We’ve been hard at work designing our AI-augmented assessment crafting engine and one of its most important capabilities will be its ability to create problems using many different problem types. Over the last few weeks in this space, we’ve examined problem types and why a particular type of problem may be used in a certain situation. Step into the workplace and you will notice something important: decisions don’t come packaged with four neat options on a bubble sheet. Employees must recall facts, solve problems, make judgments, and create solutions, often in uncertain conditions. If education and training are to prepare people for that reality, assessments must mirror the challenges that are likely to be encountered.
Traditional tests have their place, but workplace learning requires more than recall. An employee in customer service may need to remember company policies, but they also need to apply those policies in sensitive situations. An engineer must recall formulas, but the real value lies in applying those formulas to design systems and then creating solutions that have never been attempted before. These layers of performance echo the range of problem types used in education, from simple recall to higher-order tasks and executive functioning.
The problem is that adult training often stops at the first layer. Corporate learning programs often rely on multiple choice because it is quick and easy to administer and may not have experience crafting assessments using other problem types. While this method captures whether employees have memorized information, it does little to test whether they can apply knowledge in the real world. The result is a gap between training and practice, where learners may pass assessments but struggle when confronted with the messy realities of work.
This is where scenario-based and project-driven assessments work very well. Just as flight simulators allow pilots to practice safely before taking the controls of an actual aircraft, simulations and role-plays allow employees to test their skills in realistic contexts. Handling a virtual customer complaint, making a judgment call in a branching case study, or collaborating on a simulated project provides insight into judgment, problem-solving, and adaptability: skills that multiple choice alone cannot evaluate effectively.
Many companies see the value in this approach. By embedding adaptive assessments into workplace training, it becomes possible to track not only what employees remember, but how they respond in practical scenarios. AI tools such as QueryTek make this scalable. They can generate adaptive problems that evolve with each learner, offering quick checks when memory must be tested and more complex tasks when judgment and creativity are needed. For employers, this means more than compliance training and a more accurate view of workforce readiness.
And, of course, the practical outcome is stronger, more effective teams. Employees are trained to recall procedures and, importantly, are better prepared to apply them under pressure, adapt to unexpected challenges and even innovate new approaches. When organizations grow and the technological landscape shifts, they need to deploy and scale this kind of adaptive, realistic assessment to keep competitive.
When assessments are designed with real-world performance in mind, they become more about preparation. They become tools that build capability. In the classroom this means better learning and in the workplace, it means a workforce better capable of addressing challenges both known and unknown.
