Coding tests play a pivotal role in tech recruiting, shining a spotlight on the prowess of each developer. These assessments are tailored to mirror real-world challenges, ensuring that a candidate isn’t just versed in theory but can truly bring code to life in practical scenarios.

But those real-world scenarios are evolving. 

With the advent of AI tools like ChatGPT and GitHub Copilot, we’re witnessing a profound shift in the development landscape. Just as developers once leaned heavily on StackOverflow or turned to Google for quick insights, they now frequently consult these AI companions for guidance and optimization. In fact, 82% of developers now use AI tools in their development process.

This transformation begs a pressing question for hiring managers and tech recruiters: If AI tools have become so intrinsic to modern development, shouldn’t our coding assessments adapt to this new standard? The debate on allowing access to Google during tests has been around for a while, but introducing AI into the mix adds a fresh, more nuanced dimension to the conversation.

Every company that hires developers will have to grapple with this question. And it’s not a “let’s-put-it-off-for-later” kind of issue. The answer could redefine tech hiring as we know it.

The Changing Nature of Development

Gone are the days when a developer’s world was limited to their integrated development environment (IDE), a few chosen frameworks, and perhaps a sprinkling of API documentation kept open in a browser tab. Today, software development is more expansive and dynamic, and AI tools are making a recognizable mark on it.

Consider GitHub Copilot, for instance. It’s not just an auto-suggest tool that helps developers complete a line of code. It’s a co-pilot in the truest sense, offering solutions, predicting the next line, and sometimes even educating developers on best practices. Imagine being mid-way through a tricky function and having a tool that doesn’t just help you complete it but suggests an optimized way to achieve the same outcome. It’s like having a seasoned developer whispering expert advice in your ear.

And then there’s ChatGPT. Let’s say a developer is grappling with a peculiar bug, and the usual forums don’t have the answer. ChatGPT is there, ready to brainstorm and debug with them, actively contributing to the problem-solving session.

These examples aren’t mere hypotheticals; they reflect the evolving day-to-day reality of developers. According to a recent Github survey, 70% of developers say AI coding tools will offer them an advantage at work and expect better code quality, completion time, and resolving incidents to be the greatest benefits. By seamlessly integrating AI tools into their workflow, they can be more efficient, explore multiple solutions, and even learn on the job. It’s not about replacing human skills or intuition; it’s about enhancing them.

Now, this doesn’t mean every line of code a developer writes will be assisted by AI. But it does indicate a shift in the ecosystem. As developers continue to integrate these tools into their repertoire, the boundary between human expertise and AI-enhanced skills becomes a bit fuzzy.

For hiring managers and tech recruiters, this raises an exciting yet challenging question. How do you discern a developer’s core skills from their proficiency in working with AI tools? And, more importantly, should there even be a distinction?

The Real Question for Hiring Teams

The crux of the matter isn’t just about the tools developers have at their disposal or how the art of coding is evolving. It boils down to two central questions: 

• What skills are we truly trying to assess?
• How do we ensure that our tests are still relevant in the face of innovation?

Let’s draw a parallel. A decade or so ago, a common debate in tech recruitment circles revolved around allowing candidates to use Google during coding assessments. The crux of that debate was clear: in the real world, developers wouldn’t be restricted from accessing resources. Why then create an artificial barrier in tests? Fast-forward to today, and we’re facing a similar predicament, albeit on a more sophisticated scale.

If we recognize that AI tools are not just auxiliary aids but integral parts of a developer’s toolkit, then the debate shifts. It’s no longer about whether candidates can use AI tools like ChatGPT during assessments; it’s about whether they should. 

To illuminate the point further: If a developer’s daily job involves collaborating with an AI tool to optimize workflows, debug more effectively, or generate parts of code, shouldn’t their proficiency in using these tools be part of what we assess? It’s akin to evaluating a carpenter not just on their ability to hammer a nail but also on their skill in using a modern nail gun.

This is the real dilemma facing hiring managers and tech recruiters. In an era where the tools of the trade are in flux, the challenge is to craft assessments that capture both the timeless essence of coding and the contemporary nuances brought about by AI.

Approaches to Integrating AI in Coding Tests: Pros and Cons

As hiring teams grapple with the evolving role of AI in development, they’re presented with a range of options on how to incorporate these tools into their assessment process. Each approach comes with its own set of advantages and challenges. The key thing to remember is that creating an AI strategy isn’t about finding the right answer. Rather, the goal is to embrace AI on your own terms with an approach that works for your organization. 

Let’s break down the primary strategies.

Prevent the Use of AI

In this approach, candidates are given a traditional coding environment without access to external AI tools. It’s the old-school method where one’s coding chops are tested in isolation. Proctoring tools are often employed to ensure the candidate isn’t accessing external resources, and plagiarism detection systems are on the lookout for copy-pasted solutions.

 Pros: 

• Clarity of Assessment: You’re certain that solutions stem from the candidate’s raw knowledge and skills.
• Standardization: All candidates face the same conditions, ensuring fairness.

