AI may support the process.
AI may assist with defined support tasks such as organizing documents, preparing evidence inventories, structuring questions, drafting administrative text from approved records, and supporting review preparation.
Responsible AI · Quality Assurance · Institutional Trust
Responsible AI Use for Accreditation
A human-led governance approach for using artificial intelligence in quality assurance without replacing expert judgment or AAC decision-making.
AI can support accreditation work. It can help organize materials, prepare evidence inventories, structure questions, compare documents, and support administrative drafting.
But accreditation depends on trust. Evidence must be real. Expert judgment must remain human. Confidentiality must be protected. Decisions must remain with the authorized body.
AAC’s approach is not automated accreditation. It is governed AI support for evidence-based, human-led quality assurance.
AAC has adopted this approach for its own accreditation, validation, monitoring, renewal, and related quality assurance procedures. The framework may also support agencies and institutions that want to define responsible AI use in their own review processes.
Accreditation governance
Why Responsible AI Use Matters
Accreditation is not only a document review. It is a trust process. Institutions submit evidence. Experts interpret it. Panels discuss it. Governance bodies make decisions. Public status is communicated to students, partners, regulators, and other stakeholders.
AI may help with some parts of this work, but it can also create new risks: unsupported summaries, hidden assumptions, fabricated evidence, unsafe handling of confidential data, misleading status claims, or quiet replacement of expert judgment.
Responsible AI use means that AI support must remain visible, controlled, validated, and accountable.
The framework
AAC’s Core Doctrine
AAC’s responsible AI approach is built around a simple governance rule.
Experts exercise judgment.
Experts remain responsible for interpreting evidence, asking questions, identifying strengths and weaknesses, assigning scores where applicable, proposing findings and conditions, and contributing to reports.
AAC owns the decision.
Formal AAC decisions remain with AAC’s authorized governance bodies. AI must not decide accreditation, validation, candidacy, monitoring outcomes, renewal, appeals, complaints, sanctions, or public status.
?
A useful test for any AI use: can it be explained, documented, checked, and defended if the case is questioned later?
Defined support tasks
Where AI May Assist
AAC allows AI to support defined tasks where the purpose is clear, confidentiality is protected, and a responsible human actor validates the output.
Document organization
Organizing case files, evidence folders, document inventories, and submission materials.
Evidence inventories
Preparing preliminary inventories of documents and mapping materials to standards for human review.
Question structuring
Helping experts or case officers organize possible clarification questions.
Report organization
Supporting structure, consistency, formatting, and administrative drafting from approved records.
Monitoring tables
Organizing condition fulfilment evidence, renewal materials, and monitoring submissions.
Public text from approved records
Drafting status wording or administrative public text only from approved AAC decisions and records.
i
AI support must remain visible, controlled, and subject to human validation where it may influence official materials.
Protected responsibilities
Where AI Must Not Be Used
Some tasks belong to human experts and AAC governance bodies. AI must not cross that line.
XDecide accreditation, validation, or Candidate status outcomes.
XAssign final scores or determine whether standards are met.
XReplace expert judgment or manipulate expert findings.
XFabricate evidence, generate false data, or conceal weaknesses.
XCreate fake records, committee minutes, or assessment samples.
XCreate false student feedback, employer feedback, or institutional data.
XRemove legitimate uncertainty from evidence or findings.
XDecide appeals, complaints, misconduct, or sanctions.
XProcess confidential materials through unapproved public tools.
XInflate, extend, or misrepresent AAC public status.
XPresent planned or recommended actions as completed practice.
XTransfer responsibility from an authorized person or body to a tool.
!
AI may assist with work. It may not assume responsibility.
Risk-based safeguards
Four Levels of AI Use
AAC classifies AI use by risk. The higher the risk, the stronger the safeguards.
Level 1
Permitted AI Use
Low-risk support, normally allowed where confidentiality and responsibility are respected. Examples include grammar correction, formatting, translation of non-sensitive materials, and general non-case-specific text.
