Mapped Processes
The institution knows where AI is used and how it connects to academic and administrative workflows.
External institutional review
AI-Native University Accreditation
External institutional review for universities where AI is part of teaching, learning, assessment, student support, governance, and quality assurance.
Many universities now use AI somewhere. That alone does not make them AI-native.
AAC’s AI-Native University Accreditation pathway is designed for institutions that can show how AI is connected to institutional purpose, academic standards, student experience, human oversight, evidence, governance, and continuous improvement.
This is not a review of how many tools a university uses. It is a review of whether AI use is organized, explainable, responsible, and quality-assured.
Responsible AI. Quality Assurance. Institutional Trust.
Institutional coherence
What AI-Native University Accreditation Means
AI-Native University Accreditation provides an external quality assurance review of an institution’s AI-enabled model.
It looks at how AI is used across academic, administrative, student-facing, and quality assurance functions. The focus is not on novelty. The focus is on institutional coherence.
A university may use advanced tools and still be weakly governed. Another institution may use fewer tools but have clearer responsibility, better evidence, stronger oversight, and a more credible quality assurance system.
AINU Accreditation is designed to review that difference.
Beyond a collection of tools
What Makes a University AI-Native?
An AI-native university is not built by adding tools to old processes and calling the result innovation. It is built when AI-supported processes are mapped, governed, reviewed, and connected to the institution’s educational mission.
Clear Responsibility
People remain accountable. AI support does not blur responsibility for academic, institutional, or student-impacting decisions.
Human Oversight
AI may support work, but academic judgment, expert review, assessment decisions, and governance authority remain human-led.
Evidence and Monitoring
The institution can show evidence of use, effectiveness, risk management, review, and improvement.
Quality Assurance Connection
AI use is connected to internal QA, external review, continuous improvement, and institutional standards.
Accurate Communication
Students, staff, partners, and external stakeholders receive clear and proportionate explanations of how AI is used.
i
AI-native does not mean using more AI. It means using AI purposefully, transparently, responsibly, and under quality assurance.
Who it is for
Institutions and stakeholders shaping accountable AI use
The pathway is designed for higher education institutions and quality assurance stakeholders that need a credible external perspective on an AI-enabled institutional model.
AI-Enabled Universities
For institutions that already use AI across academic, student support, administrative, or quality assurance processes.
Online and Hybrid Institutions
For institutions where digital delivery, learning platforms, analytics, and AI-supported services are central to the student experience.
Universities Building AI Governance
For institutions moving from scattered AI use toward policies, responsibilities, oversight, evidence, and institutional control.
Institutions Seeking External Review
For universities that want an external quality assurance perspective on whether their AI-enabled model is coherent and credible.
New or Transforming Institutions
For institutions designing new AI-supported models of teaching, assessment, support, operations, or governance.
International Partners and QA Stakeholders
For organizations interested in how AI-native institutional development can be reviewed, benchmarked, and governed.
Three distinct stages
The AINU Accreditation Pathway
The pathway moves from participation in the AI+QA Institute ecosystem to preliminary evidence review and, where appropriate, formal institutional accreditation review.
01Membership
Membership is the entry point into the AAC AI+QA Institute ecosystem. It provides access to selected resources, professional orientation, briefings, and pathway information. Membership is not accreditation.
Indicative timing: normally confirmed immediately after acceptance and payment.02Candidate for AINU Accreditation
Candidate status is a preliminary evidence-based stage. It may indicate that AAC has reviewed initial information and that the institution may proceed toward full AINU Accreditation Review, subject to AAC procedures.
Candidate status is not accreditation, approval, certification, recognition, endorsement, or a guarantee of final outcome.
Indicative timing: up to 4 weeks after complete submission, scope confirmation, and payment where applicable.03AINU Accreditation
AINU Accreditation is the formal institutional review. It examines whether the institution’s AI-enabled model is coherent, governed, evidence-based, human-supervised, and quality-assured.
Formal accreditation requires a separate review procedure and AAC decision.
Indicative timing: up to 10 weeks after complete submission, scope confirmation, and payment where applicable.i
Progression from membership to Candidate status and from Candidate status to AINU Accreditation Review is not automatic. Each stage requires separate application, review, payment where applicable, documentation, and AAC decision. Indicative timing starts after scope confirmation, complete documentation, and payment where applicable.
