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What Skills Can Be Checked in an AI Interview?
A good techie isn’t just someone who writes code that works. It’s someone who can think clearly, solve real-world problems, and explain their choices. That’s what hiring teams look for, and that’s exactly what the Geektrust AI Interviewer is designed to evaluate.
It doesn’t just check if someone solved the problem. It looks at how they approached it, how they communicated their thinking, and how ready they are for the role.
What skills does it assess?
The AI Interviewer supports a wide range of roles, from freshers to senior developers. It can be used to assess Frontend, Backend, Fullstack, QA, and DevOps candidates, with support for experience levels up to 10 years (we’re still working on getting the nuances right for 10+ years candidates).
Skills covered span most modern tech stacks. For roles using Java, Python, JavaScript, TypeScript, Go, and frameworks like React, Spring Boot, and Node.js, we support both technical Q&A and coding rounds. For C#, Angular, and Vue, we currently support structured Q&A interviews. Coding support for these is being added soon.
Each interview can include coding challenges, logic-based problem solving, API and DB reasoning, CI/CD knowledge, debugging scenarios, and system design, depending on the role. Communication clarity, both written and verbal, is evaluated through typed and voice-based responses.
How are skills evaluated?
The system begins with role-specific templates and asks targeted questions around fundamentals, problem-solving, and design. Based on the candidate’s responses, the AI follows up with deeper questions or requests for clarification, much like a real interviewer would.
Answers are scored using time tested assessment frameworks, static analysis, test case results, reasoning logic, and LLM-powered interpretation. This helps create a complete picture that reflects clarity, depth, and thought process, not just correctness of the code.
What does the report show?
Hiring teams receive a structured report that includes:
- Skill and sub-skill level scoring (e.g. JS theory, HTML/CSS, React coding, communication)
- Strengths and improvement areas
- What went well and where the candidate struggled
- Proctoring results and session behavior summary
- Full transcripts along with video recordings
This allows both recruiters and engineers to make faster, more informed decisions.
What about soft skills?
The interview also captures a candidate’s ability to explain code and thought process, walk through projects, and respond to feedback.
Is it flexible?
Yes. You can use predefined templates or build custom ones based on role, seniority, and tech stack. The interview depth can be adjusted depending on whether you’re hiring a junior QA or a senior backend engineer.
What this means for your team
You get a 360-degree view of a candidate’s capabilities without scheduling a single interview. The AI handles the early rounds with depth and structure, so your team can focus on the final conversations that lead to better hires.