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The rapid adoption of generative AI in software development has impacted the industry, yet its efects on developers with visual impairments remain largely unexplored. To address this gap, we used an Activity Theory framework to examine how developers with visual impairments interact with AI coding assistants. For this purpose, we conducted a study where developers who are visually impaired completed a series of programming tasks using a generative AI coding assistant. We uncovered that, while participants found the AI assistant benefcial and reported signifcant advantages, they also highlighted accessibility challenges. Specifcally, the AI coding assistant often exacerbated existing accessibility barriers and introduced new challenges. For example, it overwhelmed users with an excessive number of suggestions, leading developers who are visually impaired to express a desire for “AI timeouts.” Additionally, the generative AI coding assistant made it more difcult for developers to switch contexts between the AI-generated content and their own code. Despite these challenges, participants were optimistic about the potential of AI coding assistants to transform the coding experience for developers with visual impairments. Our fndings emphasize the need to apply activity-centered design principles to generative AI assistants, ensuring they better align with user behaviors and address specifc accessibility needs. This approach can enable the assistants to provide more intuitive, inclusive, and efective experiences, while also contributing to the broader goal of enhancing accessibility in software development 

As we built and deployed a digital storytelling system to teach digital literacy skills to rural Appalachians, we discovered key opportunities and challenges to promoting digital literacy in this region. We identified that the importance of storytelling in Appalachian culture made digital storytelling an effective means of teaching these skills to residents. However, the poor technology infrastructure at our study site posed challenges to our participants' ability to use technology and learn new skills. We found that poor infrastructure reinforces low self-efficacy, discouraging participants from using technology. In environments where computers are often slow and unreliable, it is not possible to form realistic expectations of how a computer should act. Therefore, it becomes difficult for users to untangle if the issues they encounter are because of usage errors or the technology. These findings highlight how infrastructure and self-efficacy should be accounted for together when conducting rural HCI research. 

Rapidly diffusing 'industry 4.0' technologies stand to impact a broad range of stakeholders. Prior to implementation, forward looking formative analyses can identify systems and policy designs to promote equitable benefit. We investigate this potential through an analysis of stakeholders to a potential drone implementation on a small commercial farm in Rwanda. Translating stakeholders' imaginaries within a post-colonial frame, we identify hopes and concerns related to agency and influenced by global and local systems of power. The findings highlight constraints that recommend system designs promoting local agency and control and policies designed to balance local data management against potentially 'extractive' multinational data transfer processes.