AI / EdTech Platforms
For platforms supporting teaching, learning, assessment, student support, admissions, analytics, institutional services, or quality assurance.
External quality assurance review
AI+QA Validation
External quality assurance review for AI-enabled educational platforms, solutions, systems, services, and providers.
AI+QA Validation is an AAC pathway for non-university organizations operating in or around higher education, AI-enabled education, institutional support, quality assurance, assessment, admissions, analytics, student services, and related fields. It helps providers explain how their solutions are governed, evidenced, supervised, and aligned with institutional trust.
Responsible AI. Quality Assurance. Institutional Trust.
What validation means
What AI+QA Validation Means
AI+QA Validation provides an external quality assurance perspective on a defined platform, solution, system, service, or provider operating in higher education or related educational contexts.
The review considers whether the subject of validation is clearly described, educationally relevant, responsibly governed, supported by evidence, transparent about AI use, designed with human oversight, and understandable for institutions that may adopt or rely on it.
Who it is for
Education-facing providers across the AI+QA landscape
The pathway supports defined platforms, solutions, systems, services, and providers that need to communicate educational relevance, evidence, governance, and institutional readiness.
Service Providers
For organizations providing AI-enabled education services, implementation support, consulting, institutional tools, or managed solutions.
Assessment and Academic Integrity Tools
For solutions connected to assessment design, academic integrity, feedback, proctoring, evaluation support, or learning evidence.
Admissions and Student Recruitment Systems
For tools supporting applicant guidance, recruitment, admissions communication, student matching, or enrollment-related workflows.
Student Support and Analytics Solutions
For systems supporting student success, progression, advising, retention, early alerts, learning analytics, or institutional intelligence.
AI Startups
For early-stage providers preparing for institutional conversations, pilot design, QA-aware positioning, and possible later validation.
Three distinct stages
The AI+QA Validation Pathway
The pathway moves from professional participation to preliminary readiness review and, where appropriate, a formal evidence-based 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 validation.
Indicative timing: normally confirmed immediately after acceptance and payment.02Candidate for AI+QA Validation
Candidate status is a preliminary evidence-based stage. It may indicate that AAC has reviewed initial information and that the defined subject may proceed toward a full AI+QA Validation Review, subject to AAC procedures. Candidate status is not validation, approval, accreditation, certification, recognition, endorsement, or a guarantee of final outcome.
Indicative timing: up to 4 weeks after complete submission, scope confirmation, and payment where applicable.03AI+QA Validation
AI+QA Validation is the formal external quality assurance review. It applies only to the defined reviewed scope and leads to a validation decision by the relevant AAC body. Formal validation 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 Validation 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.
Evidence-based review domains
What AAC Reviews
AI+QA Validation focuses on quality assurance, governance, evidence, transparency, and institutional readiness. It is not a substitute for technical, legal, cybersecurity, or regulatory certification.
Purpose and Scope
Does the provider clearly explain what the solution does, who it serves, and where it should or should not be used?
Educational Relevance
Is the solution connected to real educational, institutional, student support, assessment, governance, or quality assurance needs?
Responsible AI Use
Is AI use transparent, governed, documented, proportionate, and aligned with human oversight?
Evidence and Effectiveness
Is there credible evidence of use, piloting, implementation, outcomes, user feedback, or institutional value?
Governance and Accountability
Are responsibilities, oversight arrangements, escalation routes, decision boundaries, and accountability mechanisms clear?
Human Oversight
Does the solution preserve meaningful human control where academic, institutional, quality assurance, or student-impacting judgments are involved?
Data, Privacy, and Risk Awareness
Are data categories, privacy risks, confidentiality issues, implementation risks, and operational safeguards identified?
Institutional Readiness
Can higher education institutions understand, adopt, monitor, and evaluate the solution responsibly?
Claims and Public Communication
Are marketing claims accurate, proportionate, and not misleading?
Quality Assurance Alignment
Does the solution support or respect quality assurance principles rather than bypassing them?
Preliminary stage
Candidate for AI+QA Validation
Candidate for AI+QA Validation is a preliminary stage for organizations, platforms, solutions, systems, or services that have submitted enough initial information to be considered potentially suitable for a full AI+QA Validation Review.
Candidate status may support structured preparation for full validation, but it must not be presented as validation itself.
i
Candidate status is not validation, approval, endorsement, certification, accreditation, recognition, or a guarantee of the final outcome.
Formal review procedure
AI+QA Validation Review
The formal process confirms the defined scope, examines evidence, enables clarification, and supports human-led AAC decision-making.
Scope confirmation
AAC confirms what exactly is being reviewed.
