AI and Training: A Partnership That Actually Works

Let’s face it—training in the property assessment world isn’t always effective (or exciting). New assessors dive into dense manuals, busy mentors juggle ten things at once, and experienced staff lean heavily on memory and habit.

AI won’t magically turn every training session into a masterpiece, but it can make training sessions more effective. With AI, structured learning can be more targeted, self-training systems can better surface the right information at the right time, assessors can simulate decision-making scenarios without real stakes on the line, and mentoring can be more productive by helping the mentor to focus on what the mentee really needs advice on.

Let’s examine all four areas of learning that AI can affect.

Structured Training (Classroom Style)

We’ve all experienced structured, or classroom, training. The instructor covers and breaks down key ideas, and learners take notes, ask questions, and work through examples. Often a slideshow, handouts, and discussion or practical exercises are the key tools used by the instructor.

With AI, the instructor’s job becomes easier. For example,  instructors can use AI to pull in real-world case data, draft practice exercises, and generate examples that match the topic of the day more quickly. Some tools even capture live audio, summarize discussions in real time, and flag confusing parts—so instructors can clarify concepts on the fly and prevent students from moving on to the next topic before completely understanding the last topic.

Students can use AI after the classroom training to revisit summaries, test their recall, or dig deeper into topics. What was a one-direction lecture can turn into a back-and-forth experience.

We don’t see much of this in assessor training yet—but the benefits are obvious: less prep time, more interaction, and better retention.

Simulation-Based Training (Hands-On Practice)

Simulation-based training becomes a viable option for assessors thanks to AI.

Until recently, simulations  were mostly used in fields like healthcare and the military. To create a simulation, instructors will program scenarios that reflect what the worker will experience in the field. In the past, creating these scenarios was time consuming and tedious, and thus expensive to produce.

Scenario generation with AI is much easier. An instructor can upload anonymized case data, and the AI can generate scenarios with gray areas or tricky situations that help learners practice judgment in a safe setting based on the case data provided.  There is very little programming on the part of the instructor.

Simulation as a training tool is already gaining ground in the appraisal profession. The Appraisal Institute, a leader in property valuation education, includes simulation-based courses in its core offerings. If simulation works for fee appraisal, mass appraisal can benefit too.

Thanks to simulation, AI makes experiential learning possible; the assessor no longer has to wait for experience to teach them a lesson. Instead, they can use a simulation to “live” the experience early in their careers.

Self-Paced Training

Self-paced training has been around for decades—but AI-powered programs can now help assessors learn new tools, navigate evolving rules, and tackle complex methodologies. These platforms let people learn at their own pace and adapt to each learner’s specific needs.

The key difference between old self-training systems and new AI-enhanced training system is the degree to which the AI interprets the answers.

Previous self-learning systems would note what you missed and simply represent the questions. However, an AI system might not just note what you missed, but based on how much time you took, and other subjects you might have had difficulty with, it might determine how best to close the gap, adjusting difficulty, or adding targeted examples without waiting for a human to step in.

This adaptability reduces the setup time and tuning that used to fall on instructors or instructional designers. Now, instead of handcrafting every module, teams can launch more flexible learning tracks and trust AI to keep them effective over time.

Platforms like Coggno already offer these features, working more like virtual mentors than static learning hubs. If someone struggles with a concept, the system reinforces it with added  context before moving on. That kind of responsiveness helps training stick.

It also makes self-paced learning more scalable. Teams can roll it out easily, and learners get targeted help right when they need it—not just more content. It’s not just learn at your own pace—it’s learn with guidance that evolves with you.

Mentoring and Peer Learning

Mentors have a tough job. Often times, the mentoring process isn’t well structured and the mentor wastes time focusing on topics that the mentee doesn’t really need help with.

AI helps in a number of ways, but the biggest way AI helps mentors is by allowing them to focus on what matters. When someone finishes  structured, simulation-based, or self-paced training, the system pulls all that info together. It shows where the person got stuck, what questions came up, and which concepts didn’t fully stick. So mentors don’t have to guess where they can help— they walk in with a clear picture.

Mentoring becomes a lot more focused. Instead of covering everything, mentors can target the spots where someone struggled—missed answers, skipped steps, repeated mistakes—and build the session around that. AI doesn’t replace the mentor’s know-how; it just helps them use it more effectively.

It also takes the pressure off prep. AI can pull case files, inspection notes, and past feedback into one place, so mentors don’t waste time digging through records. That means more time for meaningful conversations.

Peer learning gets a boost, too. When a team’s getting ready for revaluation, AI can flag recurring issues, highlight tricky areas, and suggest cases worth talking through. What used to be casual chat can now become part of the learning process.

AI won’t replace the human side of mentoring—but it gives you a much better place to start.

Conclusion

Before jumping all in, consider the risks. AI tools can misinterpret what learners need, surface irrelevant examples, or miss important judgment calls. That’s why people still matter. Instructors review simulations before using them. Mentors double-check system suggestions. Humans stay in the loop. AI can support—but not replace—the experience, context, and good judgment that make training effective.

At its best, AI doesn’t just make training faster. It helps us pass along the kind of knowledge that usually takes years to learn—turning real-world instincts into clear, teachable insights.

The right AI tools won’t do your job for you. But they make your job easier—saving time, spotting gaps, and helping others learn faster.