arxiv: 2603.17258 · v2 · submitted 2026-03-18 · 💻 cs.HC

Recognition: 2 theorem links

· Lean Theorem

"Not Just Me and My To-Do List": Understanding Challenges of Task Management for Adults with ADHD and the Need for AI-Augmented Social Scaffolds

Jingruo Chen , Yibo Meng , Kexin Nie

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Pith reviewed 2026-05-15 09:27 UTC · model grok-4.3

classification 💻 cs.HC

keywords ADHDtask managementAI systemssocial scaffoldsneurodiversityhuman-computer interactionqualitative interviewsspeculative design

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The pith

Task management among adults with ADHD is relationally and affectively co-constructed rather than an isolated individual effort.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that adults with ADHD handle tasks through emotional alignments and relationships with others, not through isolated self-control. Standard productivity tools fail them because they assume steady willpower and straight timelines. Interviews with 42 adults revealed coping strategies that rely on social and emotional scaffolding. A follow-up study with speculative AI designs showed preferences for features that enable co-regulation. This matters because it points to new ways AI can fit the actual rhythms of attention instead of forcing neurotypical patterns.

Core claim

Adults with ADHD often face challenges with task management because of emotional and relational misalignments between their cognitive needs and standard systems. Through interviews and speed dating with speculative designs, the research finds that task management is relationally and affectively co-constructed. This leads to design implications for socially-aware AI that supports co-regulation and nonlinear attention rhythms, along with insights into which AI features users value most.

What carries the argument

Socially and emotionally scaffolded strategies for task management, extended through AI-augmented designs that enable co-regulation.

Load-bearing premise

The self-identified ADHD adults in the sample represent typical experiences, and their reactions to hypothetical designs accurately predict what real AI systems would need.

What would settle it

A larger study tracking actual use of an implemented AI social scaffold system over months to see if it improves task completion rates and reduces stress compared to standard tools.

Figures

Figures reproduced from arXiv: 2603.17258 by Jingruo Chen, Kexin Nie, Yibo Meng.

**Figure 1.** Figure 1: Conceptual overview of task management in adults with ADHD as a relationally and affectively co-constructed [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗

**Figure 2.** Figure 2: Method Overview ID Gender Age Occupation Education Diag.† Tools Used P1 M 19 Student Bachelor’s CD Todoist; Trello; Forest app; digital timer P2 F 19 Student Bachelor’s SSI Reminders; sticky notes P3 F 18 Student Bachelor’s CD None P4 M 38 Teacher PhD CD None P5 F 22 Student Bachelor’s CD Forest app; digital timer P6 M 32 Office worker Bachelor’s CD Forest app; to-do list P7 F 18 Student Bachelor’s CD Stic… view at source ↗

**Figure 3.** Figure 3: Challenge–Expectation mapping from interview findings [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗

**Figure 4.** Figure 4: Design Implications Mapping We argue that co-regulation should be built into the core architecture of future systems. Platforms could offer live "focus rooms" where users synchronize work periods with AI or human partners. Journaling dashboards could allow peer check-ins, where users report moods, progress, and obstacles to a trusted group or AI companions. Task timelines could be shared across human-AI p… view at source ↗

**Figure 5.** Figure 5: Overall participant ratings for the 13 speculative design concepts (N=20; 1–5 Likert scale where 1 = least [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper delivers grounded interview data showing ADHD task management as relational and socially scaffolded, with useful feedback on AI concepts, but the small self-selected sample limits how far the claims travel.

read the letter

The core contribution is the empirical picture from 22 interviews plus 20 speed-dating sessions: adults with ADHD describe task management as something they handle through other people and emotional cues rather than solo willpower or linear apps. That framing is backed directly by participant quotes and themes, and it leads to concrete design suggestions for AI that supports co-regulation and flexible timing. The speed-dating part gives clear signals on which of the 13 speculative concepts people liked and why, which is more actionable than pure critique of existing tools. Methods follow standard HCI practice for this kind of work, and the interpretations stay close to what participants said without obvious overreach. The main limitation is the sample: self-identified adults, total N around 42, all recruited in ways that favor those already thinking about these issues. That makes the relational claim solid for the group studied but leaves open how much it applies more broadly or how well the hypothetical AI preferences would hold up in real deployments. No circular reasoning or invented mechanisms appear in the analysis. This paper is for HCI researchers and tool builders focused on neurodiversity and productivity systems. It gives them specific, usable insights without claiming universality. A serious editor should send it to peer review; the data is real and the topic timely, even if revisions will need to tighten the scope and discuss generalizability more explicitly.

Referee Report

0 major / 1 minor

Summary. The paper claims that task management among adults with ADHD is relationally and affectively co-constructed rather than an isolated individual act. It supports this through thematic analysis of 22 semi-structured interviews exploring challenges and socially/emotionally scaffolded coping mechanisms, followed by a speed-dating study with 20 additional participants evaluating 13 speculative AI design concepts for task support. The work contributes empirical insights into distributed practices, design implications for socially-aware AI systems supporting co-regulation and nonlinear attention, and analysis of user preferences for AI features.

Significance. If the results hold, this work is significant for the HCI and AI communities by challenging individual-centric assumptions in productivity tools and providing grounded evidence for relational approaches to task support. Strengths include direct grounding in participant interview data and speed-dating feedback using standard qualitative methods, with consistent insights into emotional scaffolding and no circular reasoning; the analysis of preferences for specific speculative designs offers concrete, falsifiable directions for future AI systems.

minor comments (1)

[Abstract] Abstract: minor typo with missing space in '(3)an analysis' (should read '(3) an analysis').

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive review, recognition of the work's significance for HCI and AI communities, and recommendation to accept. We appreciate the acknowledgment of the empirical grounding in participant data, the consistent insights on emotional scaffolding, and the concrete directions for future AI systems.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is a qualitative empirical study whose central claim—that task management for adults with ADHD is relationally and affectively co-constructed—is derived directly from thematic analysis of 22 semi-structured interviews plus speed-dating feedback from 20 additional participants on 13 speculative AI design concepts. No mathematical derivations, fitted parameters, predictions, or self-citation chains exist; the reported methods contain no internal reductions where outputs equal inputs by construction. All load-bearing steps rest on participant data that remain externally falsifiable and independent of the authors' prior work.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This qualitative study relies on empirical data collected through interviews and participatory design evaluation. There are no free parameters fitted to data, no mathematical axioms invoked, and no new entities postulated; the contributions are interpretive insights from participant responses.

pith-pipeline@v0.9.0 · 5507 in / 1205 out tokens · 67995 ms · 2026-05-15T09:27:56.199757+00:00 · methodology

discussion (0)

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design implications for socially-aware AI systems that support co-regulation and nonlinear attention rhythms

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Reference graph

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