Application and documentation
The provider submits required information, evidence, policies, explanations, and supporting documents.
Evidence review
AAC reviews the materials against AI+QA Validation expectations.
Clarification questions
AAC may request additional explanations, evidence, or corrections.
Expert review
Qualified experts review the case within the defined scope.
Validation report
A report or review record is prepared according to AAC procedures.
Decision
The relevant AAC body makes the validation decision.
Public wording
Where applicable, public wording or listing is issued using approved AAC language.
Monitoring or renewal
Validated status may be subject to monitoring, renewal, or scope review where applicable.
The procedure may be adapted depending on the scope, maturity, risk, and nature of the platform, solution, system, service, or provider.
Accurate status description
Possible Validation Outcomes and Public Wording
Any public status must be accurate, proportionate, and specific to the scope AAC reviewed.
Candidate for AI+QA Validation
AI+QA Validated Platform
AI+QA Validated Solution
AI+QA Validated System
AI+QA Validated Service
i
Validation applies only to the defined reviewed scope. It must not imply institutional accreditation, program accreditation, legal approval, national recognition, cybersecurity certification, regulatory approval, or approval of all provider products and services.
Development and visibility route
INSELECT Route for AI Startups
Some AI startups may not yet be ready for full AI+QA Validation. For them, AI+QA Institute Membership may connect with INSELECT as a development and visibility route.
INSELECT can support visibility, positioning, institutional readiness, pilot preparation, ecosystem engagement, and market-facing presentation. When a startup becomes sufficiently mature, it may apply for the standard AI+QA Validation pathway.
AI+QA Institute Membership
→INSELECT development and visibility
→Possible later AI+QA Validation pathway
INSELECT is not accreditation, validation, certification, approval, recognition, endorsement, Candidate status, or a formal AAC quality assurance decision.
Responsible AI and Human-Led Review
Technology may assist. Accountability remains human.
AI+QA Validation 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 validation outcomes, replace expert judgment, assign final status, or create evidence.
Formal validation decisions remain with AAC’s relevant body.
Human-led review
AI may support the process.Experts exercise judgment.AAC owns the decision.
Fees and review stages
A staged review structure
Fees correspond to distinct review stages and do not create or guarantee a formal AAC outcome.
Preliminary stageCandidate for AI+QA Validation Review
Launch fee until Sept 15, 2026USD 3,000
Standard fee after Sept 15, 2026USD 6,000
Founding Member future feeUSD 3,600
Typical timing: up to 4 weeks after complete submission, scope confirmation, and payment where applicable.
Formal reviewAI+QA Validation Review
Launch fee until Sept 15, 2026USD 5,000
Standard fee after Sept 15, 2026USD 10,000
Founding Member future feeUSD 6,000
Typical timing: up to 10 weeks after complete submission, scope confirmation, and payment where applicable.
Ongoing stageAnnual AI+QA Validation Monitoring
Launch fee until Sept 15, 2026USD 900
Standard fee after Sept 15, 2026USD 1,500
Founding Member future feeUSD 900
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, validation, or any formal AAC outcome. Indicative timing starts after scope confirmation, complete documentation, and payment where applicable.
Application and next step
Begin with a defined validation inquiry
AAC begins by understanding what the provider wants reviewed and whether the proposed platform, solution, system, service, or provider scope is suitable for the validation pathway.
Submit validation inquiry
Briefly describe the platform, solution, system, service, or provider.
Confirm scope
AAC clarifies the precise subject and boundaries of review.
Join AI+QA Institute
Membership is normally the ecosystem entry point.
Submit initial materials
Provide the required documentation, evidence, and explanations.
Candidate review
AAC considers readiness for Candidate status.
Full Validation Review
Eligible cases may proceed to the formal review.
Decision and public wording
AAC confirms the outcome and any approved wording.
Why pursue validation now
Move from AI claims to QA-ready explanations
AI adoption in higher education is accelerating. Universities and quality assurance stakeholders increasingly need to understand not only what a tool does, but how it is governed, evidenced, monitored, and aligned with institutional responsibility.
AI+QA Validation helps providers move from generic AI claims to QA-ready explanations.
- Clarify their value proposition
- Strengthen trust with institutions
- Prepare for institutional review questions
- Demonstrate responsible AI governance
- Improve evidence and documentation
- Avoid overstated or misleading claims
- Speak the language of higher education quality assurance
Your next step
Prepare your AI-enabled solution for institutional trust.
AI+QA Validation helps education-facing platforms, solutions, systems, and service providers demonstrate responsible AI use, quality assurance awareness, evidence, governance, and readiness for higher education contexts.