Preliminary stage
Candidate for AINU Accreditation
Candidate for AINU Accreditation is a preliminary stage for institutions that have submitted enough initial information to be considered potentially suitable for a full AI-Native University Accreditation Review.
Candidate status can help an institution organize its evidence, clarify its AI-enabled model, and identify readiness gaps before full review.
It must not be presented as accreditation itself.
i
Candidate status is not accreditation, approval, endorsement, certification, recognition, or a guarantee of the final outcome.
Formal institutional procedure
AINU Accreditation Review
The formal process confirms the institutional scope, examines evidence, enables clarification and discussion, and supports human-led AAC decision-making.
Scope confirmation
AAC confirms the institutional scope and the AI-enabled model to be reviewed.
Application and documentation
The institution submits required information, policies, evidence, process descriptions, and supporting documents.
Evidence review
AAC reviews the materials against AINU expectations and quality assurance principles.
Clarification questions
AAC may request additional explanations, evidence, interviews, or corrections.
Expert review
Qualified experts review the case within the defined institutional scope.
Institutional discussion
Where applicable, AAC may conduct meetings, interviews, or a structured online or site-based discussion.
Review report
A report or review record is prepared according to AAC procedures.
Decision
The relevant AAC body makes the accreditation decision.
Public wording
Where applicable, public wording or listing is issued using approved AAC language.
Monitoring or renewal
Accredited status may be subject to monitoring, renewal, or scope review where applicable.
The same AINU Accreditation expectations apply to all reviewed institutions. Review activities, evidence requests, meetings, and documentation requirements may vary depending on the institution’s profile, delivery model, jurisdiction, and scope of AI-enabled activity.
Evidence-based review domains
What AAC Reviews
AINU Accreditation looks at whether AI use is connected to institutional quality. The review is not a technology beauty contest. It examines whether AI-supported activity is governed, evidenced, explainable, and aligned with the institution’s academic mission.
Strategy and Institutional Purpose
Is AI use connected to the institution’s mission, academic model, and strategic development?
Governance and Accountability
Are responsibilities, oversight structures, policies, escalation routes, and decision boundaries clear?
Teaching and Learning
How does AI support curriculum delivery, learning design, personalization, academic support, and student engagement?
Assessment and Academic Integrity
How are assessment, feedback, academic integrity, authorship, authenticity, and human judgment protected?
Student Support and Progression
How does the institution use AI to support student guidance, retention, wellbeing, progression, and completion?
Admissions and Recruitment
Are AI-supported admissions, recruitment, communication, and applicant guidance processes transparent and responsible?
Institutional Analytics and Decision Support
How are analytics, dashboards, early alerts, institutional intelligence, and decision-support tools governed?
Quality Assurance and Continuous Improvement
Is AI use connected to internal QA, monitoring, review cycles, evidence, and continuous improvement?
Staff Capacity and Professional Development
Are academic, administrative, and QA staff prepared to use and supervise AI responsibly?
Risk, Data, and Transparency
Are risks, data categories, privacy considerations, communication duties, and stakeholder expectations identified and managed?
Human Oversight
Where academic, institutional, or student-impacting decisions are involved, is meaningful human oversight preserved?
Public Claims and Status Accuracy
Does the institution communicate its AI use and AAC status accurately, without overstating recognition or approval?
Responsible AI and human-led accreditation
Technology may assist. Accountability remains human.
AINU Accreditation follows the same governance principle as the wider AAC AI+QA Institute. AI may assist with organizing materials, preparing evidence inventories, structuring questions, or supporting review administration where properly governed.
AI does not decide accreditation outcomes, replace expert judgment, assign final status, or create evidence.
Formal accreditation decisions remain with AAC’s relevant body.
Human-led accreditation
AI may support the process.Experts exercise judgment.AAC owns the decision.
Fees and review stages
A staged institutional review structure
Fees correspond to distinct review stages and do not create or guarantee a formal AAC outcome.
Preliminary stageCandidate for AINU Accreditation Review
Launch fee until Sept 15, 2026USD 2,500
Standard fee after Sept 15, 2026USD 5,000
Founding Member future feeUSD 3,000
Typical timing: up to 4 weeks after complete submission, scope confirmation, and payment where applicable.
Formal reviewAINU Accreditation Review
Launch fee until Sept 15, 2026USD 9,000
Standard fee after Sept 15, 2026USD 18,000
Founding Member future feeUSD 10,800
Typical timing: up to 10 weeks after complete submission, scope confirmation, and payment where applicable.
