The four areas
Last updated 18 August 2026
IASER’s work is organised into four areas, with the seven projects proposed at launch continuing inside them as workstreams — same charter, same deliverable, same links. Every area is looking for two co-leads; an area without both seats confirmed stays forming, and nothing stalls the others.
You can choose the areas you want to be in, and nominate yourself as a co-lead. Both are open now. If you ranked the seven groups when they launched, you have already been placed in the area your first choice now sits inside.
For how groups are chartered and how they operate, see the RFP.
Community
Outreach, community building, and helping Muslim non-profits adopt AI well. Includes the non-profit AI acceleration work and the Islam, AI and Education project.
Workstreams: Islam, AI & Education, Non-profit AI Acceleration.
Seats: domain lead, program lead. Read the area page →
Tech
Ansari 4, JaleesBench and evaluation, and post-training open-weight models for AI sovereignty.
Workstreams: Ansari 4, Islamic Benchmarking, Post-training Frontier Open-Weight Models for AI Sovereignty.
Seats: domain lead, program lead. Read the area page →
Policy and Research
Government and regulatory work where AI touches Muslim communities, and the research that supports it, including standards for what may call itself an Islamic AI assistant.
Workstreams: Policy, Standards for Islamic AI.
Seats: domain lead, program lead. Read the area page →
Org Foundations
Newsletters, marketing, fundraising, events, and everything that keeps IASER running. This is the area that makes the other three possible.
Workstreams: none yet — this area provides continuous service rather than a deliverable, and is opt-in only.
Seats: coordinator, backup. Read the area page →
The workstreams
Each workstream below is a summary. The full charter for every one is published — mission, deliverable, scope, milestones, and what it needs — and each summary links to it. Every workstream has a named owner within its area.
In Community
Islam, AI & Education
Build an adaptive learning platform for Islamic education of 13–18 year olds.
First deliverable, staged: a curriculum and pedagogy specification grounded in the tradition with a scholarly review loop; then a prototype covering one subject with a pilot cohort; then iteration toward a deployable platform.
In scope from day one: 13–18 means minors. Privacy, safeguarding, and parental consent belong in the charter’s scope section at the start, not discovered later.
Non-profit AI Acceleration
Help Muslim non-profits adopt AI well. The founder’s sketch has three stages — case studies, transformation services, and an AI-first operating system for non-profits — and stage one is the charterable unit.
First deliverable: a published case-study collection of Muslim non-profits that have successfully implemented AI (target 8–12 organizations, interviews plus write-ups). It is finishable, needs no code, and its findings de-risk the later stages: what do non-profits actually adopt, and where does it stick? Stages two and three become successor charters informed by what stage one learns.
In Tech
Ansari 4
Build the next generation of Ansari on the MultiSage expert-panel architecture: several frontier models consulted in parallel, combined with Ansari’s Islamic knowledge tools — Qur’an search, hadith retrieval across the major collections, prayer and date awareness, scholarly citations — with a facilitator model deciding when to consult the panel and synthesizing cited answers, including comparative fiqh perspectives across madhabs.
First deliverable: a deployed Ansari 4 (alpha → member beta → public), evaluated against Ansari 3 before public release.
Grounding: scholarly review is essential here, not decorative. Panel synthesis must not launder weak opinions into confident answers.
Works with: Islamic Benchmarking provides the evaluation gate; Standards for Islamic AI would make Ansari 4 the reference implementation.
Islamic Benchmarking
Take JaleesBench and run with it.
First deliverable: JaleesBench v2 and beyond — new scenarios past the current 140, additional pressures and framings, judge-agreement improvements, the results browser, dataset and rubric maintenance, and the cross-tradition family the paper commits to. The construct is faith-general; Islam is instance one.
Explicit responsibility: serving as the evaluation gate for Ansari 4 and Post-training for AI Sovereignty is part of the job, not a favour.
Readiness: this one charters first — volunteers are already asking.
Post-training Frontier Open-Weight Models for AI Sovereignty
Post-train frontier open-weight models to hold Islamic values, and release the recipes and weights openly — so the community owns frontier models aligned with its values, rather than renting them.
Which model: base-model selection is part of the work. Inkling (Thinking Machines) is the leading candidate but only an example — Nemotron is also on the list. Inkling’s case is empirical: mid-pack out of the box, but the best guided ceiling of any model tested, with near-zero steadfastness drop under pressure. The values fit in a page of guidance and the model holds them, which is the ideal profile for baking them in through post-training.
First deliverable: a frontier open-weight model post-trained to hold Islamic values, evaluated on JaleesBench against its base model and the guided ceiling, with the recipe and weights diff released openly.
Starts with: defining the input to the post-training. A values spec grounded in Qur’an, Sunnah and maqasid is the starting hypothesis, not a foregone conclusion — and whatever form it takes, it is a religious document. Scholarly review here is load-bearing, not decorative.
Works with: the tightest loop in the pipeline. This group trains, Islamic Benchmarking measures, Standards specifies.
In Policy and Research
Policy
Shape IASER’s policy work on AI where it touches Muslim communities.
The charter is deliberately open. The co-leads shape it with the founder rather than inheriting a fixed plan. The option space includes: reactive — policy briefs and submissions when regulation touches Muslim communities; proactive — a policy agenda drawn from IASER’s five principles; service — governance toolkits for Muslim organizations adopting AI; or convening — an annual policy convening rather than a standing output.
Charters last, once its co-leads and the founder have shaped the deliverable. It launches with the rest as forming.
Standards for Islamic AI
For something to call itself an Islamic AI assistant, what criteria should it meet?
First deliverable: a published standard — versioned, versionable, and testable wherever possible. Sourcing and citation requirements (does it cite real hadith, and classical sources?), knowledge floors, professed-values alignment, conduct under pressure, disclosure requirements (madhab positioning, “not a mufti” boundaries, when to refer to a scholar), and data and privacy expectations. Possibly tiered — self-attested, benchmarked, reviewed — rather than binary.
Start with: a definition workshop. Is this a standard (measurable criteria) or a guidance document (what builders should consider)? The answer changes the deliverable.
Scope discipline: standards are where volunteer groups most easily become debating societies. Version 0.1 covering assistants only — not all “Islamic AI applications” — is the right first scope.