ASL Educators’ Perspectives on AI for Enhancing Student Learning in American Sign Language Education

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Authors: Saad Hassan, Laleh Nourian, Caluã de Lacerda Pataca, Michelle L. Olson, Toni D'aurio, Kanupriya Agarwal, Syeda Mah Noor Asad, Garreth W. Tigwell, Matt Huenerfauth
Cited by: 1
References: 137

Abstract

Interest in learning American Sign Language (ASL) is growing across higher education institutions in North America, as reflected in rising enrollments. Yet this growth is constrained by limited program availability and few opportunities to practice outside the classroom. AI-based technologies show promise for supporting ASL learning, but educators – who bring essential pedagogical, linguistic, and cultural expertise – have been largely absent from conversations on the design of these tools, with prior work focusing primarily on learners. To address this, we conducted formative interviews with eleven Deaf and one hearing ASL instructor, followed by two focus groups with six Deaf educators, to examine how AI tools could support ASL education. Findings revealed priorities for technology design and considerations for integration into existing pedagogical practices, with attention to curricular, linguistic, and access factors. We offer insights for designing and researching technologies aimed at (1) providing adaptive, structured feedback on signing performance and (2) supporting immersive conversational practice with virtual signing partners.

Fields of Study & Subjects

Hearing Impairment and Communication Hand Gesture Recognition Systems Subtitles and Audiovisual Media

Publication details

Journal / source
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Pages
1–20
Publication date
13 April 2026
Language
EN