Easy-Web-Check is a free, accessible web-based tool designed for the quick and straightforward assessment of digital content accessibility. It is aimed at individuals and organizations looking to improve accessibility in their websites or applications. With its user-friendly approach, Easy-Web-Check provides an initial evaluation of whether key accessibility requirements are met. This allows potential barriers for users with diverse needs to be identified and addressed early in the development process.

The testing methodology of Easy-Web-Check is based on recognized accessibility standards. It examines essential aspects such as alternative text for images, sufficient color contrast, and keyboard navigability. The results help detect fundamental accessibility issues that may hinder users with visual, motor, or cognitive impairments. By identifying these problems early on, developers and content creators can make necessary adjustments to enhance the overall user experience.

In addition to Easy-Web-Check’s standard evaluation, a more comprehensive accessibility assessment can be conducted based on EN 301 549. This European standard defines detailed requirements for digital accessibility and ensures compliance with legal regulations. A full assessment according to EN 301 549 provides a deeper analysis and helps organizations meet their obligations for inclusive digital services.

While Easy-Web-Check is free for personal and non-commercial use, expert-supported test completions are also available for those seeking professional guidance. This option allows users to receive expert insights, ensuring a more thorough understanding of accessibility issues and possible solutions. By combining an easy-to-use automated check with expert-supported evaluations, Easy-Web-Check provides a flexible approach to improving web accessibility.

To help users share their accessibility assessment results, Easy-Web-Check offers a public results page and an accessible PDF report. These features make it easier to communicate accessibility findings with stakeholders, development teams, or regulatory bodies. By providing clear, shareable reports, Easy-Web-Check supports transparency and collaboration in the effort to create a more inclusive digital environment.

A maturity model for digital accessibility was originally developed by our team for universities. It serves as an analysis tool that allows the universities to measure their ability to implement digital accessibility across all areas of the institution. Based on the results, the institutions learn about the next steps to achieve a higher level of digital accessibility.

The aim of our current project is to adapt this analysis tool to another organization. This meets the organization’s need for an approach to improving accessibility, while being more cost-effective than developing a new maturity model. At the same time, the project serves as a feasibility study, exploring if and how an existing maturity model can be transferred to organizations with different structures.

To this end, we have divided the project into 3 phases:

Phase 1a:
We first developed interview questions, ensuring that they covered relevant aspects of day-to-day tasks, workflows, and the overarching structure of the targeted organization. We then conducted interviews with at least one representative from all departments of the institution. This enabled us to gain a general understanding of the company.

Phase 1b:
The information from Phase 1a was used to create an organigram by summarizing the tasks, workflows, internal and external collaborations, and key challenges. This visualizes the structures of the institution.

Phase 2a:
In this phase, we used an existing pool of accessibility indicators originally compiled to design the maturity model for universities. It was created by collecting indicators from various accessibility maturity models for different domains, including higher education, businesses, and other fields.

The indicator pool was reviewed systematically. Indicators that were not applicable to the organization, because they refer to processes that do not exist in this specific institution (as identified in Phase 2), were removed.

Phase 2b:
We then selected suitable indicators for the targeted organization from this pool. The organigram from Phase 1b was used in this process. By assigning indicators to specific areas of the organigram, we were able to identify potential structures for the adapted maturity model, such as organizing dimensions by departments or processes. This step also revealed gaps, i.e., processes in the organization for which no existing indicators were suitable. In these cases, new indicators were developed.

Phase 3:
In a next step, four levels of accessibility will be defined for each indicator. We will also define the transitions between these levels, including recommendations for actions to achieve a higher level.

With the finished maturity model, the institution will be able to assess itself and measure its levels of accessibility at regular intervals. As a result, the analysis tool will create a network diagram that shows how accessibility is already being implemented in the different departments. Recommendations for actions to improve accessibility will be issued based on the results.

The existing maturity model for universities serves as an inspiration throughout the adaptation process. We draw from the experiences gained by our team during the development by replicating successful approaches. We also use the completed model as a guide for designing the new model, including decisions about the number and structure of dimensions, indicators, and levels. Where applicable, we directly adopt relevant components of the original model without modification.

Reflection of the current phase 1: The quality of the content of the interviews varied, which was reflected in the relevance and structure of the answers given. In some cases, the questions were not answered directly, resulting in information that was unrelated to the topic or of little use.

The final presentation will provide a brief review of all phases, but at the moment phase 2 is not yet complete.

Helix Opportunity has developed a browser extension, Ottitor, that is available on all chromium-based browsers and utilizes Large Language Models, Natural Language Processing, Optical Character Recognition, and other advanced technologies, to analyze web content to identify more complex accessibility issues that typically require manual intervention.

It has been designed with a streamlined, point of use user interface that reduces distractions and fatigue, enhances focus, and pesents easy use.

Many Web Content Accessibility Guideline (WCAG) rules necessitate a semantic understanding of content, which traditionally could only be verified through human intervention. This process is often time-consuming and prone to inconsistencies. In developing Ottitor, we explored leveraging the capabilities of artificial intelligence to automate these checks, making accessibility audits more efficient and reliable. This digital accessibility auditing tool has been successfully implemented as a browser extension, allowing for seamless integration into the web browsing experience. It effectively addresses various WCAG rules that require nuanced comprehension, ensuring that digital content is accessible to all users without the extensive manual effort typically required.

One of the significant features of Ottitor is its ability to address the challenges posed by abbreviations, idioms, and jargon. AI capabilities make it possible for Ottitor to analyse links for coherence, as well . Our AI implementation expands abbreviations for better comprehension but also provides interpretations in multiple languages, making information accessible to a broader audience. Idioms and jargon can be tricky for users, especially those unfamiliar with a language. Ottitor’s AI implementation also provides the meaning of terminologies and explanations for idioms and phrases. It supports multiple languages to ensure inclusivity.

Ottitor had to overcome several limitations with existing AI models. For example, very complex or heavy web pages had to be procesed through multiple scans due to token limitations or exceeding the number of allowable API requests. These constraints can impact the tool’s ability to analyze and provide feedback on large-scale content effectively. Despite these challenges, we remain committed to refining our technology and expanding its capabilities based on user feedback and continuous development.

We are currently exploring advanced features like screen recording analysis, video and audio file analysis, image analysis, and dynamic content analysis. By recognizing patterns and relationships in data, we aim to enhance the auditing process even further, ensuring comprehensive accessibility reviews.

W3C Accessibility Guidelines Working Group and Accessible Platform Architectures Working Group Invited Expert, David Fazio formed the W3C Maturity Model Task Force in 2020 to explore the development of a process document that would guide individuals, and organizations of all types and sizes, with a blueprint to consistently achieve digital accessibility throughout all of its activities, whether they are internal or external, services or product development. The Accessibility Maturity Model is a tool to assess your current capabilities, and provides steps for improvement. As a Task Force we analyzed multiple commercially available accessibility maturity models, and determined that a primary goal of private use of this approach was to generate sales leads. The Task Force assembled a global group of accessibility professionals from varied industries research the key elements, and activities, that would be most universally applicable, for a successful accessibility maturity model, and have since developed the first W3C published maturity model for digital accessibility.