In the ever-evolving technical job market, engineering and talent acquisition leaders are confronted with the challenges of balancing effectiveness, standardization, and scalability in their hiring processes. Insert candidates using a plethora of AI-enabled solutions to aid them in the job search process to throw off your confidence on the question of effectiveness. How effective is your hiring process in truly measuring a candidate’s skill set and alignment to your role, ultimately impacting their performance on the job and your organization’s performance? 

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Chances are, you have had discussions internally about how candidates are using AI to navigate their job searches or solve your technical interviews, and what you can do about it. We have heard it all in terms of AI use from hyper-tailoring resumes to job descriptions to relying on AI for shortcuts during technical interviews . While many companies aim to completely block candidate’s from using AI as part of the hiring process, this approach falls short in terms of acknowledging candidate ingenuity and a general shift in AI use on the job to increase productivity and efficiency. 

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In this article, we delve into the limitations of AI-use in technical interviews, how to spot AI-use in interviews, and explore how Byteboard is thinking about the future outlook. 

What are the limitations of AI-use in technical interviews? 

We asked an AI chatbot and virtual assistant this question and couldn’t agree more: ‍

• ‍Over-reliance on AI: Candidates may depend too much on AI tools to solve problems, which can hinder their ability to demonstrate real problem-solving skills during live interviews.‍‍
• ‍Limited Creativity: AI tools might generate standard solutions, reducing the opportunity for candidates to showcase unique or creative approaches.‍
• Bias in Algorithms: AI tools may reinforce biases in problem selection or feedback, potentially disadvantaging certain candidates.‍
• Lack of Soft Skills Evaluation: AI tools primarily focus on technical skills and may overlook essential communication and teamwork abilities.‍
• Ethical Concerns: Excessive use of AI could blur the line between assistance and dishonesty, raising concerns about fairness.

Meta use of AI for content development aside, it is not AI-use in interviews that’s broken but rather it’s issues further up-funnel that bring a mixed bag of candidates to live interviews (1) and the types of questions that candidates are being asked to answer (2). 

How to spot AI-use in an interview?

With the advent of AI-use in interviews, getting a correct or model answer feels less relevant to a successful, strong interview. What’s most important is the candidate’s ability to prioritize interview responses within specific time constraints, the process by which they got to a solution or response, and their overall ability to explain the decision making process throughout the interview. 

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For project-based interviews, we’ve found: 

• Inconsistencies in a candidate’s knowledge and skills – Candidates show inconsistent signal across specific skill areas across an interview (e.g., variable performance across different questions measuring similar skills). 
• Lack of personalization – Candidates lack the ability to bring in their personal expertise or provide examples of how they have implemented specific technologies or solved certain problems in the past. 
• Inability to explain the problem solving process – Candidates are unable to demonstrate prioritization throughout interviews and explain the overall problem solving journey and how they got to a solution. 

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At Byteboard we monitor candidate responses for originality (e.g., similarity to prior submissions received), rigid prose, and overall syntax (e.g., variable naming conventions and comments). 

Building the future of technical interviewing at Byteboard 

Looking ahead to the future, technical interviews should continue to move towards more realistic (and holistic) representations of the day-to-day of a software engineer (insert relevant role). Byteboard CoreEval focuses on integrating evaluation of durable skills such as trade-off analysis and communication and technical skills such as writing efficient, clean code and data structures and algorithms into a two-part project-based interview. 

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At Byteboard, we are investing in development of the “technical assessments of the future.” With the pace of innovation and constant introduction of new technologies and tools, we are focusing on building an interview experience with a more authentic context that reflects a candidate’s ability to do the job well while using the tools that are familiar to them including AI-based tools for generating and debugging code.

_{Want to learn more about our next-gen technical assessments?} 

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