Neurodivergent Women in Software Engineering

• Neurodivergence represents varied cognitive profiles (e.g., ADHD, Autism) that challenge conventional norms and shape unique inclusion challenges for women in software engineering.
• Empirical analyses reveal that underdiagnosis, masking, and gendered biases intensify career barriers and discriminatory practices in tech environments.
• Hybrid methodologies, including inclusive personas and cognitive walkthroughs, provide practical strategies for reforming organizational cultures in engineering.

Neurodivergent women in software engineering represent a multiply-marginalized demographic subject to unique and compounding barriers rooted in neurocognitive differences and gendered social structures. Their experiences are shaped by underlying patterns of underdiagnosis, masking, male-centric organizational cultures, deficient accommodation practices, and systematic biases across the engineering career lifecycle. Recent empirical and methodological investigations map these challenges, propose frameworks for actionable inclusion, and highlight the need for intersectionality-aware analytic tools (Zaib et al., 5 Dec 2025, Chakraborty et al., 25 Oct 2025).

1. Definition and Operationalization of Neurodivergence

Neurodivergence encompasses cognitive differences, including but not limited to ADHD, Autism Spectrum Disorder, dyslexia, and dyspraxia, which diverge from neurotypical developmental or cognitive patterns. In survey-based research, such as Chakraborty and Baltes’ secondary analysis of the State of the Developer Nation 2025, neurodivergence is operationalized as a subset of “disability,” alongside physical disabilities and chronic health conditions. Respondents self-identify via categorical survey items, e.g., “Do you have a disability (including neurodivergence such as ADHD or Autism)?”, enabling aggregated analyses of bias and discrimination without direct enumeration of neurodivergence-specific figures (Chakraborty et al., 25 Oct 2025).

In intersectional analytic frameworks, neurodivergence is proposed as a distinct diversity dimension rather than solely a deficit or clinical category (Chakraborty et al., 25 Oct 2025, Zaib et al., 5 Dec 2025). This distinction is critical for reorienting organizational inclusion and for designing methods that map uniquely intersectional experiences.

2. Demographic Patterns and Prevalence of Discrimination

Analysis of discrimination in software engineering, based on a pool of 8,717 developers, reveals that 6.29% of survey respondents overall report disability-related workplace discrimination. The gender breakdown exposes an elevated prevalence of such discrimination for non-men (encompassing both women and non-binary individuals), with 14.7% reporting “disability” discrimination compared to 4.7% of men (Chakraborty et al., 25 Oct 2025).

Applying these percentages, approximately 174 of 1,181 non-men respondents—primarily women—explicitly cited experiences of such discrimination. However, due to aggregation of disability type in the available data, precise figures disaggregated solely for neurodivergent women are not available. No sub-analyses by race, age, or sexual orientation for neurodivergent women have been published in these studies. This suggests that the organizational data landscape remains insufficiently granular for targeted intervention.

3. Methodological Frameworks for Analyzing Intersectional Challenges

Advanced methodological approaches have emerged to make the invisible labor and discrimination of neurodivergent women analytically visible. Mendez et al.’s InclusiveMag provides a “meta-method” for defining inclusivity frameworks tailored to specific populations. Burnett et al.’s GenderMag offers a cognitive-walkthrough process using personas reflecting gender-linked cognitive facets. Building on these, a hybrid methodology is proposed (Zaib et al., 5 Dec 2025):

• Three-phase loop:

1. Scope: Targeted literature review to surface recurring cognitive, social, organizational, structural, and career progression barriers for neurodivergent women.
2. Derive: Evidence-grounded persona creation using LLMs and analytic worksheet development, calibrated to reflect masking intensity, underdiagnosis, neurotype, and role.
3. Apply: Practitioner workshops and validation with neurodivergent women to identify inclusiveness barriers and generate concrete organizational recommendations.

Through descriptive open coding of qualitative survey responses and analytic adaptation via intersectional personas, these frameworks systematically capture the interplay between neurocognitive traits and gendered workplace dynamics (Chakraborty et al., 25 Oct 2025, Zaib et al., 5 Dec 2025).