Ongoing stageAnnual AINU Monitoring
Launch fee until Sept 15, 2026USD 1,200
Standard fee after Sept 15, 2026USD 2,000
Founding Member future feeUSD 1,200
Indicative timing: depends on monitoring scope, documentation, and AAC procedures.
i
Fees are subject to AAC’s official fee schedule, scope confirmation, eligibility review, and applicable AAC procedures. Payment of a fee does not guarantee Candidate status, accreditation, or any formal AAC outcome.
Application and next step
Begin with an institutional AINU inquiry
AAC begins by understanding the institution’s AI-enabled model and confirming the scope that may be suitable for the AINU Accreditation pathway.
Submit AINU inquiry
The institution contacts AAC and briefly describes its AI-enabled institutional model.
Confirm scope
AAC clarifies whether the case concerns institutional AI use, a defined unit, a delivery model, or a broader AI-native institutional claim.
Join AI+QA Institute
Membership is normally the entry point into the AI+QA Institute ecosystem.
Submit initial materials
The institution submits required information, policies, evidence, process descriptions, and explanations.
Candidate review
AAC may review whether the institution is suitable for Candidate for AINU Accreditation.
Full AINU Accreditation Review
Where appropriate, the case proceeds to full AI-Native University Accreditation Review.
Decision and public wording
AAC confirms any outcome and approved public wording according to its procedures.
Why pursue AINU Accreditation now
Move from AI activity to institutional coherence
AI use inside universities is becoming harder to explain with generic policy statements. Students, staff, partners, regulators, and quality assurance bodies increasingly need to understand how AI is actually used, governed, monitored, and connected to academic quality.
AINU Accreditation helps institutions move from scattered AI activity to a clearer institutional model.
- Clarify where AI is used and why
- Connect AI activity to teaching, assessment, student support, and QA
- Strengthen governance and accountability
- Prepare evidence for external review
- Protect human academic judgment
- Improve public communication
- Avoid overstated or vague AI claims
- Show that innovation and quality assurance can work together
Your next step
Build an AI-native institution that can be explained.
AINU Accreditation helps higher education institutions demonstrate that AI use is not only active, but governed, evidenced, human-supervised, and connected to academic quality.
External institutional review
AI-Native University Accreditation
External institutional review for universities where AI is part of teaching, learning, assessment, student support, governance, and quality assurance.
Many universities now use AI somewhere. That alone does not make them AI-native.
AAC’s AI-Native University Accreditation pathway is designed for institutions that can show how AI is connected to institutional purpose, academic standards, student experience, human oversight, evidence, governance, and continuous improvement.
This is not a review of how many tools a university uses. It is a review of whether AI use is organized, explainable, responsible, and quality-assured.
Responsible AI. Quality Assurance. Institutional Trust.
Institutional coherence
What AI-Native University Accreditation Means
AI-Native University Accreditation provides an external quality assurance review of an institution’s AI-enabled model.
It looks at how AI is used across academic, administrative, student-facing, and quality assurance functions. The focus is not on novelty. The focus is on institutional coherence.
A university may use advanced tools and still be weakly governed. Another institution may use fewer tools but have clearer responsibility, better evidence, stronger oversight, and a more credible quality assurance system.
AINU Accreditation is designed to review that difference.
Beyond a collection of tools
What Makes a University AI-Native?
An AI-native university is not built by adding tools to old processes and calling the result innovation. It is built when AI-supported processes are mapped, governed, reviewed, and connected to the institution’s educational mission.
Mapped Processes
The institution knows where AI is used and how it connects to academic and administrative workflows.
Clear Responsibility
People remain accountable. AI support does not blur responsibility for academic, institutional, or student-impacting decisions.
Human Oversight
AI may support work, but academic judgment, expert review, assessment decisions, and governance authority remain human-led.
Evidence and Monitoring
The institution can show evidence of use, effectiveness, risk management, review, and improvement.
Quality Assurance Connection
AI use is connected to internal QA, external review, continuous improvement, and institutional standards.
Accurate Communication
Students, staff, partners, and external stakeholders receive clear and proportionate explanations of how AI is used.
i
AI-native does not mean using more AI. It means using AI purposefully, transparently, responsibly, and under quality assurance.
Who it is for
Institutions and stakeholders shaping accountable AI use
The pathway is designed for higher education institutions and quality assurance stakeholders that need a credible external perspective on an AI-enabled institutional model.
