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

  • Saad Hassan*
  • , Laleh Nourian
  • , Calua de Lacerda Pataca
  • , Michelle M Olson
  • , Toni D'aurio
  • , Kanupriya Agarwal
  • , Syeda Mah Noor Asad
  • , Garreth W. Tigwell
  • , Matt Huenerfauth
  • *Corresponding author for this work
  • Tulane University
  • Rochester Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.
Original languageEnglish
Title of host publicationProceedings of the 2026 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery (ACM)
Pages1-20
Number of pages20
ISBN (Print)9798400722783
DOIs
Publication statusPublished (VoR) - 13 Apr 2026

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