When Great Assignments Meet Bad Interviews. Rethinking Technical Hiring in the age of AI

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A familiar hiring problem is becoming harder to ignore:

A candidate submits an excellent technical assignment, but struggles badly when asked to explain it in an interview.

Is the assignment measuring ability, or is it measuring how effectively someone can use AI?

A recent discussion among founders and hiring managers surfaced several practical approaches to this problem.

The AI-Assisted Assignment Problem

Large language models have changed the nature of take-home assignments.

A candidate can now generate code, architecture diagrams, documentation, test cases, and sophisticated explanations with relatively little effort.

The problem isn't necessarily that candidates use AI. In many workplaces, they will use AI every day.

The real question is:

Do they understand what they produced?

One practical approach is to ask the candidate to make a small, unexpected change to their assignment during a live call.

It could be a minor feature, a change in business logic, an API modification, or a design adjustment.

Someone who genuinely understands the work can usually navigate the change.

Someone who primarily assembled the solution from an LLM-generated response may struggle to explain where to make the change or why the existing implementation works the way it does.

This turns the assignment from a final answer into a starting point for deeper evaluation.

Go One or Two Layers Deeper

Some candidates can produce impressive work but cannot go beyond the answer they have rehearsed.

Ask questions such as:

  • Why did you choose this approach?
  • What alternatives did you consider?
  • What are the trade-offs?
  • What happens at scale?
  • What would break first?
  • What would you change today?
  • Why is this better than another implementation?

This suggests an important distinction:

AI can help someone produce an answer. It is much harder to fake deep ownership of that answer.

💡 Better Hiring Starts With Better Assessments

The challenge isn't just interviewing — it's designing assignments that actually reveal what candidates know.

MonitorExam's AssessMe platform lets you:

  • Create auto-graded technical assignments with custom scoring
  • Add live verification rounds (ask candidates to modify, debug, or extend code)
  • Track submission patterns & time analysis
  • Combine AI-assisted work with live technical discussion
  • Measure both production ability AND understanding

Start using AssessMe for better technical hiring →

Start With Work History

One approach is to avoid starting with a coding test altogether.

Begin with a 30-minute conversation about the candidate's previous work.

Ask them to explain:

  • The product they worked on
  • Their specific responsibilities
  • The architecture
  • The team structure
  • Their development process
  • Difficult problems they encountered
  • Decisions they personally made
  • Failures and incidents
  • How they measured success

The goal isn't to test whether they remember technical terminology.

It is to establish whether they actually participated in the work they claim to have done.

A candidate who owned a system for several years should normally be able to explain its decisions, compromises, failures, and evolution in considerable detail.

Ask Candidates to Show Their Best Work

Another useful approach is to ask:

"Show me the work you're most proud of."

It could be:

  • A product they built
  • A feature they designed
  • A difficult bug they fixed
  • An automation system
  • An architecture decision
  • A process they improved

Then go deep.

  • Why did you build it that way?
  • What alternatives did you reject?
  • What went wrong?
  • How did you debug it?
  • What was the hardest part?
  • What would you change now?
  • Why is the final solution better than the alternatives?

This creates a different kind of interview.

Instead of asking candidates to solve an artificial problem, you're asking them to demonstrate ownership of a real problem they already solved.

That can be a powerful signal.

The Two-Round Model

Hiring doesn't necessarily need eight interview rounds.

A relatively simple process could be:

Round 1: Work History and Technical Discussion

Spend around 30 minutes understanding the candidate's previous work, responsibilities, decisions, and problem-solving ability.

Round 2: Practical Assignment

Give the candidate a realistic problem relevant to the role.

Final Validation

Discuss the assignment live and ask the candidate to modify, extend, debug, or redesign part of it.

This combination tests three different things:

Experience → Production Ability → Understanding

The assignment tells you what the candidate can produce.

The live discussion tells you whether they understand what they produced.

⚡ Automate Assignment Grading, Focus on What Matters

Manual grading of technical assignments takes time away from meaningful conversations with candidates.

