Artificial intelligence isn’t coming for our jobs … at least not yet. But that doesn’t mean it should be ignored. In fact, figuring out where AI best fits on your test development team is one of the most important strategic decisions organizations can make right now.
AI is powerful, fast, and surprisingly good at some things. It’s also inconsistent, limited in key areas, and nowhere near ready to replace human expertise. The key is to treat it not as a replacement, but as a collaborator: one that can assist, accelerate, and even inspire, if it’s placed in the right role.
The Exam Development Ecosystem
Developing a high-quality exam is a team sport. It brings together content experts, exam developers, and psychometricians, each with their own skills and responsibilities:
Subject Matter Experts (SMEs) contribute deep domain knowledge to ensure items are relevant, accurate, and aligned with professional practice.
Exam Developers focus on blueprinting, item writing, test form construction, and quality control.
Psychometricians ensure the statistical integrity of the exam: designing scoring models, conducting data analyses, and validating inferences.
Each of these roles plays a different but essential part in building a defensible and effective assessment. So, where does AI fit in? The answer depends on what AI can and can’t do.
What AI Does Well (and Not So Well)
At its core, modern AI, especially large language models like ChatGPT, is built to generate and summarize text. These capabilities make it useful for a range of text-heavy tasks, especially in the early stages of exam development. AI can:
But there are important limitations to keep in mind:
That said, AI can support surprisingly complex goals if you can break them down into clear, structured steps. The more precisely you describe a task, the more likely AI is to produce something useful. It thrives in well-defined workflows, especially when paired with human review.
AI as an Assistant Exam Developer or SME
Given those strengths, AI is best thought of as an assistant to exam developers and SMEs. It can speed up routine work: generating item drafts, exploring alternate phrasings, adapting items to new formats, and suggesting variations. It’s particularly helpful when SMEs are busy or fatigued, or when a team needs to rapidly iterate on ideas.
Think of AI as a fast, tireless co-author, one that helps with brainstorming, language refinement, and versioning. But it still needs human guidance. It won’t catch factual errors, recognize subtle bias, or ensure that content aligns precisely with your blueprint.
In other words, it makes a strong assistant, but not a replacement.
But Is It a Psychometrician?
It’s easy to assume that AI’s technical capabilities mean it belongs in the psychometrician’s domain. After all, it’s algorithmic, fast, and fluent in technical language. But that assumption confuses surface-level ability with core competency.
Most AI models don’t conduct psychometric analyses. They can’t calibrate items using IRT, analyze item performance with CTT, assess test reliability, or optimize forms for content balance and measurement precision. They can describe these processes (often well), and they might even write code snippets to help automate parts of them. But they don’t analyze results, flag misfitting items, or design measurement strategies.
In this sense, AI might serve as a helpful assistant data analyst. It’s able to generate R or Python scripts based on prompts, but not to verify the statistical meaning of the results. It lacks the interpretive insight and quality assurance needed to make psychometric decisions.
And it’s worth noting that when AI is someday ready to take over more duties of a psychometrician, it will have to analyze data using Python or R scripts, just like your psychometrician.
Conclusion: Right Tool, Right Role
AI isn’t a psychometrician, but it can be a capable assistant exam developer or SME, if used wisely. It’s not a replacement for expertise, but a force multiplier for the people who already have it.
The key to using AI effectively in testing isn’t automation, it’s collaboration. When you give AI the right instructions and the right role, it can make your test development team faster, more creative, and more efficient.