WG001 · Research operating unit
What is the reference architecture of an AI-native educational institution, and how can that architecture be implemented, evidenced, and transferred across institutions? Choose how to contribute — or understand the broader research area first.
Working Group WG001
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Institutional Architecture for Education is an operating unit under Research & Evidence. Working Group WG001 is operational metadata.
Primary actions
Charter
What is the reference architecture of an AI-native educational institution, and how can that architecture be implemented, evidenced, and transferred across institutions?
Flagship working group for universities and academic systems that need an architecture for intelligence transformation, not isolated AI tools. Forming in public with an open research agenda.
Open Questions
The smallest unit of the Research Network is not membership — it is an open research question.
What durable pieces of institutional design — capability, evidence, decision rights, ownership — must be redesigned before AI tooling proliferates?
What memory and feedback lets an institution compound learning from everyday work rather than only from reports?
Does assessment belong in institutional architecture as its own building block, or primarily as evidence produced by capability and practice?
If capability, evidence, and decision design become primary, what institutional role do departments still play?
Should curriculum remain primary, or should learner and faculty capability become primary with curriculum as pathway structure?
Which capabilities must remain institution-owned for trust and exit readiness — and which can be platform-enabled?
First contribution opportunity
Review the proposed canonical primitives for an AI-native educational institution and contribute examples, objections, missing concepts, or institutional constraints.
Target publication window: 2026-09-30 · Staff owner: research
Success definition: WG001-WN002 is published with substantive input from at least five external contributors across at least three stakeholder categories, with every contribution traceable through the Research queue.
After invited
01
Staff invite after conversation; person receives the WG001 brief.
02
Confirms role, contribution interest, and attribution preference.
03
Assigned a specific review of WG001-WN002 primitives — not a vague membership ask.
04
Uses the structured review format (examples, objections, missing concepts, constraints).
05
ATRISI records accepted suggestions, unresolved disagreements, and institutional examples.
06
Credit per attribution policy; anonymous institutional examples allowed when requested.
07
research_status → active only after meaningful contribution, not after inquiry alone.
Help ATRISI define durable institutional primitives for AI-native education by reviewing a draft taxonomy before it hardens into doctrine.
What you are asked to do
What you are not asked to do
Time expectation: 2–4 hours of focused review over 2–3 weeks, plus optional attendance at one monthly discussion.
First cohort target: 8–12 people
Quotation
Written submissions may be quoted or paraphrased in working notes unless the contributor marks a passage as off-record or confidential.
Credit
Named contributors are acknowledged in the published note when they consent. Role-only or anonymous institutional examples are supported.
Co-authorship
Co-authorship is offered when a contributor shapes substantive structure or text across multiple primitives, not for a single comment. ATRISI confirms co-authorship in writing before publication.
Institutional examples
Examples may remain anonymous at institutional or individual request. Anonymized examples are still counted as contributions for activation.
Conflicts of interest
Contributors should disclose commercial, employment, or vendor relationships relevant to the primitives under review.
Editorial responsibility
ATRISI retains editorial responsibility for WG001 publications while the working group is convened by ATRISI. Disagreements that remain unresolved will be published as open questions, not forced consensus.
Monthly discussion
One focused WG001 discussion per month (optional for async reviewers).
Working notes
One working note every 6–8 weeks while the group is active.
Async review
Written review is the default contribution mode.
Quarterly synthesis
One public synthesis of accepted changes and open disagreements each quarter.
Pilots
Pilot discussions begin only after primitives and capability/evidence architecture are sufficiently stable.
Submit review against WG001-WN002 using this structure. Staff will record outcomes in the Research queue.
Track inside Ops / staff notes first — do not publish all of these on /impact yet.
Publication sequence
Working notes, frameworks, and pilot reports follow a durable code so the research operation stays legible before a larger CMS exists.
Opening note: architecture is the missing layer between AI ambition and institutional compounding.
Review draft of institutional primitives — contributors submit examples, objections, missing concepts, and constraints.
Relate capability development, evidence collection, and decision systems across institutional levels.
Reference model distilled from working notes, pilots, and evidence.
First institutional pilot report connecting architecture claims to observed evidence.
Working notes
WG001-WN001
Working Note 001 for WG001. Argues that universities fail at AI adoption when they treat transformation as procurement, and introduces institutional architecture as the missing layer between strategy, programs, platforms, and evidence.
Read WG001-WN001WG001-WN002
Working Note 002 for WG001. Proposes an initial set of institutional primitives for critique. Contributors should submit examples, objections, missing concepts, and institutional constraints using the structured review format.
Read WG001-WN002Source material
Initiatives
Flagship initiative under WG001 to define, critique, and validate a reference architecture for AI-native educational institutions — connecting capability, assessment, evidence, decisions, and platforms.
Can educational institutions compound AI adoption into durable institutional intelligence if they adopt an explicit architecture before accelerating tools?
Related frameworks
Framework
ATRISI framework for converting fragmented institutional activity into reusable intelligence across workflows, governance, research, and platforms.
Framework
A readiness model for universities evaluating AI adoption across faculty capability, assessment, governance, research, and institutional systems.
Framework
A research operating system model for AI-native knowledge continuity, artifact tracking, evidence synthesis, and reusable research workflows.
Collaborate
Join WG001 to critique the agenda, co-author the next note, contribute source material, or host a validation pilot. Evidence from implementation feeds the public Impact surface.
