AI for Gender-Based Digital Exclusion Research

This post explains how AI-enabled qualitative research can accelerate analysis of gender-based digital exclusion, aimed at academic and program teams running community-based participatory research in low- and middle-income settings. The primary keyword for this post is gender-based digital exclusion. According to Shahil-Feroz et al. in PLOS One (2026), Pakistan faces wide digital gender gaps shaped by sociocultural norms and household gatekeepers. This article distills the PLOS One study protocol into actionable AI workflows that turn Urdu interviews, photo-elicitation outputs, and role-play transcripts into validated themes and co-designed recommendations.

Key Takeaways

According to the PLOS One study protocol by Shahil-Feroz et al. (2026) PLOS One, gender-based digital exclusion in a low-income Karachi community is driven by sociocultural norms, family gatekeepers, and structural barriers; the study will use participatory methods and reflexive thematic analysis to co-design interventions.

The study protocol published on 20 August 2026 plans a two-year CBPR project starting February 2026 with participant recruitment between August and November 2026 and data collection completed by April 2027, as reported in PLOS One (2026).
According to the authors citing the GSMA Mobile Gender Gap Report 2024, women in Pakistan were 38% less likely than men to own a mobile phone or access mobile internet in 2024.
The PLOS One protocol estimates approximately 36–45 participants for FGDs and interviews, and reports that community advisory committee members will be compensated PKR 4500 (16 USD); the project received a SSHRC grant of $70, 626, as stated in PLOS One (2026).

What Happened: study design and methods for investigating gender-based digital exclusion

The study protocol in PLOS One (2026) outlines a community-based participatory research (CBPR) project in Azam Basti, Karachi that combines photo-elicitation, role-play FGDs, in-depth interviews, and co-design workshops to explore gender-based digital exclusion.

According to Shahil-Feroz et al. in PLOS One (2026), the research is grounded in Technofeminism and comprises three phases: formation of a Community Advisory Committee (CAC), qualitative data collection with roughly 36–45 participants, and three co-design workshops to develop community-informed strategies.

The PLOS One protocol specifies that FGDs will be conducted in Urdu (or Sindhi when required), transcribed in the original language, translated into English, and analysed using reflexive thematic analysis with NVivo support, as detailed in the methods section.

Findings snapshot (planned metrics and implications)

Date	Metric	Value	Implication
20 August 2026	Protocol published	PLOS One	Public methods and timeline for the CBPR project
2024	Mobile gender gap (Pakistan)	Women 38% less likely to own/access mobile internet (GSMA 2024)	Quantifies the access deficit the study aims to contextualize
Feb 2026–Jan 2028	Project timeline	2 years, recruitment Aug–Nov 2026, data complete Apr 2027	Provides windows for data availability and policy briefs in late 2027
Project grant (2025 competition)	Funding	$70, 626 (SSHRC Insight Development Grant)	Resources for CBPR activities, CAC stipends, and transcription/translation
Sampling	Participant count	Approx. 36–45 participants across FGDs and interviews	Sufficient for reflexive thematic analysis and saturation per protocol

Implications for qualitative researchers and program teams

Use AI early to speed transcription and translation while preserving language nuance: the PLOS One protocol requires Urdu transcription and English translation, which is time consuming when done manually.

According to Shahil-Feroz et al. in PLOS One (2026), reflexive thematic analysis will be used, so researchers should preserve original-language transcripts and memos for audit trails and coder reflexivity.
The study emphasises engaging family gatekeepers and community leaders, so researchers should design recruitment and consent workflows that protect participant safety and confidentiality as required by ethics approvals listed in the protocol.

How Evidano Helps: map of common problems to AI-enabled qualitative workflows

Problem: Slow, error-prone transcription and translation

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

Solution: Use Evidano’s speech-to-text with custom dictionaries and PII redaction to transcribe Urdu FGDs and interviews and export time-stamped transcripts for coder review. See Evidano Speech-to-Text for capabilities and language support.

Problem: Manual, siloed coding across languages

Researchers following the PLOS One (2026) protocol need bilingual coding and NVivo-style organization, which is time consuming.

Solution: Evidano ingests original-language transcripts and translations, applies thematic and frequency analysis, and supports hierarchical codes and co-occurrence networks so teams can compare themes across participant groups for reflexive thematic analysis.

Problem: Slow co-design feedback loops to communities

The PLOS One protocol plans multiple co-design workshops and stakeholder materials that require rapid synthesis.

Solution: Evidano generates extractable theme summaries, verbatim quotes, and visuals researchers can use to create culturally appropriate briefs and arts-based outputs for community advisory committees; learn more on our features page.

FAQ: gender-based digital exclusion

What is gender-based digital exclusion and why does it matter for researchers?

Answer: Gender-based digital exclusion is the unequal access to and use of digital technologies between genders, and it matters because it shapes who benefits from digital services and data-driven programs.

According to the United Nations definition cited in PLOS One (2026), digital inclusion means "equitable, meaningful, and safe access to use, lead, and design of digital technologies, services, and associated opportunities for everyone, everywhere"; researchers must therefore study both access and agency.

Which qualitative methods does the PLOS One protocol use and why?

Answer: The protocol uses CBPR, photo-elicitation, role-play FGDs, and in-depth interviews to surface household norms and power relations.

Shahil-Feroz et al. in PLOS One (2026) explain that photo-elicitation and role-play are chosen to elicit sensitive information about gendered norms while co-design workshops produce community-informed solutions.

How can AI preserve ethical standards when analyzing sensitive qualitative data?

Answer: AI can preserve ethics by applying PII redaction, secure encryption, and human-in-the-loop review during transcription and coding.

The PLOS One (2026) protocol highlights ethics approvals and consent practices; platform features that match those protections are necessary to ensure participant confidentiality during automated processing.

How many participants does the study plan to recruit and when will results be available?

Answer: The protocol plans approximately 36–45 participants and expects results beginning in late 2027.

Shahil-Feroz et al. in PLOS One (2026) report a two-year project timeline starting February 2026 with recruitment in August–November 2026 and analysis concluding by early 2028.

Conclusion & Next Steps

AI-enabled qualitative workflows make the PLOS One (2026) CBPR design on gender-based digital exclusion faster to run and easier to translate into policy recommendations for stakeholders.

The PLOS One protocol documents specific dates, methods, and metrics (for example, a planned start of February 2026 and an anticipated release of findings in late 2027) that qualitative teams can align to when planning analysis capacity.

If you run qualitative studies that include multi-language transcription, photo-elicitation, and co-design outputs, you can reduce time to insight with targeted AI features for transcription, translation, thematic analysis, and visualization.

To pilot these workflows on your study materials, Try Evidano for free.

Topics

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

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