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Cited by 25Disability Education and EmploymentDigital Accessibility for DisabilitiesDisability Rights and Representation

Neurodivergence and Work in Human-Computer Interaction: Mapping the Research Landscape

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Burtscher, Sabrina, Gerling, Kathrin·2024·Proceedings of the 3rd Annual Meeting of the Symposium on Human-Computer Interaction for Work
DOI 10.1145/3663384.3663386
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Abstract

Work environments are commonly designed with non-disabled people in mind. In this contrasting review, we explore how Human-Computer Interaction (HCI) research approaches neurodivergence in workplace settings. We provide an in-depth analysis of eleven HCI publications focused on this area. Our results show a fragmented research landscape with varying views on disability and purposes of work, not always prioritizing the actual needs and preferences of neurodivergent people. We highlight research opportunities for our community, and advocate for a justice-oriented perspective that centers neurodivergent people and their right to self-determination (i.e., leading life with autonomy and the ability to make their own choices) in the context of work.

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  • The gap investigated by this work lies at the intersection of HCI work that actively involves neurodivergent subjects and literature investigating neurodivergent individuals in higher education and broad accessibility of universities and their systems.

    “The gap investigated by this work lies at the intersection of the mentioned HCI work which actively involves its neurodivergent subjects [21, 29, 39, 64, 64, 93], and the body of literature which investigates the experience of these neurodivergent individuals in higher education [4, 14, 15, 51, 57, 70], as well as work investigatigating the broad accessibility of universities and their systems [3,”

    From Burnout by Design: How Digital Systems Overburden Neurodivergent Students in Higher Education (2025) · cites this paper as [21]

    The Introduction and Findings establish that this is HCI work about neurodivergent people in workplace settings. But the Methods describe a close-reading literature review, not research that directly involved neurodivergent participants; the direct participation described in the Corpus Description belongs to studies it reviews. The higher-education and university-accessibility literature is cited separately in the citing sentence, so it is not attributed to this source. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • This study fills a critical gap in HCI, work, and education research.

    “This study fills a critical gap in HCI, work, and education research”

    From Burnout by Design: How Digital Systems Overburden Neurodivergent Students in Higher Education (2025) · cites this paper as [21]

    The Conclusion (Section 9; page not provided) identifies research opportunities concerning neurodivergence and work in HCI, supporting the claim’s HCI-and-work component. Table 1 excludes school and general contexts without an explicit focus on work, so the paper does not establish a gap in education research. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • The mental workload of organising student life while balancing stigma can lead neurodivergent students to mask their traits and not communicate their needs, deepening difficulties and leading to burnout or drop-out.

    “The mental workload of organising their student life (such as managing coursework deadlines) whilst balancing the stigma of outwardly presenting as neurodivergent in the workplace [21], or even disclosing as such, can lead neurodivergent students to mask their traits and not communicate their needs, deepening their difficulties and leading to burnout or drop-out [79, 80].”

    From Burnout by Design: How Digital Systems Overburden Neurodivergent Students in Higher Education (2025) · cites this paper as [21]

  • The failure to design for neurodiversity from the outset leaves disabled users as an afterthought, reinforcing systemic inequities.

    “The failure to design for neurodiversity from the outset leaves disabled users as an afterthought [21], reinforcing systemic inequities.”

    From Burnout by Design: How Digital Systems Overburden Neurodivergent Students in Higher Education (2025) · cites this paper as [21]

  • Prior studies focus on workplace accessibility barriers.

    “Unlike prior studies that focus on workplace accessibility barriers [15, 21]”

    From Burnout by Design: How Digital Systems Overburden Neurodivergent Students in Higher Education (2025) · cites this paper as [21]

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Authors
Burtscher, S., & Gerling, K.
Affiliations
Karlsruhe Institute of Technology (DE)
ORCID
Venue
Proceedings of the 3rd Annual Meeting of the Symposium on Human-Computer Interaction for Work
Publisher
ACM
Published
2024-06-25
Pages
1-14
DOI
10.1145/3663384.3663386
OpenAlex
W4399913374
Cited by
25
References
18
Citations by year
2026: 13 · 2025: 12
FWCI
11.38
Type
conference paper
Language
en
Open access
Licence
Locations
  • Annual Symposium on Human-Computer Interaction for Work (CHIWORK) (publishedVersion, cc-by-nc-sa)
  • Repository KITopen (Karlsruhe Institute of Technology) (submittedVersion, cc-by-nc-sa)
Topics
  • Social Sciences › Safety Research › Disability Education and Employment
  • Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
  • Social Sciences › Safety Research › Disability Rights and Representation
Keywords
neurodivergence, workplace design, neurodivergent people, disability, work environment, self-determination, autonomy

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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