4. Key Challenge Domains and Lived Experiences

The intersectional literature review and empirical survey analyses identify five primary domains of challenge for neurodivergent women in software engineering (Zaib et al., 5 Dec 2025, Chakraborty et al., 25 Oct 2025):

Domain	Core ND Challenge	Amplifying Gender Factor	Example Lived Impact
Cognitive/Executive	Planning, memory	Late/missed diagnosis	Labeled “unreliable” due to underestimating story points
Social/Interpersonal	Small talk, eye contact	Masking, stigma in male teams	Exclusion from informal networks
Organizational/Env	Open-plan noise, multitasking	Lack of sponsors, overcompensation	Sensory capacity depleted before deep work
Structural/Cultural	Manager unawareness	Underrepresentation in leadership	Absence read as “lack of commitment”
Career Pathway	Coding tests, interviews	Gendered expectations, penalized breaks	Rejection due to interview small-talk difficulties

Qualitative data highlight unique manifestations of workplace exclusion:

• Lack of accommodation for non-linear thinking and alternative workflows, as in denial of simple system changes that would support ADHD (Chakraborty et al., 25 Oct 2025).
• Masking/camouflaging behaviors, with neurodivergent women altering speech or social presentation to avoid stigma, mirroring gendered adaptation but stemming from invisible differences (Chakraborty et al., 25 Oct 2025, Zaib et al., 5 Dec 2025).
• Compounded disbelief in dual-marginalized identities, e.g., “You don’t look autistic,” which echoes and intensifies general skepticism faced by women in engineering (Chakraborty et al., 25 Oct 2025).
• Career stagnation and attrition due to refusal of accommodations or misinterpretation of neurodivergent traits as lack of commitment (Chakraborty et al., 25 Oct 2025).
• Mental health outcomes: Burnout and anxiety, often linked directly to chronic masking and organizational inflexibility (Chakraborty et al., 25 Oct 2025).

5. Underdiagnosis, Masking, and the “Invisible Load”

A recurrent theme is pervasive underdiagnosis and late diagnosis of neurodivergent women, attributed to historically male-centric diagnostic criteria. This often results in undetected executive dysfunction and the proliferation of impostor syndrome (Zaib et al., 5 Dec 2025). Masking behaviors—quantified as the discrepancy between internal state and performed behavior—exert significant affective and cognitive cost, correlating with heightened anxiety and depression. These factors cause neurodivergent women to expend additional labor to “fit in,” aggravating burnout and reducing psychological safety (Zaib et al., 5 Dec 2025).

6. Organizational and Methodological Recommendations

Recent research prescribes multifaceted strategies for supporting neurodivergent women in software engineering (Chakraborty et al., 25 Oct 2025, Zaib et al., 5 Dec 2025):

• Re-frame neurodiversity as a distinct identity: Abandoning a solely deficit/disability model permits recognition of unique strengths (e.g., pattern recognition, creativity) and incentivizes broader inclusion efforts.
• Universal design and flexible process norms: Implement written agendas, multiple channels for code review, and asynchronous communication by default, avoiding only ad-hoc, reactive accommodations.
• Neuro-affirmative leadership training: Educate managers about masking, sensory management, and the value of disclosure, analogous to mainstreaming unconscious-bias trainings for gender and race.
• Neuro-inclusive hiring practices: Remove exclusionary language from job descriptions, employ clear rubrics and alternative assessments (e.g., take-home tasks), and reduce reliance on small talk and ambiguous social cues during interviews.
• Foster psychological safety and allyship: Enable disclosure through safe spaces, mentorship, and peer support, reducing the penalty for visible difference and self-advocacy.
• Measurement and tracking: Disaggregate cognitive style from generalized “disability” in data collection to uncover domain-specific pain points and longitudinal impacts of interventions.

A plausible implication is that process-level redesign, rather than piecemeal accommodation, is necessary to counter persistent exclusion and attrition of neurodivergent women in engineering environments.

7. Limitations and Future Directions

Empirical coverage remains constrained by current survey instruments and analytic aggregation; existing studies lack the necessary granularity to enumerate or track neurodivergent women’s outcomes independently of broader “disability” populations (Chakraborty et al., 25 Oct 2025, Zaib et al., 5 Dec 2025). Proposed methodological advances to address this include the publication of validated intersectional personas, deployment of hybrid cognitive walkthrough workshops across teams, and the dissemination of toolkits specifically designed for neurodiversity and gender inclusion in SE (Zaib et al., 5 Dec 2025). Further research priorities include longitudinal evaluation of intervention efficacy and development of automated analytic supports for scalable adoption.

Collectively, these programmatic strands aim to surface and address the “invisible load” imposed on neurodivergent women and to embed intersectionality-aware inclusion mechanisms at every level of the software engineering lifecycle.