SearcharxivSearch

arXiv subjects

Shalini Madan

Publications and source records attributed to Shalini Madan.

2 recordsLinked to original sources

Two's a Crowd: Human and AI-Based Copresence for Developers with ADHD

Effective collaboration and communication are vital to developer productivity and well-being, yet remain constrained by human factors such as attention, intrinsic motivation, and interpersonal accountability. These constraints are particularly vital for developers identifying with Attention Deficit Hyperactivity Disorder (ADHD), who navigate persistent environmental barriers in modern hybrid workplace settings. While developers with ADHD frequently rely on collaborative copresence practices (such as body doubling or pair programming) to support executive function, the recent emergence of agentic AI coding assistants has begun reshaping these collaborative dynamics. To investigate how developers with ADHD engage in human and AI-based copresence practices, we conducted semi-structured interviews with 14 software engineers with ADHD. Our findings reveal that while traditional human-human copresence provides critical social support and onboarding structure, it forces developers to constantly manage professional reputation and sacrifice personal privacy. Conversely, developers leverage emerging human-AI copresence to maintain accountability and cognitive flow without the social anxiety, performance judgment, or surveillance associated with human observation. Based on these empirical insights, we map developer copresence practices onto core dimensions of Goffman's copresence theory and Forsgren et al.'s SPACE framework of developer productivity, and provide design recommendations for AI-based tools that promote inclusive collaboration for developers with ADHD.

cs.HC

Reflections and Recommendations on AI Adoption Practice from a Mixed-Ability Research Group

Generative AI tools have recently been rapidly adopted by academics in mixed-ability research teams for both personal and professional tasks. While previous work on adoption of AI-based workflows has focused on collaboration and productivity, the perceptions of AI use within research teams remains divided. Through qualitative analysis of interviews of the five members of our mixed-ability research team, we discuss the motivations, challenges, and practices surrounding the use of generative AI in our lab. We reflect on experiences that shaped recommendations for balanced AI use that enable mixed-ability team workflows: (1) managing disability tax & crip time, (2) homogenizing identity, (3) risk disclosure of private information, (4) self-experimentation and miscellaneous tasks, and (5) information seeking. We build upon these themes to present AI practice recommendations we established for our lab to promote AI workflow adoption while preserving agency and disability identity.

cs.HC