External quality assurance review
AI+QA Validation
External quality assurance review for AI-enabled educational platforms, solutions, systems, services, and providers.
AI+QA Validation is an AAC pathway for non-university organizations operating in or around higher education, AI-enabled education, institutional support, quality assurance, assessment, admissions, analytics, student services, and related fields. It helps providers explain how their solutions are governed, evidenced, supervised, and aligned with institutional trust.
Responsible AI. Quality Assurance. Institutional Trust.
What validation means
What AI+QA Validation Means
AI+QA Validation provides an external quality assurance perspective on a defined platform, solution, system, service, or provider operating in higher education or related educational contexts.
The review considers whether the subject of validation is clearly described, educationally relevant, responsibly governed, supported by evidence, transparent about AI use, designed with human oversight, and understandable for institutions that may adopt or rely on it.
Who it is for
Education-facing providers across the AI+QA landscape
The pathway supports defined platforms, solutions, systems, services, and providers that need to communicate educational relevance, evidence, governance, and institutional readiness.
AI / EdTech Platforms
For platforms supporting teaching, learning, assessment, student support, admissions, analytics, institutional services, or quality assurance.
Service Providers
For organizations providing AI-enabled education services, implementation support, consulting, institutional tools, or managed solutions.
Assessment and Academic Integrity Tools
For solutions connected to assessment design, academic integrity, feedback, proctoring, evaluation support, or learning evidence.
Admissions and Student Recruitment Systems
For tools supporting applicant guidance, recruitment, admissions communication, student matching, or enrollment-related workflows.
Student Support and Analytics Solutions
For systems supporting student success, progression, advising, retention, early alerts, learning analytics, or institutional intelligence.
AI Startups
For early-stage providers preparing for institutional conversations, pilot design, QA-aware positioning, and possible later validation.
Three distinct stages
The AI+QA Validation Pathway
The pathway moves from professional participation to preliminary readiness review and, where appropriate, a formal evidence-based 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 validation.
Indicative timing: normally confirmed immediately after acceptance and payment.02Candidate for AI+QA Validation
Candidate status is a preliminary evidence-based stage. It may indicate that AAC has reviewed initial information and that the defined subject may proceed toward a full AI+QA Validation Review, subject to AAC procedures. Candidate status is not validation, approval, accreditation, certification, recognition, endorsement, or a guarantee of final outcome.
Indicative timing: up to 4 weeks after complete submission, scope confirmation, and payment where applicable.03AI+QA Validation
AI+QA Validation is the formal external quality assurance review. It applies only to the defined reviewed scope and leads to a validation decision by the relevant AAC body. Formal validation 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 Validation 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.
Evidence-based review domains
What AAC Reviews
AI+QA Validation focuses on quality assurance, governance, evidence, transparency, and institutional readiness. It is not a substitute for technical, legal, cybersecurity, or regulatory certification.
Purpose and Scope
Does the provider clearly explain what the solution does, who it serves, and where it should or should not be used?
Educational Relevance
Is the solution connected to real educational, institutional, student support, assessment, governance, or quality assurance needs?
Responsible AI Use
Is AI use transparent, governed, documented, proportionate, and aligned with human oversight?
Evidence and Effectiveness
Is there credible evidence of use, piloting, implementation, outcomes, user feedback, or institutional value?
Governance and Accountability
Are responsibilities, oversight arrangements, escalation routes, decision boundaries, and accountability mechanisms clear?
Human Oversight
Does the solution preserve meaningful human control where academic, institutional, quality assurance, or student-impacting judgments are involved?
Data, Privacy, and Risk Awareness
Are data categories, privacy risks, confidentiality issues, implementation risks, and operational safeguards identified?
Institutional Readiness
Can higher education institutions understand, adopt, monitor, and evaluate the solution responsibly?
Claims and Public Communication
Are marketing claims accurate, proportionate, and not misleading?
Quality Assurance Alignment
Does the solution support or respect quality assurance principles rather than bypassing them?
Preliminary stage
Candidate for AI+QA Validation
Candidate for AI+QA Validation is a preliminary stage for organizations, platforms, solutions, systems, or services that have submitted enough initial information to be considered potentially suitable for a full AI+QA Validation Review.
Candidate status may support structured preparation for full validation, but it must not be presented as validation itself.
i
Candidate status is not validation, approval, endorsement, certification, accreditation, recognition, or a guarantee of the final outcome.
Formal review procedure
AI+QA Validation Review
The formal process confirms the defined scope, examines evidence, enables clarification, and supports human-led AAC decision-making.
Scope confirmation
AAC confirms what exactly is being reviewed.
