A mosaic of prototype screens from the Easy Read Generator redesign—an accessibility-focused civic tech tool reimagined by UMD students to better serve users with diverse cognitive and digital literacy needs.

Forked, Not Finished: Mentoring Civic Tech the Open Source Way

This spring, I had the opportunity to support several student-led civic tech projects through the University of Maryland’s iConsultancy program. The partnership was originally facilitated through my role at the National Democratic Institute (NDI), but when NDI’s participation was disrupted by a sweeping freeze on U.S. foreign assistance programs, I continued advising the students in a personal capacity.

What started as a straightforward mentorship experience became a much more fluid—and in some ways more meaningful—engagement, shaped by shifting roles, student initiative, and a shared interest in public-interest technology. In many ways, it reminded me of the spirit of open source: people stepping in, adapting to change, and contributing however they can. NDI itself has long embraced open source platforms like Decidim and CiviCRM as part of its commitment to digital democracy—tools that reflect the values of transparency, adaptability, and shared ownership.

Three Projects, Three Distinct Challenges

Each iConsultancy team focused on a different scope of work—specifically related to Decidim, an open-source platform for democratic participation, and a new tool that NDI was designing to make information more accessible to people with intellectual disabilities. These projects were all rooted in the open source ethos: building in the open, iterating in real time, and aiming for impact beyond the immediate team.

1. Decidim Alternate Deployment Methods

This team explored ways to simplify and modernize how Decidim is deployed across different environments. The official Heroku option had become outdated, and the manual installation process was prohibitively complex for non-expert users.

The students conducted a technical evaluation of Docker and Heroku deployment methods, tested them across operating systems, and ultimately created an updated Docker configuration tailored for production environments. Their contributions were submitted to the Decidim GitHub repo. These additions make it significantly easier to deploy Decidim in a production environment using Docker Compose. Like many open source contributions, their work advanced on community-maintained tools, with the potential to be picked up and improved by others.

2. Easy Read Generator UX Redesign

The second team focused on redesigning the user interface for NDI’s Easy Read Generator project, a tool that simplifies complex civic documents to make them more accessible for individuals with intellectual disabilities and those with lower literacy levels.

Drawing on user research, accessibility guidelines (like WCAG), and competitive analysis, the students developed a high-fidelity prototype and detailed UX recommendations. While I had envisioned an iterative redesign of existing wireframes, the team pushed the concept further—exploring new features such as login options and donation functionality. Their willingness to experiment expanded the conversation about what this tool could become. 

3. Manual Installation Documentation Enhancements

The third project aimed to unify and improve Decidim’s manual installation documentation. English-language instructions were incomplete, and more robust Spanish-language documentation had yet to be translated or standardized.

The team was tasked with consolidating and testing these disparate guides, streamlining the process for deploying Decidim with all its intended features. Documentation is the connective tissue of any open source ecosystem, and while this team faced challenges in delivering their final product, the importance of the task—and the gaps it sought to fill—remains clear.

Lessons from the Field

Each project reflected the realities of open collaboration: sometimes productive, sometimes messy, always instructive. The teams that stayed organized and engaged produced genuinely useful outputs that could be built upon by others. In other cases, student groups struggled to balance their workload or needed more support to stay aligned with the project’s goals.

To be clear, this isn’t a critique of the iConsultancy model—student-led learning is, by design, exploratory. But like any open source initiative, success is rarely the result of individual effort alone. It depends on a thoughtful mix of initiative, shared norms, and an ecosystem of support. Civic tech projects, especially those aiming for real-world relevance, demand a working knowledge of community context, accessibility, and technical infrastructure—all challenging to fully absorb in a single semester. And just as open source contributors rely on documentation, mentors, and community to navigate complex codebases, student teams benefit from structured feedback, clear goals, and a culture that rewards asking questions. Those ingredients can turn short-term projects into lasting contributions.

Why I Stayed

Even after my layoff from NDI, I chose to remain involved because my commitment to the projects didn’t depend on a formal title. The UMD students brought real energy and fresh ideas. And continuing to mentor them gave me a sense of continuity and purpose at a time when many other structures were unraveling.

In civic tech, we often talk about resilience, distributed leadership, and decentralization. These principles are foundational to the open source ecosystem, where no single person or entity controls the project and leadership often emerges organically from contributors. This experience reminded me that these values aren’t just theoretical—they show up in how we navigate change. Open source projects are a fitting metaphor: they can survive the loss of their initial stewards, thriving as new contributors pick up the thread. Our work, too, can have a life beyond any single job or institution. Even when a formal role ends, the ideas, tools, and momentum we create can continue evolving—adapted, expanded, and reimagined by others who care.