Level 2
Controlled AI Use
Allowed under defined safeguards. Examples include preliminary evidence mapping, document inventories, expert question drafting, monitoring evidence tables, and report structuring support.
Level 3
Restricted AI Use
Allowed only with heightened safeguards or prior authorization. This may concern confidential evidence, personal data, draft expert reports, Commission materials, appeals, complaints, investigations, or sensitive status materials.
Level 4
Prohibited AI Use
Not allowed in AAC procedures. This includes deciding outcomes, assigning final scores, fabricating evidence, judging credibility, replacing expert judgment, or misrepresenting AAC status.
Accountability in practice
Human Validation and Records
AI output is not official merely because it was produced. It becomes usable only after responsible human validation.
Human validation
An authorized person checks the AI-assisted output against source documents, corrects errors, removes unsupported claims, preserves uncertainty, and confirms that judgment has not been outsourced to the tool.
Responsible human actors check, correct, confirm, or reject AI-assisted output before it enters official materials.
AI-use records and declarations
AAC may require AI-use records and declarations where AI materially supports accreditation, validation, monitoring, renewal, appeal, complaint, investigation, public communication, or related quality assurance procedures.
Material AI use may need to be documented so the process remains visible, accountable, reviewable, and defensible.
Approved Prompt Library
AAC has developed a controlled prompt library for different stakeholder groups. The library helps define what AI may be used for, by whom, and under what safeguards. It supports consistency, transparency, and responsible use without replacing human judgment.
i
Declarations are not intended to punish responsible AI use. They are intended to make material AI use visible.
Protected foundations
Evidence Integrity and Confidentiality
Responsible AI use protects both the authenticity of the case and the information entrusted to the review process.
Evidence integrity
Accreditation depends on authentic evidence. AI may help describe, organize, translate, or present evidence. It must not invent it.
AI must not create or misrepresent committee minutes, approval records, policies, survey data, student or employer feedback, assessment samples, staff records, LMS records, contracts, QA reports, or monitoring evidence.
Planned or recommended actions must not be presented as completed practice.
Confidentiality and data protection
Confidential AAC or institutional materials must not be processed through unapproved AI tools.
This may include self-evaluation reports, contracts, financial materials, student and staff data, assessment samples, LMS records, expert notes, draft reports, Commission materials, monitoring submissions, appeals, complaints, and investigation records.
No efficiency gain justifies unsafe disclosure of confidential accreditation materials.
Accurate communication
Public Status Accuracy
AI-generated marketing language can easily overstate a status. AAC requires accurate public wording.
AI-generated public language must not expand the status AAC actually granted.
- Membership is not accreditation.
- Candidate status is not full accreditation.
- Validation is not institutional accreditation unless expressly granted.
- Program accreditation is not institutional accreditation.
- AI+QA Institute participation is not validation or accreditation.
- INSELECT listing is not validation or accreditation.
- Recognition claims must not exceed AAC’s actual status and scope.
Shared governance language
Who This Framework Is For
The framework supports organizations and people responsible for credible external quality assurance and accreditation practice.
Quality Assurance Agencies
For agencies developing rules on how AI may be used in external review, accreditation, monitoring, and decision preparation.
Higher Education Institutions
For institutions preparing self-evaluation, evidence, policies, AI-use declarations, and responsible submissions.
Experts and Reviewers
For reviewers who may use AI only for support tasks that do not replace professional judgment.
QA Governance Bodies
For accreditation commissions, councils, boards, and decision-making bodies that need to preserve authority, due process, confidentiality, and public trust when AI is used in quality assurance workflows.
Consultants and Service Providers
For external consultants and technology providers supporting institutions or accreditation-related workflows.
International Partners
For ministries, public authorities, networks, and organizations interested in responsible AI use in quality assurance.
Practical cooperation
How AAC Can Support Agencies and Institutions
AAC can share its responsible AI governance approach with institutions, agencies, and partners that want to use AI without weakening the credibility of quality assurance.
