Building a Continuous Accessibility Feedback System with AI: A Step-by-Step Guide

Introduction

Accessibility feedback often gets lost in the shuffle—emails pile up, bugs sit unassigned, and users feel ignored. GitHub faced this challenge head-on by transforming their approach using AI and automation. This guide breaks down their proven methodology into actionable steps, showing you how to create a living system where every piece of accessibility feedback is tracked, prioritized, and resolved continuously. Instead of relying on static audits, you'll build a dynamic engine that listens to real people and turns their insights into meaningful software improvements.

What You Need

• A GitHub organization or project repository
• GitHub Actions enabled (free for public repos)
• GitHub Copilot license (or equivalent AI assistance)
• Access to GitHub Models (or a compatible AI service)
• An existing channel to collect accessibility feedback (e.g., web form, email, community forum)
• A cross-team commitment to prioritize accessibility (approval from product, engineering, design)
• A small backlog of past accessibility issues to use as test data
• Basic understanding of GitHub issue templates, workflows, and YAML syntax

Step-by-Step Instructions

Step 1: Centralize Feedback Collection

Start by gathering all existing accessibility feedback into a single repository. Create a dedicated GitHub repo (e.g., accessibility-feedback) or add an area to your main project. Set up a clear intake process using GitHub Issues. For every new piece of feedback—whether from a screen reader user, keyboard-only user, or low-vision person—ensure it lands in this repo. This eliminates the scattered backlogs that GitHub once faced, where feedback lived across different tools and teams. Use issue forms to enforce consistency. Your goal is one source of truth.

Step 2: Design Templates for Clarity

Create issue templates that capture essential details: user scenario (e.g., “I’m a keyboard-only user and can’t navigate the settings menu”), environment (browser, assistive tech, version), affected pages/components, and severity. GitHub’s YAML-driven issue templates work perfectly here. For example, include fields for WCAG criterion (if known), workarounds, and suggested fix. These templates make every report actionable and reduce back-and-forth. As noted in GitHub’s journey, without structure, feedback turns into noise.

Step 3: Triage Historical Backlog

Before introducing AI, clean house. Review all outstanding accessibility issues—the ones that “linger without owners” or are promised for mythical “phase two.” Label each with a priority (e.g., P0 for blockers, P1 for major, P2 for minor) and assign an owner. If you have hundreds, batch them by component. GitHub used this triage to “lay the groundwork” before automation. Remove duplicates, close stale ones, and convert valid reports into the new template format. This step ensures your AI-driven system starts with a solid, non-chaotic foundation.

Step 4: Automate Feedback Capture with GitHub Actions

Now wire up automation. Create a GitHub Actions workflow that triggers when a new issue is filed (or when a new feedback item arrives via a webhook). The workflow can:

• Parse the issue body and assign relevant labels (e.g., accessibility, keyboard-only, screen reader)
• Add the issue to a project board under a new column like “Triage”
• Tag the appropriate team based on the affected component (e.g., navigation, authentication)
• Send a thank-you comment to the reporter with an expected response time

Start with simple actions—like label management and assignment—before moving to AI-enhanced steps. This mirrors GitHub’s initial approach: “We didn’t want AI to replace human judgment—we wanted it to handle repetitive work.”

Step 5: Leverage AI for Structuring and Routing

Integrate GitHub Copilot or GitHub Models to “clarify, structure, and track user feedback.” For example, use an AI prompt in your workflow to analyze the issue description and suggest:

• A structured summary (e.g., “Screen reader user cannot complete checkout: focus trap on country dropdown”)
• Likely WCAG violations (e.g., 2.1.1 Keyboard, 2.4.3 Focus Order)
• Suggested component owners (e.g., @team-checkout, @team-design-system)

You can call the GitHub Models API directly from an Action using a token. The AI doesn’t make final decisions, but it reduces manual triage time. This is the “continuous AI” that turns feedback into implementation-ready solutions.

Step 6: Prioritize and Track Continuously

With AI handling structuring, your team now sees a clean stream of prioritized issues. Use GitHub Projects (or equivalent) to manage the lifecycle: Triage → Accepted → In Progress → Review → Done. Every issue gets tracked from submission to resolution. Automation can nudge owners if an issue stays in “In Progress” too long. Additionally, create a dashboard that shows accessibility health: time-to-response, time-to-close, and recurring problem areas. This turns accessibility into a living metric, not a static report. As GitHub emphasizes: “not eventually, but continuously.”

Step 7: Maintain Human Oversight and Iterate

AI and automation are tools—the heart of this system is human expertise. Schedule regular reviews where accessibility champions audit AI-suggested labels and routes. Collect feedback on the process itself: are users getting responses faster? Are fixes being deployed? Adjust templates, labels, and workflows based on real-world results. Celebrate wins (like fixing a screen reader blocker) to build momentum. This iterative cycle matches GitHub’s GAAD pledge to strengthen accessibility across open source. Remember: “The most important breakthroughs rarely come from code scanners—they come from listening to real people.”

Tips for Success

• Start small: Don’t automate everything at once. Begin with centralization and templates, then add a simple Action, then AI. Each step builds confidence.
• Involve users with disabilities in designing your templates and testing your workflow. Their lived experience will catch blind spots no AI can.
• Make it visible: Publish your accessibility feedback repository (or a sanitized version) so the community can see issues are being tracked. Transparency builds trust.
• Don’t over-automate: AI should augment, not replace. Keep a “human in the loop” for prioritization and complex routing.
• Measure what matters: Track not just resolution time, but user satisfaction. Follow up with reporters to see if the fix actually worked.
• Iterate on templates: As you learn, refine your templates to ask better questions. Short forms encourage more submissions, but detailed ones yield richer data. Find the balance.