Cons: 

• Unrealistic Scenario: It might not fully capture the nuances of a real-world coding job where all tools are accessible.
• Missed Skill Evaluation: The approach might overlook a developer’s proficiency in working with AI tools.

Allow Limited Use of AI 

In this scenario, you might have developers work in a controlled environment where the IDE comes with a built-in AI assistant, acting as a pair programmer. This assistant can suggest optimizations or guide the test-taker through complex problems. However, external AI tools or search engines remain off-limits. Proctoring tools monitor the test-taking process and how candidates work with the AI assistant. Meanwhile, plagiarism detection tools watch for instances where candidates receive unauthorized external help.

Pros:

• Relevant Skill Assessment: This mirrors a modern development workflow, assessing the synergy between the developer and AI.
• Controlled Environment: The built-in AI ensures candidates have a standardized AI experience.

Cons:

• Gray Areas: Defining “limited use” might pose challenges, leading to assessment discrepancies.
• Balancing Act: You could run the risk of candidates leaning too heavily on the AI, making it hard to evaluate their independent skills.

Allow Complete Use of AI

Here, candidates are let loose in a fully-equipped digital playground, complete with AI tools like GitHub Copilot or ChatGPT. The assessment evaluates not just the final solution but the process — how effectively a candidate collaborates with AI. To counter potential misuse, a variety of advanced plagiarism detection systems work in tandem.

Pros:

• Holistic Evaluation: Recognizes the full spectrum of modern coding, from raw skills to AI-enhanced development.
• Push for Innovation: With AI at their side, candidates might come up with out-of-the-box solutions.
• Real-World Environment: This is as practical as it gets, with developers solving problems the exact same way they would on the job.

Cons:

• Attribution Challenges: Discerning the candidate’s contribution versus AI’s could be tricky.
• Integrity Concerns: With more tools available, ensuring authentic solutions becomes paramount.

Each of these approaches brings forth a distinct vision of what coding assessments should look like in the age of AI. It’s worth noting that companies can use a combination or blend of these approaches in their hiring process. For example, a company could go with a more restrictive approach for initial screening assessments, and then allow for open use of AI for a smaller candidate pool in the interview process.

Upholding Assessment Integrity in the Age of AI

The integrity of coding assessments is a cornerstone of effective tech recruitment. In a world where AI tools can significantly influence the output, ensuring that a candidate’s work is genuine, original, and indicative of their skills becomes paramount — regardless of how you decide to assess their skills. However, the methods used to secure the integrity of assessments will look different for every company, depending on how they choose to embrace AI.

Leveraging Proctoring Tools

In scenarios where you either prevent or limit the use of AI, using proctoring tools becomes essential. These tools can monitor a candidate’s screen, browser tabs, and even their webcam to ensure that they aren’t accessing unauthorized resources. Modern proctoring software has grown sophisticated enough to detect suspicious behavior and flag it for review, ensuring a fair testing environment.

Investing in Plagiarism Detection

Monitoring for plagiarism has always been essential in coding tests. However, the introduction necessitates a greater focus on plagiarism detection.

Before the spread of AI, the industry standard for plagiarism detection relied heavily on MOSS code similarity. In addition to producing higher false positives rates, this approach also unreliably detects plagiarism originating from conversational agents like ChatGPT. That’s because ChatGPT can produce somewhat original code, which can circumvent similarity tests.

The new industry standard for securing tests is an AI-powered plagiarism detection system. HackerRank’s AI model – which is currently the only one in the market – can track dozens of signals across three categories — coding behavior features, attempt submission features, and question features. And it can analyze them to calculate the likelihood of suspicious activity. This upholds transparency, fairness, and equity, regardless of how integrated AI is into the testing process. 

Educating the Candidates

Finally, setting clear expectations is crucial. Before the assessment, candidates should be thoroughly briefed about the tools they can use, the expectations regarding collaboration with AI, and the consequences of unfair practices. An informed candidate is less likely to breach assessment integrity.

By combining technology with transparent communication, companies can navigate the challenges posed by AI in coding assessments. The goal remains unchanged: to accurately gauge a candidate’s skills in an environment that’s both fair and indicative of real-world scenarios.

Embracing the Future of Coding Assessments

As technical skills evolve, so too must our methods of evaluating technical talent. The rise of AI tools like ChatGPT and GitHub Copilot isn’t merely a passing trend; it signifies a shift in how developers approach their craft. As such, the debate over their inclusion in coding tests is more than just a pedagogical question — it’s a reflection of the changing definition of what it means to be a developer.

For hiring managers and tech recruiters, the challenge lies in balancing tradition with innovation. The decision isn’t binary; as highlighted, there’s a spectrum of approaches, each with its merits.

Whatever path companies choose, the core principle remains the same: assessments should be a genuine reflection of on-the-job skills and scenarios. AI is undeniably a part of that picture now. But, as with all tools, it’s about how you use it. 

This article was written with the help of AI. Can you tell which parts?