Accreditation Wasn’t Built for Algorithms — But Universities Are Deploying Them Anyway

Accreditation in higher education was built to evaluate institutions, not algorithms. Its standards presume human judgment exercised through faculty governance, curricular deliberation and documented academic process. Artificial intelligence disrupts that presumption in subtle but consequential ways.

As universities deploy AI to influence curriculum design, assessment, advising and progression, accreditation frameworks are being stretched beyond their design limits. The problem is not that AI is incompatible with quality assurance. The problem is that accreditation was never designed to evaluate automated judgment at scale.

This gap matters because accreditation does more than certify quality. It legitimizes federal financial aid eligibility, institutional credibility and public trust.

Accreditation Presumes Human Judgment
The Documentation Problem
Accreditation Reviews Are Retrospective — AI Is Not
Governance, Not Innovation, Is the Missing Layer
The Line Accreditors Will Eventually Draw

Accreditation Presumes Human Judgment

Regional accreditors consistently emphasize faculty responsibility for curriculum, assessment and academic standards. Whether articulated through expectations of faculty oversight, academic control or continuous improvement, the underlying assumption is human deliberation and accountability.

AI systems complicate this assumption when they:

Generate assessments,
Infer mastery or risk
Recommend remediation
Flag academic concerns
Influence progression pathways

At that point, academic judgment is no longer exclusively human. It is delegated, partially or fully, to algorithmic processes.

Current accreditation standards do not explicitly address this delegation.

The Documentation Problem

Accreditation depends on documentation: syllabi, assessment reports, curriculum maps, learning outcomes and evidence of review. AI systems generate parallel documentation — prompts, model outputs, inference logs and analytics — that often sit outside formal academic records.

When AI influences academic decisions, accreditors may reasonably ask:

Where is the documentation of that decision?
Who approved the criteria?
How was bias evaluated?
How can the decision be reviewed or appealed?

If the answers reside primarily in vendor dashboards or proprietary model logs rather than institutional records, accreditation expectations are no longer being met.

Recent research (found here and here) on learning analytics governance confirms that institutions often struggle to surface, interpret and document AI-influenced decisions during external review processes, precisely because those decisions are embedded in systems rather than academic workflows.

Accreditation Reviews Are Retrospective — AI Is Not

Accreditation evaluates decisions after the fact. AI systems operate in real time.

This temporal mismatch creates risk. AI-generated judgments may shape learning and assessment long before institutions formally examine their impact. When accreditors later review outcomes, institutions may struggle to reconstruct how decisions were made, particularly when AI systems are adaptive, opaque or vendor-controlled.

Contemporary scholarship (found here and here) on AI governance in education highlights this problem directly: adaptive systems evolve continuously, while institutional review mechanisms remain episodic and retrospective.

Accreditation was designed for reviewable processes, not continuously evolving algorithmic systems.

Governance, Not Innovation, Is the Missing Layer

The accreditation risk is not AI adoption itself; it is un-governed AI adoption.

Accreditors are unlikely to reject AI outright. But they will expect institutions to demonstrate:

Faculty oversight of AI-assisted academic functions
Transparency of criteria and models
Documented review of AI-generated decisions
Alignment with institutional mission and learning outcomes

Empirical studies (found here) of predictive analytics in higher education show that when governance frameworks lag behind AI deployment, institutions inadvertently shift academic authority away from faculty and toward opaque technical systems.

Absent governance, AI becomes a black box inside a framework built for transparency.