Using the Potential of GenAI Tools for Accessibility
Paper in proceeding, 2026

The increasing use of Generative artificial intelligence (GenAI) and related tools in computing education offers new forms of on-demand support but also raises concerns about accessibility and equity. Evidence suggests that learners with disabilities may struggle with verbose, unstructured, and biased GenAI outputs, potentially exacerbating existing inequities. Despite extensive research on GenAI integration in computing education, accessibility and inclusivity considerations remain largely unexplored. This working group investigates how current GenAI tools align with accessibility and Universal Design for Learning (UDL) principles in computing education. Through a systematic literature review, the group examines tool features, identifies accommodations for students with disabilities, and exposes gaps in inclusive design and pedagogical integration, with the goal of informing more equitable GenAI-supported learning environments.

inclusion

computer science

generative ai

accessibility

feedback

students

computing education

llms

diversity

equity

Author

Natalie Kiesler

Nuremberg Tech

Bedour Alshaigy

Uppsala University

Yasmine N. Elglaly

Western Washington University

Ilenia Fronza

Free University of Bozen-Bolzano

Alex Gerdes

University of Gothenburg

Earl W. Huff

University of Texas

Sven Jacobs

University of Siegen

Dominic Lohr

premier experts GmbH

Raymond Pettit

University of Virginia

Andreas Scholl

Nuremberg Tech

Sandra Schulz

University of Potsdam

David H. Smith

Virginia Polytechnic Institute and State University

Iticse 2026 Proceedings of the 31st ACM Conference on Innovation and Technology in Computer Science Education V 2

731-732
9798400726330 (ISBN)

31st Innovation and Technology in Computer Science Education, ITiCSE 2026
Madrid, Spain,

Subject Categories (SSIF 2025)

Computer and Information Sciences

DOI

10.1145/3803401.3812045

More information

Latest update

9/30/2026