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Your AI Is Copying Bad Code

Prompting AI-Assisted Coding Tools for Accessible Outcomes

A11YTO Conf 2025 — Session


Speakers

Everett Zufelt
VP, Strategic Partnerships & Emerging Technology
Orium

Ajinkya Kunjir
Research Lead
Title TBD
Orium


Abstract

AI-assisted coding tools—such as GitHub Copilot, Cursor, and Claude Code—significantly accelerate interface development through natural-language prompts. However, these systems are trained on large corpora that include years of inaccessible patterns. As a result, they frequently produce visually polished yet inaccessible interfaces.

This session presents a practical method for "accessibility prompt engineering": techniques that guide AI tools toward output aligned with WCAG, ARIA best practices, and inclusive interaction patterns. Through demonstrations and real-world examples, attendees will learn how to shape prompts that yield semantic HTML, meaningful names/roles/states, robust keyboard interaction, and verifiable accessibility. The session also covers how to validate AI-generated code and how to operationalize a reusable prompt library so accessibility becomes a default outcome rather than a post-hoc fix.


Learning Objectives

By the end of this session, participants will be able to:

  1. Diagnose common accessibility failures produced by AI-generated UI code.

  2. Write effective prompts that enforce semantic structure, keyboard operability, and sufficient color contrast.

  3. Apply ARIA judiciously (and minimally) to complement, not replace, native semantics.

  4. Validate outputs using automated checks (axe, Lighthouse, pa11y) and targeted manual testing.

  5. Establish a team prompt library and review workflow to maintain accessibility quality at scale.


Orium • A11YTO Conf 2025

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