Unlock the Magic of Language Samples Again—with a Little Help from AI

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AI for language sample analysis in speech therapy child talking and adult listening Rakovic Speech and Language Chat

As I celebrate my 46th year in the field of speech and language pathology, I find myself more committed than ever to continued learning and evolving with the needs of the students and professionals I serve. The field has transformed dramatically since I began, and while some foundational practices have stood the test of time, others—like language sample analysis—have become increasingly difficult to implement amidst ever-growing caseloads and documentation demands. I was intrigued by the thought of how to use AI for language sample analysis in speech therapy

That’s why I found Principles for AI-Assisted Language Sample Analysis by Dr. Darin Woolpert on speechpathology.com to be not only timely but profoundly practical. This mini-course reenergized my understanding of how technology, specifically AI, can help us return to core best practices like language sample analysis—even in the face of limited time and resources.

Dr. Woolpert’s presentation offered a clear-eyed look at how AI can serve as a helpful assistant rather than a replacement for clinical expertise. He walked participants through the ways in which large language model (LLM) chatbots—like ChatGPT—can be used to draft transcriptions, identify basic errors, and segment utterances, all while emphasizing the importance of SLP oversight, editing, and ethical responsibility.

AI for language sample analysis in speech therapy woman overwhelmed by paperwork Rakovic Speech and language Chat

What resonated most with me was the notion of AI as a time-saving collaborator. Like many SLPs, I’ve drifted away from routine use of language sample analysis because of its time-consuming nature. Dr. Woolpert acknowledged this reality while also demonstrating how AI, if thoughtfully used, can support us in reviving this gold-standard tool in our diagnostic process.

In addition to his pragmatic guidance, Dr. Woolpert reminded us of our professional role. AI is not the clinician—we are. We are the ones with the brain, the license, and the responsibility to evaluate and interpret. AI may assist, but it does not replace our judgment.

To Dr. Woolpert, I extend my sincere thanks for a thoughtful, forward-thinking presentation. You provided not only a roadmap for integrating AI into one of our most valuable assessment tools, but also a model for how to embrace innovation without compromising clinical integrity. I am excited to try to utilize AI for language sample analysis in speech therapy

This course reinvigorated my commitment to blending evidence-based methods with emerging technology—an approach I’ll proudly carry forward into my next year of practice and beyond.