Orientation and briefings
- Introductory briefings
- Framework presentations
- Agency-to-agency exchange
Policy and capability
- Policy and procedure mapping
- Reviewer training
- Responsible AI use workshops
Tools and implementation
- AI-use declaration and record templates
- Controlled prompt libraries for stakeholder groups
- Pilot design and workflow review
- Public communication guidance
i
Support may include briefings, workshops, framework adaptation discussions, pilot design, and review of AI use across accreditation-related procedures. AAC does not impose its framework on other agencies.
Connected pathways
Connection with AI+QA Institute Pathways
Responsible AI use provides a shared governance foundation across the wider AI+QA Institute ecosystem.
Membership
Members may access selected resources, briefings, and orientation related to responsible AI use and quality assurance.
AI+QA Validation
Validation reviews consider whether AI-enabled platforms, solutions, systems, services, or providers demonstrate responsible AI use, evidence, transparency, and human oversight.
AI-Native University Accreditation
AINU Accreditation reviews whether institutions use AI purposefully, responsibly, transparently, and under quality assurance.
Next step
Request a Responsible AI Use Briefing
Begin with a focused conversation about the organization, procedure, or governance question you want to address.
Submit inquiry
Contact AAC and briefly explain your interest in responsible AI use for accreditation or quality assurance.
Clarify focus
AAC clarifies whether the discussion concerns agency procedures, institutional submissions, reviewer practice, validation, AINU, monitoring, or public communication.
Select format
AAC may propose a briefing, workshop, policy discussion, pilot conversation, or framework presentation.
Follow-up
Where appropriate, AAC may support further development, documentation, training, or cooperation.
Responsible AI · Quality Assurance · Institutional Trust
Use AI without weakening trust.
AAC’s Responsible AI Use approach helps institutions, agencies, experts, and partners understand where AI can support accreditation work — and where human judgment, evidence integrity, confidentiality, data protection, and formal decision-making must remain protected.
Responsible AI · Quality Assurance · Institutional Trust
Responsible AI Use for Accreditation
A human-led governance approach for using artificial intelligence in quality assurance without replacing expert judgment or AAC decision-making.
AI can support accreditation work. It can help organize materials, prepare evidence inventories, structure questions, compare documents, and support administrative drafting.
But accreditation depends on trust. Evidence must be real. Expert judgment must remain human. Confidentiality must be protected. Decisions must remain with the authorized body.
AAC’s approach is not automated accreditation. It is governed AI support for evidence-based, human-led quality assurance.
AAC has adopted this approach for its own accreditation, validation, monitoring, renewal, and related quality assurance procedures. The framework may also support agencies and institutions that want to define responsible AI use in their own review processes.
Accreditation governance
Why Responsible AI Use Matters
Accreditation is not only a document review. It is a trust process. Institutions submit evidence. Experts interpret it. Panels discuss it. Governance bodies make decisions. Public status is communicated to students, partners, regulators, and other stakeholders.
AI may help with some parts of this work, but it can also create new risks: unsupported summaries, hidden assumptions, fabricated evidence, unsafe handling of confidential data, misleading status claims, or quiet replacement of expert judgment.
Responsible AI use means that AI support must remain visible, controlled, validated, and accountable.
The framework
AAC’s Core Doctrine
AAC’s responsible AI approach is built around a simple governance rule.
AI may support the process.
AI may assist with defined support tasks such as organizing documents, preparing evidence inventories, structuring questions, drafting administrative text from approved records, and supporting review preparation.
Experts exercise judgment.
Experts remain responsible for interpreting evidence, asking questions, identifying strengths and weaknesses, assigning scores where applicable, proposing findings and conditions, and contributing to reports.
AAC owns the decision.
Formal AAC decisions remain with AAC’s authorized governance bodies. AI must not decide accreditation, validation, candidacy, monitoring outcomes, renewal, appeals, complaints, sanctions, or public status.
?
