Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.06864 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912473079087104 |
|---|---|
| author | Deshmukh, Raghavendra |
| author_facet | Deshmukh, Raghavendra |
| contents | Digital work environments in IT and knowledge-based sectors demand high levels of attention management, task juggling, and self-regulation. For adults with ADHD, these settings often amplify challenges such as time blindness, digital distraction, emotional reactivity, and executive dysfunction. These individuals prefer low-touch, easy-to-use interventions for daily tasks. Conventional productivity tools often fail to support the cognitive variability and overload experienced by neurodivergent professionals. This paper presents a framework that blends Systems Thinking, Human-in-the-Loop design, AI/ML, and privacy-first adaptive agents to support ADHD-affected users. The assistant senses tab usage, application focus, and inactivity using on-device ML. These cues are used to infer attention states and deliver nudges, reflective prompts, or accountability-based presence (body doubling) that aid regulation without disruption. Technically grounded in AI, the approach views attention as shaped by dynamic feedback loops. The result is a replicable model for adaptive, inclusive support tools in high-distraction work environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06864 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Toward Neurodivergent-Aware Productivity: A Systems and AI-Based Human-in-the-Loop Framework for ADHD-Affected Professionals Deshmukh, Raghavendra Human-Computer Interaction Digital work environments in IT and knowledge-based sectors demand high levels of attention management, task juggling, and self-regulation. For adults with ADHD, these settings often amplify challenges such as time blindness, digital distraction, emotional reactivity, and executive dysfunction. These individuals prefer low-touch, easy-to-use interventions for daily tasks. Conventional productivity tools often fail to support the cognitive variability and overload experienced by neurodivergent professionals. This paper presents a framework that blends Systems Thinking, Human-in-the-Loop design, AI/ML, and privacy-first adaptive agents to support ADHD-affected users. The assistant senses tab usage, application focus, and inactivity using on-device ML. These cues are used to infer attention states and deliver nudges, reflective prompts, or accountability-based presence (body doubling) that aid regulation without disruption. Technically grounded in AI, the approach views attention as shaped by dynamic feedback loops. The result is a replicable model for adaptive, inclusive support tools in high-distraction work environments. |
| title | Toward Neurodivergent-Aware Productivity: A Systems and AI-Based Human-in-the-Loop Framework for ADHD-Affected Professionals |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2507.06864 |