Technological shifts also represent a fundamental reimagination of quality assurance. When education technology platforms can seamlessly connect institutions and automate complex processes, they demonstrate the potential for similar transformation in accreditation oversight.
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Higher education’s accreditation system is stuck in the past. While health care and financial services have embraced real-time monitoring and data-driven quality assurance, colleges and universities still rely on a model of periodic evaluations, creating dangerous lag times in identifying quality concerns and consuming enormous institutional resources in the process.
But there’s a better way forward. In my recent policy brief for the American Enterprise Institute (AEI), I outlined how data analytics, artificial intelligence, and digital platforms could revolutionize accreditation into a dynamic, evidence-based system focused on continuous improvement. The timing couldn’t be better: President Trump’s April 2025 Executive Order, “Reforming Accreditation to Strengthen Higher Education,” creates new momentum for this type of transformation.
The Current System’s Dysfunction
When the Southern Association of Colleges and Schools Commission on Colleges (SACSCOC), which oversees more than 750 institutions serving nearly 5 million students, failed to submit basic required documentation during its own 2022 review process, it highlighted the fundamental challenges of the current documentation-heavy approach.
In SACSCOC’s case, documentation requirements from the U.S. Department of Education reached nearly 800,000 pages. Even if reduced by half, reading at 40 pages per hour would require five years of full-time work for a single reviewer, making comprehensive review practically impossible with current resources and methods.
Meanwhile, colleges and universities dedicate years preparing self-studies and documentation, diverting attention and funding from their core educational missions. Faculty withdraw from teaching and research to craft narrative reports, and administrators hire consultants to navigate the often opaque expectations of peer evaluators.
Stig Leschly, founder and president of the Postsecondary Commission, a new accreditor of outcomes-focused and innovative higher education providers, points to the fundamental problem with this approach: “Historically, accreditors have relied heavily on expensive, infrequent visits by evaluation teams and on self-studies produced intermittently by institutions themselves as their primary mechanisms for gathering information and monitoring risk.”
Periodic evaluations, he argues, can fail to detect “non-obvious, material risks or problems” at an institution—a difficult undertaking when reviews happen, in some cases, once a decade.
Learning from Other Industries
As I detailed in my AEI brief, the healthcare industry provides compelling lessons. Hospital accreditation has evolved from infrequent site visits to ongoing data analysis, leveraging analytics of patient interactions to assess performance and identify quality concerns. Johns Hopkins Hospital’s dashboard, for example, monitors patient activity using 10 key performance indicators, enabling administrators to spot and address bottlenecks to improve patient care immediately, not months (or years) down the line.
The financial services industry provides another blueprint. Modern compliance platforms allow continuous monitoring of transactions with real-time alerts for potential issues, shifting from periodic audits to ongoing compliance management. These systems centralize documentation, automate routine reporting, and facilitate ongoing communication between institutions and regulators.
The Technology-Enabled Vision
Some accreditors are already proving this transformation is possible. The WASC Senior College and University Commission (WSCUC), which accredits more than 200 U.S. and international colleges and universities, launched a new return on investment metric in its public-facing Key Indicators Dashboard, providing institutions and stakeholders with tools to analyze student outcomes and identify meaningful peer comparisons using the most recent available data. This represents exactly the direction accreditation must move to better serve students, families, and taxpayers.
“We come from the perspective that outcomes matter,” says Dr. Maria Toyoda, president and CEO of WSCUC. The dashboard was designed around the recognition that “one of the biggest considerations that students and families are going to have is how much higher education is going to cost, how much debt their student may acquire, and how much, at the end of the day, is their degree going to pay off in the labor market.”
The technology enables what WSCUC calls “better data, better conversations,” allowing accreditors to focus peer reviews on data-driven insights rather than comprehensive fishing expeditions. “Good data is central to answering questions for institutions and consumers,” she explains. When institutions make aspirational claims to learners with good data, “you can show them the benchmarks compared to their peers and say, ‘You think you can do that? Tell us how you’re going to get there.’”
