Toward Neurodivergent-Aware Productivity: A Systems and AI-Based Human-in-the-Loop Framework for ADHD-Affected Professionals

Digital work demands high levels of attention management, task juggling, and self-regulation. In IT and knowledge-based sectors, these challenges are amplified for neurodivergent professionals — particularly those with ADHD — who experience difficulty with time blindness, urgency fluctuations, emotional regulation, and executive dysfunction.

Conventional productivity tools fall short. They assume static workflows and self-regulation, offering reminders and monitoring that users find overwhelming rather than supportive. Individuals with ADHD need adaptive systems that respond to their actual attention patterns and emotional state.

The Problem: One-Size-Fits-All Design

Existing digital productivity tools rely on willpower, habit formation, and static logic. They don't adapt. They don't learn. For ADHD-affected individuals — who experience high attention variability and struggle with external regulation of attention — these tools often exacerbate the cognitive burden rather than alleviating it.

A New Approach: Voice-Enabled, Adaptive Assistance

Researchers at PES University proposed a comprehensive framework that blends systems thinking, machine learning, and privacy-first adaptive agents to support ADHD-affected work in digital environments.

The core insight: treat productivity support as a dynamic feedback loop, not a static tool. A voice-enabled assistant provides behavioral cues using lightweight, on-device machine learning. The system learns each person's attention patterns, task completion profiles, and emotional regulation needs without requiring explicit input or external documentation.

Key components:

Voice-enabled interface: Reduces friction. Instead of opening an app and filling out forms, the assistant listens and responds conversationally — matching the natural communication patterns of people with ADHD.

Behavioral sensing: Uses lightweight machine learning to infer attention states and respond with non-intrusive, adaptive cues. The system evolves based on what actually works for each individual, not on generic ADHD management strategies.

Co-design with users: The framework was developed through participatory research with 25 ADHD-affected professionals across diverse IT roles. Their inputs shaped the architecture — these voices collectively defined what "neurodivergent-inclusive" actually means in practice.

Privacy-first design: On-device processing preserves autonomy and data security. The assistant never transmits raw behavioral data — it only learns and adapts locally.

The Framework: Three Layers

Attention regulation: Real-time, non-directive behavioral cues that adapt to moment-by-moment attention patterns.

Task management: Helps with time management, prioritization, and task decomposition — the executive function skills that ADHD impacts most.

Emotional co-regulation: An optional digital "body doubling" mode that provides gentle, presence-based support during high-distraction or high-stress work.

Why It Matters

ADHD-affected professionals remain underdiagnosed and undersupported in digital workplaces. These individuals often have deep expertise but struggle with the cognitive infrastructure required by modern work environments. A neurodivergent-aware system bridges that gap — not by "fixing" ADHD, but by designing support systems that honor how neurodivergent brains actually work.