The SPARC-AI Fellowship: Empowering Rural Women Through AI

Artificial Intelligence (AI) is reshaping industries and economies across the globe, yet disparities in access to these technologies remain significant. In India, women from rural areas often encounter barriers that prevent them from utilizing digital tools and business resources. The SPARC-AI Fellowship seeks to address this gap by equipping underserved communities, particularly women in Self-Help Groups (SHGs), with vital AI knowledge and resources to enhance their livelihoods.

Overview of the Fellowship

The SPARC-AI Fellowship is a year-long initiative designed for early-career professionals eager to make a difference in rural communities. Participants will engage deeply with local women entrepreneurs, helping them to leverage AI tools that can elevate their business operations, bolster income, and foster economic independence.

This fellowship offers a unique blend of AI education, community interaction, field research, and capacity building. Its main goals are to enhance AI adoption in rural settings and develop responsible technology users among young professionals.

Key Details

• Duration: 12 months
• Location: Assigned parliamentary constituencies in India
• Monthly Stipend: ₹40,000

Eligibility Criteria

The SPARC-AI Fellowship aims to attract driven individuals, ideally ranging from 23 to 30 years of age, who are passionate about technology, community development, and women’s empowerment in rural settings.

Essential Requirements

Applicants must meet the following criteria:

• Possession of a bachelor’s degree and either a master’s degree or enrollment in a master’s program by the start date of the fellowship.
• Previous field experience working with communities via NGOs, fellowships, or research initiatives.
• Fluency in the local language of the assigned constituency.
• Strong English communication skills.
• Proven emotional intelligence and ability to build trust with rural women.
• Willingness to relocate for a full year and travel within the assigned constituency.

Desirable Skills

While not mandatory, the following qualifications would enhance a candidate’s application:

• Experience in conducting surveys, interviews, and field research.
• Ability to create and edit instructional videos using a smartphone.
• Strong analytical and problem-solving skills.
• Interest in practical applications of AI, with prior experience being a plus.
• Familiarity with SHGs and community development initiatives.

Women and candidates from the programme’s operational regions are particularly encouraged to apply.

Benefits of the Fellowship

The SPARC-AI Fellowship offers a robust professional development experience while contributing to grassroots initiatives.

What Fellows Can Expect

• Extensive training in AI and hands-on experience in rural applications.
• Opportunity to work closely with MPs and district administration.
• Engagement in field research and community programme implementation.
• Access to a national fellowship network and mentorship.
• Professional references upon completion from Swaniti and the respective MP’s office.

The fellowship is structured to improve employability by marrying technical knowledge with community engagement and program management experience.

Programme Phases: A Year of Impact

The SPARC-AI Fellowship is divided into three distinct phases over the 12-month period:

Phase 1: Listen and Learn (Months 1–3)

This beginning phase involves understanding the economic challenges and opportunities faced by women in SHGs:

• Collaborate with district officials and SHG federations.
• Conduct surveys and interviews to document women’s livelihoods.
• Compile a comprehensive report detailing findings and potential AI applications.

Phase 2: Build and Train (Months 4–7)

During this phase, research findings will be transformed into practical AI resources:

• Create task cards and instructional videos tailored to SHG members.
• Train selected local women, named Saathis, to facilitate training sessions.

Phase 3: Iterate, Improve and Initiate (Months 8–12)

The final phase focuses on evaluating the programme’s impact:

• Conduct household visits to gauge the usage of AI tools.
• Address adoption challenges and gather community feedback.
• Facilitate community-led transitions for long-term sustainability.

Stipend and Working Conditions

Each selected Fellow will receive a monthly stipend of ₹40,000 throughout the fellowship duration.

Fellows will be required to stay full-time in their designated constituency, conducting regular field visits, attending SHG meetings, and engaging with local officials. Field travel expenses will be covered adequately by the respective MP’s office.

Application Process and Deadlines

The application for the SPARC-AI Fellowship 2026 must be submitted by 30 November 2026. Candidates can access the application form through the official website.

Application Requirements

• Personal information including contact details.
• Language proficiency overview.
• Confirmation of residency in the assigned constituency for the entire year.
• An updated CV (max two pages) highlighting qualifications and experiences.
• A cover letter detailing motivation and suitability.
• A one-minute video introducing the applicant and their motivations.

Candidate accuracy in submission is vital, including compliance with the guidelines outlined in the declaration.

Final Thoughts

The SPARC-AI Fellowship presents an invaluable opportunity for young professionals to gain hands-on experience in AI while championing women’s entrepreneurship and digital inclusion in rural India. By merging advanced technology with grassroots development, this initiative ensures that the advancements of artificial intelligence benefit even the most underserved communities.

For More Information

To apply, visit the official SPARC-AI Fellowship website.

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Disclaimer: Global South Opportunities (GSO) provides these listings for informational purposes and does not take responsibility for the accuracy of the information. Applicants are advised to verify details directly with the source before applying.