A useful test for any AI use: can it be explained, documented, checked, and defended if the case is questioned later?
Defined support tasks
Where AI May Assist
AAC allows AI to support defined tasks where the purpose is clear, confidentiality is protected, and a responsible human actor validates the output.
Document organization
Organizing case files, evidence folders, document inventories, and submission materials.
Evidence inventories
Preparing preliminary inventories of documents and mapping materials to standards for human review.
Question structuring
Helping experts or case officers organize possible clarification questions.
Report organization
Supporting structure, consistency, formatting, and administrative drafting from approved records.
Monitoring tables
Organizing condition fulfilment evidence, renewal materials, and monitoring submissions.
Public text from approved records
Drafting status wording or administrative public text only from approved AAC decisions and records.
i
AI support must remain visible, controlled, and subject to human validation where it may influence official materials.
Protected responsibilities
Where AI Must Not Be Used
Some tasks belong to human experts and AAC governance bodies. AI must not cross that line.
XDecide accreditation, validation, or Candidate status outcomes.
XAssign final scores or determine whether standards are met.
XReplace expert judgment or manipulate expert findings.
XFabricate evidence, generate false data, or conceal weaknesses.
XCreate fake records, committee minutes, or assessment samples.
XCreate false student feedback, employer feedback, or institutional data.
XRemove legitimate uncertainty from evidence or findings.
XDecide appeals, complaints, misconduct, or sanctions.
XProcess confidential materials through unapproved public tools.
XInflate, extend, or misrepresent AAC public status.
XPresent planned or recommended actions as completed practice.
XTransfer responsibility from an authorized person or body to a tool.
!
AI may assist with work. It may not assume responsibility.
Risk-based safeguards
Four Levels of AI Use
AAC classifies AI use by risk. The higher the risk, the stronger the safeguards.
Level 1
Permitted AI Use
Low-risk support, normally allowed where confidentiality and responsibility are respected. Examples include grammar correction, formatting, translation of non-sensitive materials, and general non-case-specific text.
Level 2
Controlled AI Use
Allowed under defined safeguards. Examples include preliminary evidence mapping, document inventories, expert question drafting, monitoring evidence tables, and report structuring support.
Level 3
Restricted AI Use
Allowed only with heightened safeguards or prior authorization. This may concern confidential evidence, personal data, draft expert reports, Commission materials, appeals, complaints, investigations, or sensitive status materials.
Level 4
Prohibited AI Use
Not allowed in AAC procedures. This includes deciding outcomes, assigning final scores, fabricating evidence, judging credibility, replacing expert judgment, or misrepresenting AAC status.
Accountability in practice
Human Validation and Records
AI output is not official merely because it was produced. It becomes usable only after responsible human validation.
Human validation
An authorized person checks the AI-assisted output against source documents, corrects errors, removes unsupported claims, preserves uncertainty, and confirms that judgment has not been outsourced to the tool.
Responsible human actors check, correct, confirm, or reject AI-assisted output before it enters official materials.
AI-use records and declarations
AAC may require AI-use records and declarations where AI materially supports accreditation, validation, monitoring, renewal, appeal, complaint, investigation, public communication, or related quality assurance procedures.
Material AI use may need to be documented so the process remains visible, accountable, reviewable, and defensible.
Approved Prompt Library
AAC has developed a controlled prompt library for different stakeholder groups. The library helps define what AI may be used for, by whom, and under what safeguards. It supports consistency, transparency, and responsible use without replacing human judgment.
i
Declarations are not intended to punish responsible AI use. They are intended to make material AI use visible.
Protected foundations
Evidence Integrity and Confidentiality
Responsible AI use protects both the authenticity of the case and the information entrusted to the review process.
Evidence integrity
Accreditation depends on authentic evidence. AI may help describe, organize, translate, or present evidence. It must not invent it.
AI must not create or misrepresent committee minutes, approval records, policies, survey data, student or employer feedback, assessment samples, staff records, LMS records, contracts, QA reports, or monitoring evidence.
