‘Hacking’ Assistive Technology: Creating and Customizing AI Tools for Personal Accessibility

Existing AI assistive applications for blind people tend to be designed for common use cases in order to be broadly applicable, for instance, reading printed text or identifying objects. Yet, individuals have unique skills, goals, and contexts, leading to a long-tail of tasks that one-size-fits-all assistive technology cannot address well. These gaps point to limitations in the current design process for assistive technologies: solutions are designed for broad groups of people, and participants are involved in limited stages of the design process (typically formative studies, short design check-ins, and evaluations). In this talk, I will present my research applying a Do-It-Yourself (DIY) approach to assistive technology, enabling people with disabilities to create AI-powered tools for their unique needs. In doing so, my approach shifts agency from researchers and developers to people with disabilities. I will first present my qualitative research on current technology gaps and DIY practices among blind people, showing how people with disabilities often already spend a high degree of effort customizing and creating workaround ‘hacks’ to make technology work for them. I will then present two tools aimed at harnessing and better supporting these DIY efforts, by enabling people to create and modify assistive technologies for themselves. Overall, my research aims to promote the democratization of AI technology creation, and support blind people in having greater control over AI-based technologies in their lives.

About our speaker
Jaylin Herskovitz joined the Department of Computer Science at Tufts University as an Assistant Professor in January 2026. Her research focuses on accessibility and AI-based assistive technologies, DIY technology, and AR/VR. Her work combines qualitative methods with technical system building, and she works with people with disabilities to design, develop, and evaluate AI-based assistive technology. Her work has been published in top-tier HCI conferences, including CHI, UIST, and ASSETS. Jaylin holds a Ph.D. in Computer Science from the University of Michigan, where she was advised by Anhong Guo. During her Ph.D., Jaylin was supported by an NSF Graduate Research Fellowship and completed internships with Apple AI/ML Research and Microsoft Research.

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