI tools during assessments
- Take-home assignments now test AI literacy, not actual coding ability
- Candidates increasingly skip roles requiring take-home tests
- Live, conversational interviews with integrity verification have become the reliable alternative
- Platforms like Fabric detect AI-assisted cheating through behavioral and timing analysis
Why Take-Home Assignments Were Already Failing
Before ChatGPT and coding co-pilots entered the scene, take-home assignments had a reputation problem. Candidates tolerated them, but rarely loved them.
The fundamental issue was always time. A "2-hour assignment" often stretched into 4 or 5 hours when candidates tried to polish their work, add tests, and write documentation. Senior engineers with families, side projects, or multiple job searches simply could not justify the investment. Many chose to withdraw from the process entirely.
Completion rates reflected this reality. Recruiters frequently saw 40-60% of candidates drop off at the take-home stage. For competitive roles where top candidates held multiple offers, take-home assignments became a filter that eliminated the best talent rather than identifying it.
There was also the fairness question. A candidate with a demanding day job and two children has fundamentally less time than a recent graduate with no commitments. Take-home assignments inadvertently favored availability over ability.
How Has AI Made Take-Home Assignments Obsolete?
The tools that broke take-home assignments did not arrive overnight. They evolved from simple code completion to full interview co-pilots that can solve complex algorithmic problems in seconds.
Modern AI tools like Cluely, Interview Coder, and Leetcode Wizard use invisible overlays that render directly on the candidate's screen without appearing in screen shares. The candidate sees a heads-up display floating over their IDE. The recruiter sees nothing.
These tools work through two primary methods:
1. Audio capture for verbal instructions
The software captures the system audio, transcribes instructions through speech-to-text engines, and feeds them to an LLM. Within 1-2 seconds, a structured answer appears on the candidate's screen.
2. OCR for written problems
For coding challenges, tools continuously scan defined regions of the screen, extract problem text through optical character recognition, and generate optimal solutions. The candidate never needs to copy-paste anything, a behavior that platforms could flag.
The result is that a take-home assignment designed to take 3 hours now takes 8 minutes. The assignment no longer measures coding skill. It measures whether the candidate has a $20/month subscription to a cheating tool.
According to Fabric's data from evaluating over 50,000 candidates, cheating adoption more than doubled from 15% in June 2025 to 35% in December 2025. The trend is accelerating, not slowing.
Why Can't Recruiters Just Detect AI-Generated Code?
This is the natural follow-up question, and the answer is uncomfortable: reliable detection of AI-generated code is extremely difficult.
AI code detectors suffer from high false positive rates. They flag nervous candidates who happen to write clean code while missing sophisticated cheaters who deliberately introduce typos or style variations to appear human.
Timing metadata offers some clues. Burst typing patterns, where large blocks of code appear instantaneously, suggest copy-pasting from an AI tool. But candidates have adapted. Many tools now simulate human typing speeds, introducing artificial delays between keystrokes.
The fundamental problem is that take-home assignments happen in an unobserved environment. Without real-time behavioral signals, recruiters are left guessing. And guessing wrong in either direction is costly. Reject a genuine candidate, and you lose talent. Hire a fraudulent one, and you face $50,000 or more in direct losses from a bad hire.
What Can Recruiters Do Instead of Take-Home Assignments?
The solution is not to abandon assessment entirely. It is to shift from asynchronous, unobserved tests to live evaluation methods that are naturally resistant to AI assistance.
1. Structured live coding interviews
Real-time observation makes invisible overlays far less effective. When an interviewer can see the candidate's screen, ask clarifying questions, and request explanations of specific code choices, the cheating playbook falls apart.
The key is making these interviews conversational rather than mechanical. Static questions can be pre-solved by AI. Dynamic follow-ups that probe the reasoning behind decisions cannot.
2. Pair programming sessions
Watching someone code in collaboration reveals thinking patterns that no AI tool can fake. Does the candidate ask good questions? Do they consider edge cases without prompting? How do they respond when you suggest an alternative approach?
3. System design discussions
For senior roles, architecture conversations that explore trade-offs and past experience are highly resistant to AI assistance. LLMs struggle to maintain coherent narratives about failures, specific project constraints, or lessons learned from real deployments.
4. AI-powered interview platforms with integrity detection
The most scalable approach combines the efficiency of automated assessment with behavioral monitoring that catches cheating in real time.
How Does Fabric Solve the Take-Home Assignment Problem?
Fabric takes a fundamentally different approach to technical screening. Instead of giving candidates unsupervised time with a problem, Fabric conducts conversational AI interviews that adapt dynamically based on responses.
When a candidate provides a polished, textbook answer, Fabric's AI interviewer drills deeper: "Can you tell me about a specific time you applied that in a project and it failed?" This context switching breaks the coherence of cheating tools, forcing candidates to demonstrate genuine knowledge.
While the conversation unfolds, Fabric's detection engine analyzes over 20 behavioral signals:
Timing patterns: Cheating tools introduce a consistent 3-5 second delay as they capture questions, process them, and generate answers. Genuine candidates show natural variation, answering simple questions quickly and pausing longer for complex ones.
Eye movement: When candidates read from an invisible overlay, their eyes move in straight horizontal lines from left to right, then snap back. This reading pattern differs distinctly from the upward or unfocused gaze of someone recalling information.
Language coherence: AI-generated responses follow rigid, list-based structures. Genuine speech includes natural restarts, self-corrections, and varied phrasing.
These signals combine into an integrity score that indicates probability of synthetic assistance. Based on extensive evaluations, Fabric detects cheating in 85% of cases, providing timestamped reports and full analysis so hiring teams can verify results.
The outcome is screening that scales like a take-home assignment but maintains the integrity of a live interview.
Conclusion
Take-home assignments served their purpose when candidate fraud meant copying Stack Overflow answers. That era is over.
Today, any candidate with basic technical literacy can complete a take-home assignment using AI tools in minutes. The assessment no longer measures what it was designed to measure.
The path forward combines live observation, dynamic questioning, and behavioral analysis. Candidates who demonstrate genuine skill benefit from faster, fairer processes. Hiring teams benefit from assessments they can actually trust.
For organizations still relying on take-home assignments, the recommendation is clear: retire them before your next hiring cycle.
FAQ
Can AI tools really complete coding assignments in minutes?
Yes. Modern tools like Leetcode Wizard and Interview Coder can solve most standard take-home challenges in under 5 minutes, including generating humanized explanations of the code.
Why do candidates dislike take-home assignments?
Time demands are the primary reason. Assignments designed for 2 hours often take 4-5 hours, and candidates with competing offers or personal obligations frequently skip roles that require them.
What is Fabric?
Fabric is an AI-powered interview platform that conducts conversational technical assessments with built-in integrity detection. It screens candidates at scale while identifying AI-assisted cheating through behavioral analysis.
How does Fabric detect cheating during interviews?
Fabric analyzes over 20 signals including response timing patterns, eye movement, typing dynamics, and language coherence. These signals combine to indicate whether a candidate is receiving AI assistance.
Are live coding interviews completely cheat-proof?
No assessment method is completely cheat-proof, but live interviews with dynamic follow-up questions are far more resistant to AI tools than unsupervised take-home assignments.