AI-Enabled Universities
For institutions that already use AI across academic, student support, administrative, or quality assurance processes.
Online and Hybrid Institutions
For institutions where digital delivery, learning platforms, analytics, and AI-supported services are central to the student experience.
Universities Building AI Governance
For institutions moving from scattered AI use toward policies, responsibilities, oversight, evidence, and institutional control.
Institutions Seeking External Review
For universities that want an external quality assurance perspective on whether their AI-enabled model is coherent and credible.
New or Transforming Institutions
For institutions designing new AI-supported models of teaching, assessment, support, operations, or governance.
International Partners and QA Stakeholders
For organizations interested in how AI-native institutional development can be reviewed, benchmarked, and governed.
Three distinct stages
The AINU Accreditation Pathway
The pathway moves from participation in the AI+QA Institute ecosystem to preliminary evidence review and, where appropriate, formal institutional accreditation review.
01Membership
Membership is the entry point into the AAC AI+QA Institute ecosystem. It provides access to selected resources, professional orientation, briefings, and pathway information. Membership is not accreditation.
Indicative timing: normally confirmed immediately after acceptance and payment.02Candidate for AINU Accreditation
Candidate status is a preliminary evidence-based stage. It may indicate that AAC has reviewed initial information and that the institution may proceed toward full AINU Accreditation Review, subject to AAC procedures.
Candidate status is not accreditation, approval, certification, recognition, endorsement, or a guarantee of final outcome.
Indicative timing: up to 4 weeks after complete submission, scope confirmation, and payment where applicable.03AINU Accreditation
AINU Accreditation is the formal institutional review. It examines whether the institution’s AI-enabled model is coherent, governed, evidence-based, human-supervised, and quality-assured.
Formal accreditation requires a separate review procedure and AAC decision.
Indicative timing: up to 10 weeks after complete submission, scope confirmation, and payment where applicable.i
Progression from membership to Candidate status and from Candidate status to AINU Accreditation Review is not automatic. Each stage requires separate application, review, payment where applicable, documentation, and AAC decision. Indicative timing starts after scope confirmation, complete documentation, and payment where applicable.
Preliminary stage
Candidate for AINU Accreditation
Candidate for AINU Accreditation is a preliminary stage for institutions that have submitted enough initial information to be considered potentially suitable for a full AI-Native University Accreditation Review.
Candidate status can help an institution organize its evidence, clarify its AI-enabled model, and identify readiness gaps before full review.
It must not be presented as accreditation itself.
i
Candidate status is not accreditation, approval, endorsement, certification, recognition, or a guarantee of the final outcome.
Formal institutional procedure
AINU Accreditation Review
The formal process confirms the institutional scope, examines evidence, enables clarification and discussion, and supports human-led AAC decision-making.
Scope confirmation
AAC confirms the institutional scope and the AI-enabled model to be reviewed.
Application and documentation
The institution submits required information, policies, evidence, process descriptions, and supporting documents.
Evidence review
AAC reviews the materials against AINU expectations and quality assurance principles.
Clarification questions
AAC may request additional explanations, evidence, interviews, or corrections.
Expert review
Qualified experts review the case within the defined institutional scope.
Institutional discussion
Where applicable, AAC may conduct meetings, interviews, or a structured online or site-based discussion.
Review report
A report or review record is prepared according to AAC procedures.
Decision
The relevant AAC body makes the accreditation decision.
Public wording
Where applicable, public wording or listing is issued using approved AAC language.
Monitoring or renewal
Accredited status may be subject to monitoring, renewal, or scope review where applicable.
The same AINU Accreditation expectations apply to all reviewed institutions. Review activities, evidence requests, meetings, and documentation requirements may vary depending on the institution’s profile, delivery model, jurisdiction, and scope of AI-enabled activity.
Evidence-based review domains
What AAC Reviews
AINU Accreditation looks at whether AI use is connected to institutional quality. The review is not a technology beauty contest. It examines whether AI-supported activity is governed, evidenced, explainable, and aligned with the institution’s academic mission.
Strategy and Institutional Purpose
Is AI use connected to the institution’s mission, academic model, and strategic development?
Governance and Accountability
Are responsibilities, oversight structures, policies, escalation routes, and decision boundaries clear?
Teaching and Learning
How does AI support curriculum delivery, learning design, personalization, academic support, and student engagement?
Assessment and Academic Integrity
How are assessment, feedback, academic integrity, authorship, authenticity, and human judgment protected?
