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Enhancing American Sign Language Learning with LLM-Assisted Feedback: A Comparative Study with Traditional Methods

Lookup NU author(s): Dr Lei ShiORCiD

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Abstract

We evaluate LLM-assisted learning for American Sign Language acquisition by comparing GPT-4o-powered real-time feedback informed by gesture recognition, against traditional image/text static instruction. In a study with 20 participants, both methods improved performance, but the LLM group showed greater gains in challenging signs and increased engagement with complex content. Effect sizes suggest meaningful advantages for LLM support, despite the limited statistical significance of the findings.

Publication metadata

Author(s): Wang J, Ivrissimtzis I, Li Z, Shi L

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 20th IFIP TC13 International Conference on Human-Computer Interaction (INTERACT 2025)

Year of Conference: 2025

Pages: 210-215

Online publication date: 12/09/2025

Acceptance date: 26/04/2025

ISSN: 0302-9743

Publisher: Springer

URL: https://doi.org/10.1007/978-3-032-05008-3_40

DOI: 10.1007/978-3-032-05008-3_40

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783032050076

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