AI Qualitative Research for Gender-Based Digital Exclusion

Gender-based digital exclusion is the uneven access and use of digital technologies by gender, and researchers need scalable qualitative methods to surface norms, gatekeepers, and co-designed solutions. According to the PLOS One study protocol by Shahil-Feroz et al. (2026), women in Pakistan face sociocultural and household barriers that reduce mobile ownership and internet use, and this post explains how AI-enabled qualitative research can accelerate analysis, validation, and community-facing outputs for that work. The primary keyword for this post is gender-based digital exclusion and the audience is qualitative researchers, program leads, and policy analysts designing inclusion interventions in low-resource settings.

Key Takeaways

According to the PLOS One study protocol (Shahil-Feroz et al., 2026) PLOS One, gender-based digital exclusion in a low-income Karachi neighbourhood will be explored using community-based participatory research with approximately 36–45 participants across FGDs, interviews, and co-design workshops.

The PLOS One protocol (published 20 August 2026) schedules the two-year project to start in February 2026 and expects participant recruitment from August–November 2026.
According to the PLOS One protocol (Shahil-Feroz et al., 2026), Pakistan ranked 145 out of 146 on the Global Gender Gap Index in 2024, and the GSMA reported women were 38% less likely than men to own a mobile or access mobile internet in 2024.
The PLOS One protocol (Shahil-Feroz et al., 2026) plans reflexive thematic analysis of Urdu transcripts translated to English and co-analysis with a Community Advisory Committee to co-design solutions with marginalized women and household gatekeepers.

What happened and how this study works

The PLOS One study protocol (Shahil-Feroz et al., 2026) outlines a community-based participatory research design in Azam Basti, Karachi, that combines focus group discussions, in-depth interviews, and three co-design workshops to investigate gender-based digital exclusion.

The PLOS One protocol (Shahil-Feroz et al., 2026) specifies three phases: formation of a Community Advisory Committee, Phase 2 qualitative data collection with approximately 36–45 participants, and Phase 3 co-design workshops with 25–30 participants in total.

The PLOS One protocol (Shahil-Feroz et al., 2026) explicitly uses photo-elicitation and role-playing methods and plans reflexive thematic analysis using Braun and Clarke’s approach with NVivo for coding.

Findings snapshot

Date / Source	Metric	Value	Implication
2024 / GSMA (cited in PLOS One)	Relative likelihood women own mobile or access mobile internet	Women 38% less likely than men	Interventions must target gendered ownership and shared-device dynamics
2024 / World Economic Forum (cited in PLOS One)	Global Gender Gap rank for Pakistan	145 of 146 countries	Structural gender barriers are severe and cross-sectoral
Aug 20, 2026 / PLOS One protocol	Planned qualitative sample	Approximately 36–45 participants across FGDs and interviews	Study sized for depth and theoretical saturation, not population estimates
Feb 2026–Jan 2028 / PLOS One timeline	Project budget (SSHRC grant)	$70, 626 (Insight Development Grant, 2025 competition)	Feasible two-year CBPR with community advisory inputs

Implications for qualitative researchers and program teams

Answer-first: Researchers and program teams must design qualitative studies that capture sociocultural norms, household gatekeepers, and co-designed solutions rather than only measuring device ownership.

The PLOS One protocol (Shahil-Feroz et al., 2026) recommends engaging family gatekeepers including male partners and elderly women, because the study authors found household decision-making and norms are central to women’s technology access.

The PLOS One protocol (Shahil-Feroz et al., 2026) employs photo-elicitation and role-play because visual and participatory methods can surface tacit norms that direct questioning may miss; the protocol states that photo sets were reviewed by the Community Advisory Committee and “are non-identifiable.”

When planning timelines, the PLOS One protocol (Shahil-Feroz et al., 2026) expects recruitment to begin in August 2026 and primary data collection to finish by April 2027, so funders and teams should budget for translation, co-analysis, and sustained community engagement through 2027.

How Evidano helps (problem → AI feature mappings)

Definition-first: What is Evidano?

Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.

Problem: Manually transcribing, translating, and coding Urdu FGDs and interviews is slow and error-prone; PLOS One (Shahil-Feroz et al., 2026) plans transcription and translation before reflexive analysis.

Solution: Evidano offers transcription and translation tools with custom dictionaries and PII redaction to accelerate Phase 2 workflows while preserving cultural terms for accurate coding. See Evidano features at Evidano features.