Application and documentation
The provider submits required information, evidence, policies, explanations, and supporting documents.
Evidence review
AAC reviews the materials against AI+QA Validation expectations.
Clarification questions
AAC may request additional explanations, evidence, or corrections.
Expert review
Qualified experts review the case within the defined scope.
Validation report
A report or review record is prepared according to AAC procedures.
Decision
The relevant AAC body makes the validation decision.
Public wording
Where applicable, public wording or listing is issued using approved AAC language.
Monitoring or renewal
Validated status may be subject to monitoring, renewal, or scope review where applicable.
The procedure may be adapted depending on the scope, maturity, risk, and nature of the platform, solution, system, service, or provider.
Accurate status description
Possible Validation Outcomes and Public Wording
Any public status must be accurate, proportionate, and specific to the scope AAC reviewed.
Candidate for AI+QA Validation
AI+QA Validated Platform
AI+QA Validated Solution
AI+QA Validated System
AI+QA Validated Service
i
Validation applies only to the defined reviewed scope. It must not imply institutional accreditation, program accreditation, legal approval, national recognition, cybersecurity certification, regulatory approval, or approval of all provider products and services.
Development and visibility route
INSELECT Route for AI Startups
Some AI startups may not yet be ready for full AI+QA Validation. For them, AI+QA Institute Membership may connect with INSELECT as a development and visibility route.
INSELECT can support visibility, positioning, institutional readiness, pilot preparation, ecosystem engagement, and market-facing presentation. When a startup becomes sufficiently mature, it may apply for the standard AI+QA Validation pathway.
AI+QA Institute Membership
→INSELECT development and visibility
→Possible later AI+QA Validation pathway
INSELECT is not accreditation, validation, certification, approval, recognition, endorsement, Candidate status, or a formal AAC quality assurance decision.
Responsible AI and Human-Led Review
Technology may assist. Accountability remains human.
AI+QA Validation 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 validation outcomes, replace expert judgment, assign final status, or create evidence.
Formal validation decisions remain with AAC’s relevant body.
Human-led review
AI may support the process.Experts exercise judgment.AAC owns the decision.
Fees and review stages
A staged review structure
Fees correspond to distinct review stages and do not create or guarantee a formal AAC outcome.
Preliminary stageCandidate for AI+QA Validation Review
Launch fee until Sept 15, 2026USD 3,000
Standard fee after Sept 15, 2026USD 6,000
Founding Member future feeUSD 3,600
Typical timing: up to 4 weeks after complete submission, scope confirmation, and payment where applicable.
Formal reviewAI+QA Validation Review
Launch fee until Sept 15, 2026USD 5,000
Standard fee after Sept 15, 2026USD 10,000
Founding Member future feeUSD 6,000
Typical timing: up to 10 weeks after complete submission, scope confirmation, and payment where applicable.
Ongoing stageAnnual AI+QA Validation Monitoring
Launch fee until Sept 15, 2026USD 900
Standard fee after Sept 15, 2026USD 1,500
Founding Member future feeUSD 900
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, validation, or any formal AAC outcome. Indicative timing starts after scope confirmation, complete documentation, and payment where applicable.
Application and next step
Begin with a defined validation inquiry
AAC begins by understanding what the provider wants reviewed and whether the proposed platform, solution, system, service, or provider scope is suitable for the validation pathway.
Submit validation inquiry
Briefly describe the platform, solution, system, service, or provider.
Confirm scope
AAC clarifies the precise subject and boundaries of review.
Join AI+QA Institute
Membership is normally the ecosystem entry point.
Submit initial materials
Provide the required documentation, evidence, and explanations.
Candidate review
AAC considers readiness for Candidate status.
Full Validation Review
Eligible cases may proceed to the formal review.
Decision and public wording
AAC confirms the outcome and any approved wording.
Why pursue validation now
Move from AI claims to QA-ready explanations
AI adoption in higher education is accelerating. Universities and quality assurance stakeholders increasingly need to understand not only what a tool does, but how it is governed, evidenced, monitored, and aligned with institutional responsibility.
AI+QA Validation helps providers move from generic AI claims to QA-ready explanations.
- Clarify their value proposition
- Strengthen trust with institutions
- Prepare for institutional review questions
- Demonstrate responsible AI governance
- Improve evidence and documentation
- Avoid overstated or misleading claims
- Speak the language of higher education quality assurance
Your next step
Prepare your AI-enabled solution for institutional trust.
AI+QA Validation helps education-facing platforms, solutions, systems, and service providers demonstrate responsible AI use, quality assurance awareness, evidence, governance, and readiness for higher education contexts.