What if grading happened automatically?

MonitorExam's AssessMe includes:

  • Auto-grading for code submissions (syntax, logic, test case validation)
  • Instant scoring & performance analytics
  • Time-tracking to detect suspicious submission patterns
  • Live verification workflow (built-in tools for asking candidates to modify their work)
  • Audit trail for compliance & hiring records

You focus on evaluating understanding. The platform handles the rest.

See how AssessMe streamlines technical hiring →

Don't Confuse Communication Skills With Technical Ability

Some technically strong candidates struggle to explain their work in English, but become much more articulate when communicating in their preferred language or in a more informal conversational setting.

This matters particularly in technical hiring.

A poor English answer does not automatically mean poor technical understanding.

If the role does not specifically require exceptional English communication, interviewers should be careful not to accidentally measure language fluency instead of engineering ability.

The better test is whether the candidate can explain the underlying concept clearly in a language and setting where they are comfortable.

What About Trial Periods?

One alternative to multiple interview rounds is to evaluate candidates through a structured trial period.

Any such arrangement should be structured fairly, comply with applicable employment laws, and ensure candidates are compensated appropriately for productive work.

The underlying idea is worth considering:

Real work can sometimes reveal more than simulated interviews.

A candidate's ability to collaborate, learn, communicate, handle ambiguity, respond to feedback, and consistently deliver can be difficult to measure through a sequence of interviews.

AI Doesn't Make Assignments Useless

The conclusion shouldn't be that take-home assignments are obsolete.

Instead:

The assignment needs a verification layer.

AI is increasingly becoming part of the normal software development workflow.

Rejecting candidates simply because they use AI may eventually become as unrealistic as rejecting candidates because they use Stack Overflow or an IDE.

The better question is whether they can use these tools effectively while retaining ownership and understanding.

A modern technical hiring process might therefore look like:

Previous Work → Practical Assignment → AI-Assisted Production → Live Modification → Deep Technical Discussion

The objective isn't to catch candidates using AI.

It is to distinguish between:

"I generated this solution."

and

"I used whatever tools were available, but I understand this solution well enough to defend it, modify it, debug it, and improve it."

That distinction is likely to become one of the most important skills in technical hiring.

The Simplest Test

If you only change one thing in your hiring process, try this:

After a candidate submits an assignment, don't just ask them to explain it. Ask them to change something.

A small, unexpected change can reveal more than another hour of theoretical questions.

Because ultimately, hiring isn't about determining whether someone can produce an impressive answer.

It's about determining whether they can own the work behind the answer.

🎯 Build Your Own Verification System

You now have the framework. You know what questions to ask. You understand the difference between AI-assisted work and genuine ownership.

The challenge: implementing this consistently across dozens of candidates, managing time zones, documenting decisions, and keeping records.

MonitorExam's InterviewME handles the infrastructure:

  • Live coding environment with live modification testing
  • Built-in whiteboard for architecture discussion
  • Recording & transcript for hiring records (with consent)
  • Integration with your existing assessment workflow
  • Analytics on candidate performance patterns

Focus on the conversation. Let the platform handle the logistics.

Start your free trial of MonitorExam →

15 exams/interviews free every month. No credit card required.

Key Takeaways

  1. Assignments alone don't prove understanding — add a verification layer with live modification requests
  2. Work history is a powerful signal — ask about previous systems, decisions, and failures
  3. Show your best work — candidates should demonstrate ownership of real problems they solved
  4. Two rounds might be enough — work history + assignment + live discussion can replace longer interview loops
  5. AI doesn't break hiring — it changes what you're testing (not whether they use AI, but whether they understand what they produced)
  6. Automate the busywork — manual grading and logging wastes time; let tools handle it

The future of technical hiring isn't about preventing AI use.

It's about measuring the one thing AI can't fake: genuine ownership of the work.

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Tags: technical-hiring, assessments, ai, candidate-evaluation, interview-process

Written by: MonitorExam
Updated: 12 August 2026

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