Student Support and Progression
How does the institution use AI to support student guidance, retention, wellbeing, progression, and completion?
Admissions and Recruitment
Are AI-supported admissions, recruitment, communication, and applicant guidance processes transparent and responsible?
Institutional Analytics and Decision Support
How are analytics, dashboards, early alerts, institutional intelligence, and decision-support tools governed?
Quality Assurance and Continuous Improvement
Is AI use connected to internal QA, monitoring, review cycles, evidence, and continuous improvement?
Staff Capacity and Professional Development
Are academic, administrative, and QA staff prepared to use and supervise AI responsibly?
Risk, Data, and Transparency
Are risks, data categories, privacy considerations, communication duties, and stakeholder expectations identified and managed?
Human Oversight
Where academic, institutional, or student-impacting decisions are involved, is meaningful human oversight preserved?
Public Claims and Status Accuracy
Does the institution communicate its AI use and AAC status accurately, without overstating recognition or approval?
Responsible AI and human-led accreditation
Technology may assist. Accountability remains human.
AINU Accreditation follows the same governance principle as the wider AAC AI+QA Institute. AI may assist with organizing materials, preparing evidence inventories, structuring questions, or supporting review administration where properly governed.
AI does not decide accreditation outcomes, replace expert judgment, assign final status, or create evidence.
Formal accreditation decisions remain with AAC’s relevant body.
Human-led accreditation
AI may support the process.Experts exercise judgment.AAC owns the decision.
Fees and review stages
A staged institutional review structure
Fees correspond to distinct review stages and do not create or guarantee a formal AAC outcome.
Preliminary stageCandidate for AINU Accreditation Review
Launch fee until Sept 15, 2026USD 2,500
Standard fee after Sept 15, 2026USD 5,000
Founding Member future feeUSD 3,000
Typical timing: up to 4 weeks after complete submission, scope confirmation, and payment where applicable.
Formal reviewAINU Accreditation Review
Launch fee until Sept 15, 2026USD 9,000
Standard fee after Sept 15, 2026USD 18,000
Founding Member future feeUSD 10,800
Typical timing: up to 10 weeks after complete submission, scope confirmation, and payment where applicable.
Ongoing stageAnnual AINU Monitoring
Launch fee until Sept 15, 2026USD 1,200
Standard fee after Sept 15, 2026USD 2,000
Founding Member future feeUSD 1,200
Indicative timing: depends on monitoring scope, documentation, and AAC procedures.
i
Fees are subject to AAC’s official fee schedule, scope confirmation, eligibility review, and applicable AAC procedures. Payment of a fee does not guarantee Candidate status, accreditation, or any formal AAC outcome.
Application and next step
Begin with an institutional AINU inquiry
AAC begins by understanding the institution’s AI-enabled model and confirming the scope that may be suitable for the AINU Accreditation pathway.
Submit AINU inquiry
The institution contacts AAC and briefly describes its AI-enabled institutional model.
Confirm scope
AAC clarifies whether the case concerns institutional AI use, a defined unit, a delivery model, or a broader AI-native institutional claim.
Join AI+QA Institute
Membership is normally the entry point into the AI+QA Institute ecosystem.
Submit initial materials
The institution submits required information, policies, evidence, process descriptions, and explanations.
Candidate review
AAC may review whether the institution is suitable for Candidate for AINU Accreditation.
Full AINU Accreditation Review
Where appropriate, the case proceeds to full AI-Native University Accreditation Review.
Decision and public wording
AAC confirms any outcome and approved public wording according to its procedures.
Why pursue AINU Accreditation now
Move from AI activity to institutional coherence
AI use inside universities is becoming harder to explain with generic policy statements. Students, staff, partners, regulators, and quality assurance bodies increasingly need to understand how AI is actually used, governed, monitored, and connected to academic quality.
AINU Accreditation helps institutions move from scattered AI activity to a clearer institutional model.
- Clarify where AI is used and why
- Connect AI activity to teaching, assessment, student support, and QA
- Strengthen governance and accountability
- Prepare evidence for external review
- Protect human academic judgment
- Improve public communication
- Avoid overstated or vague AI claims
- Show that innovation and quality assurance can work together
Your next step
Build an AI-native institution that can be explained.
AINU Accreditation helps higher education institutions demonstrate that AI use is not only active, but governed, evidenced, human-supervised, and connected to academic quality.