AI Meets Simulation: What SLP Educators Can Learn From Healthcare Training

If you're teaching in a field like speech-language pathology, you've probably felt the push to bring more simulation into your curriculum—whether that's standardized patients, role plays, or virtual reality. But with tight budgets, limited space, and the need for real-time feedback, it’s not always easy to pull off. That’s where artificial intelligence (AI) is starting to show up in big ways.

I recently dug into an article by Afzal and colleagues (2024) that dives into how AI is being used in simulation-based education (SBE), particularly in medical training. And while the focus is broader than SLP, a lot of what they found is highly relevant to how we train future clinicians in our field.

The Big Picture: Why AI in Simulation?

One of the biggest promises of AI in healthcare education is that it can scale and personalize training in ways we just can’t do with humans alone. The article outlines several ways AI is already being used in medicine: intelligent tutoring systems, virtual standardized patients, real-time feedback during simulations, and even automated scoring for clinical performance.

Imagine a world where your students can practice counseling or diagnostic interviews with an AI-driven patient that adapts to their tone and word choice in real time. Or picture a simulated therapy session where the AI flags missed cues, offers alternative prompts, and tracks a student's growth across multiple sessions—all without a supervisor sitting behind the glass.

What’s in It for SLP?

Even though the article doesn’t mention speech-language pathology by name, the takeaways feel especially useful for us. We rely heavily on simulation—both formal and informal—to teach complex skills like bedside swallow evaluations, patient counseling, and interprofessional collaboration. But our time and staffing are limited. AI could help us stretch that supervision bandwidth by offering more structured, consistent practice opportunities that students can do on their own.

In fact, the article points out that AI-enhanced simulation might be especially helpful for "deliberate practice"—the kind of repeated, focused skill-building that’s essential for learning but hard to squeeze into a packed semester. I’m already thinking about how we could use this in our graduate fluency course, where role-playing tough parent conversations or working through emotional counseling scenarios could be supported with AI before students ever walk into a real session.

What’s the Catch?

Afzal et al. are clear that AI isn’t magic, and it’s definitely not ready to replace human educators. One of the biggest challenges is accuracy. AI systems can still misinterpret input or provide misleading feedback, especially if they weren’t trained on diverse or representative datasets. And then there’s the big issue of trust: If a student doesn’t believe the feedback is reliable or feels like they’re being “graded by a robot,” they’re not going to buy in.

There’s also the ethical layer. Who owns the data? What happens if a simulation records sensitive content? These are conversations we have to start having now, not after the tech is already embedded in our programs.

Where We Go From Here

As someone working in a university clinic and teaching future clinicians, I don’t think AI is going to take over SLP education. But I do think it can help us scale what we’re already doing well. We just need to approach it like any other tool: thoughtfully, critically, and with student learning at the center.

So the next time you’re debating how to give your students more reps without burning out your team, don’t dismiss AI. Just make sure you know what you’re getting—and what your students are learning.

Reference:

Afzal, M. T., Khan, R. A., & Zeshan, F. (2024). Artificial intelligence, applications and challenges in simulation-based education. Journal of Taibah University Medical Sciences, 19(2), 214–222. https://doi.org/10.1016/j.jtumed.2023.12.004

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