Problem: Fragmented thematic coding and co-analysis

The PLOS One protocol (Shahil-Feroz et al., 2026) describes co-analysis with a Community Advisory Committee to validate themes, which requires versioned coding artifacts and easy comparison across coders.

Evidano solution: Use Evidano’s thematic, content frequency, and cross-segment analyses to produce sharable codebooks and visualizations that the advisory committee can review, accelerating consensus without sacrificing reflexivity.

Problem: Producing community-facing outputs from qualitative data

The PLOS One protocol (Shahil-Feroz et al., 2026) plans arts-based knowledge translation materials including videos, infographics, and voice messages.

Evidano solution: Export clean, evidence-tagged quotations and frequency summaries to inform scripts and storyboards, and use Evidano’s AI chat over your documents to draft accessible stakeholder briefs and policy summaries that match the study’s co-designed recommendations.

Problem: Maintaining data security and ethical transcript handling

The PLOS One protocol (Shahil-Feroz et al., 2026) requires informed consent, translation, and careful storage of transcripts.

Evidano solution: Evidano encrypts data and does not use customer data to train third-party models; teams can document consent and redaction steps inside the platform and streamline ethics-compliant workflows. Learn more at Evidano data security.

FAQ: gender-based digital exclusion

What is gender-based digital exclusion and why study it qualitatively?

Answer-first: Gender-based digital exclusion is the gap in access and meaningful use of digital technologies between genders, and qualitative study reveals the sociocultural norms and household dynamics that explain how and why the gap persists.

Supporting detail: The PLOS One protocol (Shahil-Feroz et al., 2026) argues that measuring ownership alone misses household gatekeepers, and therefore uses photo-elicitation and role-play to surface norms described in the study as “deeply entrenched sociocultural barriers.”

How large is the gender gap in Pakistan according to the PLOS One protocol?

Answer-first: According to PLOS One (Shahil-Feroz et al., 2026), GSMA data cited in the protocol shows women in Pakistan were 38% less likely than men to own a mobile phone or access mobile internet in 2024.

Supporting detail: The protocol also cites the Global Gender Gap Index placing Pakistan 145 of 146 countries in 2024, highlighting structural context for qualitative findings.

Why involve family gatekeepers in digital inclusion research?

Answer-first: Involving family gatekeepers reveals decision-making processes and norms that determine whether women can access devices or use the internet.

Supporting detail: The PLOS One protocol (Shahil-Feroz et al., 2026) explicitly recruits male partners, brothers, and elderly women for FGDs and role-play because these actors manage access and social acceptability in Azam Basti.

Can AI tools safely speed transcription and analysis for studies like this?

Answer-first: Yes, AI tools can speed transcription and translation while preserving accuracy if custom dictionaries, PII redaction, and human review are used.

Supporting detail: The PLOS One protocol (Shahil-Feroz et al., 2026) plans bilingual transcription and translation before thematic analysis; Evidano’s speech-to-text and translation features can be configured with local terms and then validated by bilingual coders to match the protocol’s quality needs. See Evidano speech-to-text at Evidano speech-to-text.

What quotes from the PLOS One protocol are most useful to report?

Answer-first: Short verbatim quotes that reflect framing and methods strengthen summaries.

Supporting detail: Use direct lines such as the United Nations definition quoted in PLOS One, “equitable, meaningful, and safe access to use, lead, and design of digital technologies, services, and associated opportunities for everyone, everywhere” (United Nations, cited in PLOS One), and the authors’ phrasing that the gap stems from "deeply entrenched sociocultural barriers" (Shahil-Feroz et al., 2026).

Conclusion & Next Steps

Answer-first: The PLOS One protocol (Shahil-Feroz et al., 2026) makes a clear case that addressing gender-based digital exclusion requires participatory qualitative methods that engage household gatekeepers and community leaders to co-design solutions.

Researchers can follow the PLOS One timeline (project start February 2026, recruitment August–November 2026, primary data collection completed by April 2027) to budget translation and co-analysis time into grants and operational plans.

If you run qualitative projects that need faster, auditable transcription, translation, and thematic synthesis for community co-design, Evidano can accelerate those steps while supporting ethical data handling; learn more at Evidano features.

Next step: Prepare your codebook and consent language to capture co-design outputs and then Try Evidano for free to pilot AI-enabled transcription and thematic analysis for your qualitative study.

Topics

gender-based digital exclusion
digital gender inclusion
qualitative research Pakistan
AI qualitative analysis

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