SHS Calendar

View Full Calendar

SHS Proseminar

Event Type
Seminar/Symposium
Sponsor
Department of Speech & Hearing Science
Location
Lincoln Hall, 702 S. Wright St, Urbana, IL 61801, Room 1002
Date
Oct 17, 2025   12:00 - 12:50 pm  
Speaker
Tiffany Hutchins, Ph.D., Associate Professor, University of Vermont, Communication Sciences and Disorders Danielle Kent, M.S., CCC-SLP, PhD Student, University of Vermont, Communication Sciences and Disorders
Contact
Pamela Hadley
E-Mail
phadley@illinois.edu

Affirming Strengths and Questioning Claims: Rethinking NLA/GLP in Speech and Language Therapy 

Tiffany Hutchins, Ph.D., Associate Professor, University of Vermont, Communication Sciences and Disorders

Danielle Kent, M.S., CCC-SLP, PhD Student, University of Vermont, Communication Sciences and Disorders

 

Abstract: Natural Language Acquisition (NLA) and Gestalt Language Processing (GLP) have recently gained rapid attention in speech-language pathology as frameworks for understanding autistic communication. Although widely promoted in online, clinical, and parent communities, these approaches raise substantive concerns that require critical examination. This presentation highlights the importance of neurodiversity-affirming care and situates the NLA/GLP debate within broader questions of evidence-based practice. We begin by noting areas of common ground between advocates and critics, including shared commitments to supporting autistic voices and fostering communicative growth. At the same time, we identify points of contention raised by researchers and practitioners who have rigorously challenged NLA’s strongest claims. Specifically, we outline definitional, conceptual, and empirical problems that continue to undermine NLA’s theoretical and clinical validity. Ultimately, the talk calls for a shift toward neurodiversity-affirming practice grounded in robust evidence. We argue for flexible, responsive approaches that foreground autistic children’s communicative strengths rather than obscuring them, and that move beyond prescriptive models toward more collaborative and context-sensitive supports.

link for robots only