🤖 AI Summary
This study addresses the challenges faced by adults with ADHD in task management, particularly the impact of emotional dysregulation and insufficient social support, which are often overlooked by existing productivity tools that fail to accommodate their nonlinear attention patterns and co-regulation needs. Through in-depth interviews with 22 adults with ADHD and speed-dating speculative design sessions involving 20 participants, complemented by 13 AI-enhanced task support prototypes, the research systematically investigates how emotional and social scaffolding shape their task practices. The work proposes a novel perspective framing task management as a relational and emotionally co-constructed process, articulates socially aware AI design principles that support co-regulation and adapt to nonlinear attention rhythms, and provides empirical grounding and functional preference guidance for affective task-support systems tailored to individuals with ADHD.
📝 Abstract
Adults with ADHD often face challenges with task management, not due to a lack of willpower, but because of emotional and relational misalignments between cognitive needs and normative infrastructures. Existing productivity tools, designed for neurotypical users, often assume consistent self-regulation and linear time, overlooking these differences. We conducted 22 semi-structured interviews with ADHD-identifying adults, exploring their challenges in task management and their coping mechanisms through socially and emotionally scaffolded strategies. Building on these insights, we conducted a follow-up speed dating study with 20 additional ADHD-identifying adults, focusing on 13 speculative design concepts that leverage AI for task support. Our findings reveal that task management among adults with ADHD is relationally and affectively co-constructed, rather than an isolated individual act. Overall, we provide (1) empirical insights into distributed and emotionally scaffolded task management practices, (2) design implications for socially-aware AI systems that support co-regulation and nonlinear attention rhythms, and (3)an analysis of user preferences for different AI design concepts, clarifying which features were most valued and why.