Tether: A Personalized Support Assistant for Software Engineers with ADHD

2025-09-02

AI Summary

Software engineering tools have long overlooked neurodiversity, particularly failing to address cognitive challenges faced by developers with ADHD—such as sustained attention, task initiation, and self-regulation. To bridge this gap, we introduce the first desktop-scale intelligent assistant explicitly designed for software engineers with ADHD. Our system integrates OS-level activity monitoring, retrieval-augmented generation (RAG), and gamified incentives, leveraging large language models (LLMs) to deliver context-aware, real-time focus support and adaptive interaction. It is cognitively grounded in ADHD-specific neurocognitive profiles, enabling dynamic responsiveness and fine-grained situational modeling. Preliminary self-deployed evaluation demonstrates that optimized prompt engineering and RAG substantially improve contextual accuracy. This work fills a critical void in the SE toolchain concerning neurodiversity and establishes a reusable technical paradigm—with empirical validation—for inclusive human–AI collaborative development.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Humans and AI: Intelligent User Interfaces
• Application Domains: Software Engineering

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Equity, diversity, and inclusion in software engineering often overlook neurodiversity, particularly the experiences of developers with Attention Deficit Hyperactivity Disorder (ADHD). Despite the growing awareness about that population in SE, few tools are designed to support their cognitive challenges (e.g., sustained attention, task initiation, self-regulation) within development workflows. We present Tether, an LLM-powered desktop application designed to support software engineers with ADHD by delivering adaptive, context-aware assistance. Drawing from engineering research methodology, Tether combines local activity monitoring, retrieval-augmented generation (RAG), and gamification to offer real-time focus support and personalized dialogue. The system integrates operating system level system tracking to prompt engagement and its chatbot leverages ADHD-specific resources to offer relevant responses. Preliminary validation through self-use revealed improved contextual accuracy following iterative prompt refinements and RAG enhancements. Tether differentiates itself from generic tools by being adaptable and aligned with software-specific workflows and ADHD-related challenges. While not yet evaluated by target users, this work lays the foundation for future neurodiversity-aware tools in SE and highlights the potential of LLMs as personalized support systems for underrepresented cognitive needs.

Problem

• Addressing overlooked neurodiversity in software engineering workflows
• Providing adaptive support for ADHD-related cognitive challenges
• Integrating context-aware assistance for developers with ADHD

Innovation

• LLM-powered desktop app for ADHD support
• Combines activity monitoring, RAG, and gamification
• OS-level tracking with personalized ADHD chatbot