EDSAFE AI Catalyst Fellowship 2026
- Omran Aburayya
- Oct 29
- 4 min read
Updated: 7 days ago
If you’re someone working at the intersection of education, policy, edtech, or school leadership — and you want to help shape how AI can be used responsibly and equitably in learning — the EDSAFE AI Catalyst Fellowship 2026 may be an extraordinary opportunity. Applications are open now, and here’s a full breakdown of what you need to know to apply and succeed.
Fellowship Summary
Location: Virtual + at least one in-person gathering (global, remote format)
Organizer: EDSAFE AI Alliance (by InnovateEDU)
Program Type: Fellowship (Catalyst) — year-long, interdisciplinary, cohort model
Target Group: Education sector leaders — e.g. administrators, teachers, policymakers, edtech professionals, community advocates
Thematic Areas: Responsible AI in education; SAFE (Safety, Accountability, Fairness, Efficacy) framework; design and deployment of AI-enabled systems in education
Duration: 1 year (with cohort engagement, virtual workshops, and a culminating capstone)
Coverage: The fellowship covers participation (i.e. there is no fee to the fellow) and provides mentorship, network access, resources — though it doesn’t explicitly list a stipend or salary support in public materials
Application Deadline: November 30, 2025, at 10 p.m. EST
Start / Announcement: Fellows will be announced in January 2026
📚 Fellowship Overview
The Catalyst Fellowship is designed to bring together leaders on both the “supply” side (edtech, industry, policy) and “demand” side (schools, districts, communities) to collaboratively reimagine AI-enabled learning systems.
Here’s how the program typically unfolds:
Interactive Learning Workshops
Fellows engage in virtual seminars and workshops led by global experts in AI, ethics, policy, and education. These sessions are not just theoretical—they’re structured to help participants grapple with emerging topics using the SAFE framework.
Cohort Learning + Mentorship
You’ll learn alongside ~30 other fellows, exchanging ideas, feedback, and cross-context insights. The cohort is supported by past EDSAFE Fellows who serve as mentors. Once or more during the year, there will be an in-person gathering to deepen connections.
Project / Micro Capstone
Toward the end of the fellowship, each fellow will design and implement a micro capstone project in their local context (a “problem of practice”) — e.g. AI literacy in classrooms, data privacy solutions, equitable AI use, policy pilots, etc.
Ongoing Collaboration & Impact
The goal is not just learning but producing actionable outputs. Fellows contribute to the global discourse, share lessons learned, and help shape policy and practice in AI and education.
Because the fellowship is remote-first and globally oriented, it’s relatively flexible for participants based in different countries (though travel may be expected for any in-person meeting).
🎁 Benefits & What You Gain
If selected, fellows can expect:
Access to Expert Knowledge: Exposure to cutting-edge research, policy debates, and trends in AI and education through workshops, seminars, readings, and discussions.
Mentorship & Network: Connection with past fellows and global leaders, which can open doors to collaborations, funding, and influence.
Practical Impact: The capstone enables you to design and test interventions in your own context — not just a theoretical exercise.
Visibility & Voice: You become part of a global community shaping AI policy in education; your insights may feed into research, publications, policy conversations.
Professional Growth: Deepening your capacity to lead, design, and critique AI systems from an ethical and equity lens.
Because the fellowship is funded (i.e. no cost to fellows), it lowers the barrier of entry.
✅ Eligibility Criteria & What They Look For
Professional Role / Leadership: You should already be working (or positioned) in education, technology, policy, research, or a related field, with capacity to influence.
Commitment to Equity & Ethics: The fellowship is deeply rooted in the SAFE framework (Safety, Accountability, Fairness, Efficacy). Demonstrating your alignment with these values is essential.
Contextual Leverage: They favor fellows who can apply their learnings in real contexts (e.g. schools, districts, edtech orgs)—your local leverage matters.
Collaborative Mindset: Because this is cohort-based, they likely favor candidates open to cross-cultural collaboration, feedback, iteration, and co-creation.
Capacity to Deliver: You should have enough bandwidth/time and institutional support (if applicable) to see your capstone through.
Global & Diverse Representation: Applications are open worldwide, and the fellowship aims to foster a diverse community of voices.
In your application, make clear your vision, pain point you want to address, stakeholders, and how you plan to scale or sustain impact beyond the fellowship.
📝 Application Procedure
Application Form: This is submitted via the EDSAFE Application Form
Personal Statement / Vision: An essay or narrative describing your interest in AI in education, your goals, and how you would leverage the fellowship for meaningful impact.
Project Proposal Sketch: Outline of a potential capstone or problem of practice: what you aim to do, in what context, rationale, intended beneficiaries, and possible outcome metrics.
CV / Resume: Highlight your relevant experience (education, leadership, projects, publications, etc.)
Letters of Recommendation / Institutional Support: Some calls require recommendation letters or endorsement from your institution or supervisor.
Make sure to submit before the deadline (Nov 30, 2025, 10 p.m. EST) — late applications are explicitly not accepted.
👌🏻 Tips & Insights to Strengthen Your Application
Articulate clearly what problem you will address in your educational context, and how AI solutions can be responsibly deployed (within SAFE).
Frame your capstone with measurable outcomes, sustainability, and scalability in mind.
Emphasize how collaboration and learning from peers will enrich your approach.
Showcase prior relevant experience (even small pilot projects in education or tech).
Demonstrate awareness of AI ethics, fairness, bias, privacy — not just enthusiasm for AI.
If possible, show that you have some institutional support or pathway to implement your capstone locally (school, district, edtech).
Prepare your recommendation letters early with clear guidance to your referees about points to emphasize (leadership, impact, alignment with values).