Using AI to Strengthen Democratic Inclusion

Participants develop a list of features they would like to be included in an Easy Read generator tool. They then used this list to design a prototype tool.
Participants develop a list of features they would like to be included in an Easy Read generator tool. They then used this list to design a prototype tool.

From the 15 percent of people around the world who live with a disability, 8 in 10 reside in developing countries. Although Article 21 of the United Nations Convention on the Rights of Person with Disabilities (CRPD) grants them the right to accessible information, people with disabilities often face communication barriers due to a lack of information accessibility. Access to information is essential for democratic and political participation, which enables people to make informed decisions and influence policies that affect their lives. If people with intellectual disabilities have greater access to easy-to-read information on political processes or policies and the necessary assistance using it, they will be better equipped to advocate for themselves and participate in democracy. By reducing communication barriers through Easy Read and other accessible formats, societies can foster inclusion, making it possible for people with disabilities to engage fully in civic life.

With these circumstances in mind, the National Democratic Institute (NDI) organized a two-day workshop in Nairobi, Kenya, to bring people with intellectual disabilities, caretakers, civil society representatives, government officials, and accessibility experts together to test and design tools for creating Easy Read documents. The workshop began by reviewing the results of a remotely-conducted activity to test assumptions about how to best address barriers to accessible information in Kenya. Participants then explored the possibility of using generative AI tools, like ChatGPT, to facilitate the creation of accessible information. To ensure that everyone could participate, NDI provided accessibility accommodations, such as sign-language interpretation, an expanded time frame agenda to allow for ample participation, and illustrations to enhance comprehension and retention.

Easy Read is a method of presenting information in an easy-to-understand format. Easy Read materials are especially beneficial for people with disabilities, those with low literacy levels, non-native language speakers, and individuals experiencing memory difficulties. Easy Read combines short sentences that are clear and free of jargon with simple images to help explain the written content. Easy Read is essential not only for people with intellectual disabilities but also for making information accessible to everyone, particularly in a democratic society. Accessible information enables all citizens to participate in civic processes, make informed decisions, and understand their rights and responsibilities. By utilizing Easy Read, NDI seeks to support inclusive democratic participation and enable people to actively engage in their communities.

Alice Mundia, Chairperson of the Differently Talented Society of Kenya (DTSK), discusses barriers faced by persons with intellectual disabilities, specifically with regard to accessing information.
Alice Mundia, Chairperson of the Differently Talented Society of Kenya (DTSK), discusses barriers faced by persons with intellectual disabilities, specifically with regard to accessing information.

Twenty representatives from various disabled people’s organizations (DPOs) and other civic groups contributed their diverse perspectives and expertise to advance information accessibility in Kenya. These groups included the United Disabled Persons of Kenya (UDPK), the Kenya Association of the Intellectually Handicapped (KAIH), Kenya ICT Action Network (KICTANet), Differently Talented Society of Kenya (DTSK), Black Albinism (BI), Ubongo Kids, Down Syndrome Society of Kenya (DSSK), Kenya Sign Language Interpreters Association (KSLIA), the Kenya National Association of the Deaf (KNAD), and the Directorate of Social Development under the Ministry of Labour and Social Services. The event fostered collaboration and laid the foundation for further development of accessible digital tools in the country.

On the first day, participants reflected on the structural challenges that restrict access to information for people with intellectual disabilities. Alice Mundia, Chairperson of the Differently Talented Society of Kenya (DTSK), led a discussion on the barriers to creating and distributing Easy Read materials. Participants then explored NDI’s Easy Read website, provided feedback on navigation and usability, and used generative AI tools to draft Easy Read documents. Working in small groups, they refined these drafts, exploring the potential and challenges of using AI for accessible content creation.

“I wish I knew about this before. This will help a lot,” said a teacher who supports students with Down Syndrome. “I struggle to break down complex jargon into understandable information. With this tool, that work becomes easier.”

During the second day, participants focused on mapping key stakeholders involved in creating and disseminating Easy Read documents and developing a prototype for an Easy Read Generator tool. Participants collaborated to design user flows, interfaces, and features for the tool by sketching visual prototypes. This hands-on session ensured that the tool would meet the diverse needs of people with intellectual disabilities and their supporters. The concept for an Easy Read Generator originated during a pitch competition in 2021, where NDI staff proposed tech solutions to democracy challenges. The winning idea, the “Right To Know” project, envisioned an Easy Read translator, anticipating the development of generative AI technologies like ChatGPT, which has enabled computers to simplify complex documents quickly.

Through the workshop, participants found that while ChatGPT is a powerful tool for generating and simplifying text, the unpaid version has several limitations that hinder its generation of accessible content. These include browsing limitations and the inability to upload documents or generate images. 