Planned or recommended actions must not be presented as completed practice.
Confidentiality and data protection
Confidential AAC or institutional materials must not be processed through unapproved AI tools.
This may include self-evaluation reports, contracts, financial materials, student and staff data, assessment samples, LMS records, expert notes, draft reports, Commission materials, monitoring submissions, appeals, complaints, and investigation records.
No efficiency gain justifies unsafe disclosure of confidential accreditation materials.
Accurate communication
Public Status Accuracy
AI-generated marketing language can easily overstate a status. AAC requires accurate public wording.
AI-generated public language must not expand the status AAC actually granted.
- Membership is not accreditation.
- Candidate status is not full accreditation.
- Validation is not institutional accreditation unless expressly granted.
- Program accreditation is not institutional accreditation.
- AI+QA Institute participation is not validation or accreditation.
- INSELECT listing is not validation or accreditation.
- Recognition claims must not exceed AAC’s actual status and scope.
Shared governance language
Who This Framework Is For
The framework supports organizations and people responsible for credible external quality assurance and accreditation practice.
Quality Assurance Agencies
For agencies developing rules on how AI may be used in external review, accreditation, monitoring, and decision preparation.
Higher Education Institutions
For institutions preparing self-evaluation, evidence, policies, AI-use declarations, and responsible submissions.
Experts and Reviewers
For reviewers who may use AI only for support tasks that do not replace professional judgment.
QA Governance Bodies
For accreditation commissions, councils, boards, and decision-making bodies that need to preserve authority, due process, confidentiality, and public trust when AI is used in quality assurance workflows.
Consultants and Service Providers
For external consultants and technology providers supporting institutions or accreditation-related workflows.
International Partners
For ministries, public authorities, networks, and organizations interested in responsible AI use in quality assurance.
Practical cooperation
How AAC Can Support Agencies and Institutions
AAC can share its responsible AI governance approach with institutions, agencies, and partners that want to use AI without weakening the credibility of quality assurance.
Orientation and briefings
- Introductory briefings
- Framework presentations
- Agency-to-agency exchange
Policy and capability
- Policy and procedure mapping
- Reviewer training
- Responsible AI use workshops
Tools and implementation
- AI-use declaration and record templates
- Controlled prompt libraries for stakeholder groups
- Pilot design and workflow review
- Public communication guidance
i
Support may include briefings, workshops, framework adaptation discussions, pilot design, and review of AI use across accreditation-related procedures. AAC does not impose its framework on other agencies.
Connected pathways
Connection with AI+QA Institute Pathways
Responsible AI use provides a shared governance foundation across the wider AI+QA Institute ecosystem.
Membership
Members may access selected resources, briefings, and orientation related to responsible AI use and quality assurance.
AI+QA Validation
Validation reviews consider whether AI-enabled platforms, solutions, systems, services, or providers demonstrate responsible AI use, evidence, transparency, and human oversight.
AI-Native University Accreditation
AINU Accreditation reviews whether institutions use AI purposefully, responsibly, transparently, and under quality assurance.
Next step
Request a Responsible AI Use Briefing
Begin with a focused conversation about the organization, procedure, or governance question you want to address.
Submit inquiry
Contact AAC and briefly explain your interest in responsible AI use for accreditation or quality assurance.
Clarify focus
AAC clarifies whether the discussion concerns agency procedures, institutional submissions, reviewer practice, validation, AINU, monitoring, or public communication.
Select format
AAC may propose a briefing, workshop, policy discussion, pilot conversation, or framework presentation.
Follow-up
Where appropriate, AAC may support further development, documentation, training, or cooperation.
Responsible AI · Quality Assurance · Institutional Trust
Use AI without weakening trust.
AAC’s Responsible AI Use approach helps institutions, agencies, experts, and partners understand where AI can support accreditation work — and where human judgment, evidence integrity, confidentiality, data protection, and formal decision-making must remain protected.