Following this workshop, NDI has begun exploring two avenues to address these limitations and improve access to accessible information for people with intellectual disabilities. First, NDI is reaching out to companies that provide Generative AI chatbots to explore the possibility of allowing NGOs that support people with intellectual disabilities to access paid services for free or at a reduced cost. Such a program could enable disability rights advocates, caregivers, and organizations to leverage the most advanced tools to generate Easy Read content. This would significantly enhance their ability to reach and support individuals who depend on these accessible materials.

NDI is also exploring avenues for developing the prototype Easy Read Generator that participants designed into a working application through future programs. This tool would not only improve the experience of using Generative AI tools to create Easy Read documents, it could also be offered for free to select partner organizations, eliminating cost as a barrier to generating easy-to-read information. 

This illustration captures the second day of the workshop, which focused on designing an Easy Read AI chatbot.
This illustration captures the second day of the workshop, which focused on designing an Easy Read AI chatbot.

Through this workshop, participants from diverse backgrounds collaborated to explore generative AI’s potential for making information accessible for all. The workshop provided an invaluable opportunity to address challenges, share insights, and develop solutions. NDI remains committed to expanding these programs to ensure that all citizens have access to information in formats they can understand and use.

Author: Jesper Frant, Senior Technology Projects Manager for NDI’s Democracy and Technology team

NDI’s engagement with this program is implemented with the support from the National Endowment for Democracy (NED) program.

Related Stories 

Early Intervention is Showing Girls that Politics is for Them

Persons With Disabilities Enhance Civic Engagement in Jordan

Partnering with the Disability Community and Parliament to Promote Inclusion

###

NDI is a non-profit, non-partisan, non-governmental organization that works in partnership around the world to strengthen and safeguard democratic institutions, processes, norms and values to secure a better quality of life for all. NDI envisions a world where democracy and freedom prevail, with dignity for all.

This story was originally posted on ndi.org.

How Smart Automation Can Be Used In International Development

This article was originally posted on NDItech.org.

Artificial Intelligence is one of those buzzwords in tech that everyone’s heard, but few people actually understand how it can be used in practice. If you’re to believe Hollywood or Stephen Hawking, AI either means androids that are indistinguishable from humans (except for the inability to use conjunctions) or super-intelligent computers that could spell the end of the human race. After attending a Tech Salon on how AI can be used in international development, I can say with absolute certainty that it is neither of those things… yet. But the “commodification” of AI is making “smart automation” — a term I quite liked as a useful synonym for AI — much more accessible outside Silicon Valley. In fact, you probably already used some form of AI today without even knowing it.

Before we get into how AI can be used in international development, let’s first understand for what type of things smart automation can and can’t be used. These capabilities or limitations can be broken down into three categories.

First, computers can now be trained to automate human intelligence. In other words, we can now train computers to do simple tasks that only humans used to be able to do — things like find which photos in your photo album have cats in them. This is a learning process whereby a human sorts out cat photos and a machine-learning algorithm (another tech buzzword) builds its own model to automate the process of finding cat photos.

Second, smart automation is only really useful as a way to augment human ability; it does not replace humans wholesale. AI is really good at classification and prediction, but it will never be 100 percent accurate. You still need a human to monitor the results, check for bias and make judgment calls.

Ok, so, now that the AI found the cat photos, it’s up to you — human — to exclude the one that is just a realistic-looking cat-shaped slipper (how’d that get in there?!?) and post the cutest, most relevant one as your animal shelter’s Facebook cover photo. We’re trying rescue kittens, not sell cat slippers…silly computer.

Finally, computers are way better than humans at doing simple, mundane tasks over and over without error or referencing vast databases of complex information. Smart automation is, therefore, a pathway to scale.

The cat example doesn’t work quite as well in this case so I’m going to dispense with that metaphor and instead turn to a real-life problem. There are simply too few doctors in Nigeria, and — given the size of the population and its rate of growth — it will be generations before we can train enough doctors. Smart automation has been shown to be surprisingly accurate at diagnosing medical ailments. Combining AI-assisted diagnosis with community health workers — who require way less training than a doctor — could be an important pathway to scaling access to medical services in places like Nigeria.

So how would an organization like NDI get started in smart automation? The Tech Salon folks recommended starting with a mid-scale pilot project tied to metrics for success and getting top-down institutional buy-in. But for me, the “how” is way less important than the “what.” In other words, selecting the right pilot project based on previously successful use cases is way more important than the size or institutional buy-in of the pilot. Also, your organization should probably have the capacity to support “dumb automation” — automation that doesn’t employ machine learning algorithms — before it makes the leap to supporting smart automation.

NDI is currently looking for ideas on an appropriate pilot project for smart automation. If you have ideas, you can email me at jfrant [at] ndi [dot] org (<= hoping the AIs aren’t smart enough to read that… yet).