The technological foundation for this transformation already exists. Dr. Mallory Dwinal-Palisch, who built Craft’s workforce education data platform before its acquisition by Western Governors University, created a system that automates data collection and reporting across multiple funding streams, including Perkins V for CTE and adult education, Title IV, and the Workforce Innovation and Opportunity Act. The Craft platform had to navigate “hundreds of thousands of pages, if not millions of pages” of overlapping regulations in which “no one can be an expert,” according to Dwinal-Palisch
Craft now operates as a nonprofit public data utility, available at no cost to any accredited institution.
The biggest challenge in the platform’s development wasn’t technical—it was conceptual. This is a common obstacle, and one that is deeply rooted in a problem Jen Pahlka calls “policy vomit.” The immense accumulation of regulatory requirements creates an environment where institutions can only hope they haven’t missed some obscure requirement buried in the requested documentation.
“There’s this ‘kitchen sink fallacy’ that if I’m sending you all of this stuff, that therefore this must be a rigorous oversight process,” notes Dwinal-Palisch. “That’s not how it works. What happens is you overwhelm people, and there’s no possible way for a human to make sense of everything in front of them.”
Technological shifts also represent a fundamental reimagination of quality assurance. When education technology platforms can seamlessly connect institutions and automate complex processes, they demonstrate the potential for similar transformation in accreditation oversight.
“Technology, if used well, should be able to lower the monitoring costs of accreditors, without increasing the expense and effort that institutions have to go through to comply,” says Leschly. “One particularly promising frontier is live and ongoing access by accreditors to the primary data that circulate inside an institution—on enrollment, outcomes, finances, and human resources. This access will allow the accreditors’ expert staff to track risk intelligently and in more efficient ways.”
This technological foundation already exists. Workforce education platforms have early proof points that they can automate data collection and reporting while maintaining educational integrity. The same principles that enable real-time course matching and student progress tracking could revolutionize how accreditors monitor institutional performance, shifting conversations from retrospective documentation to proactive problem-solving.
Artificial Intelligence as a Game-Changer
AI tools could analyze vast amounts of data to identify patterns and anomalies that would be invisible in a conventional analysis. These systems could examine student outcome data to identify institutional practices aligned with stronger graduation outcomes, helping institutions focus improvement efforts on high-impact areas.
Beyond data analysis, AI could transform how accreditors review qualitative evidence. Natural language processing could identify patterns across institutional documentation, faculty feedback, and student comments, providing deeper insights into campus culture and practices.
The Executive Order’s emphasis on student outcomes could create opportunities for AI-driven “value-added earnings” metrics that measure wage gains institutions generate for students relative to attendance costs. Rather than simple outcomes measures, technology-enabled analytics could account for student demographics, regional economic conditions, and other factors to produce more nuanced, fair assessments.
“The Postsecondary Commission invests heavily in models that measure economic outcomes at institutions, in particular, the value-added earnings outcomes of entrants,” says Leschly. “These models are data intensive and often rely on analysis of large sets of wage and education records. They are only possible in many cases because of the various hardware and software technologies that enable big data storage and analysis.”
Implementation Challenges and Solutions
The transition won’t be without challenges. Smaller institutions and Minority-Serving Institutions (MSIs) may lack resources for robust data management systems. There are legitimate concerns about whether increased data requirements could paradoxically create more paperwork rather than streamlining processes.
The key is ensuring that technology creates “comprehensible input” rather than additional burden, says Dwinal-Palisch. Her platform succeeded because it eliminated redundant reporting; instead of institutions filing separate compliance reports for each funding stream, they could input data once and automatically generate all required documentation.
“There is a false binary in the education world that the more data there is, it must assume there is more rigor, and therefore greater quality,” argues Dwinal-Palisch. “We assume in accreditation that making things hard and complex and having lots of layers equals better oversight. There’s actually no evidence of that.”
The solution lies in moving toward what she calls “apples to apples” data collection that creates reliable, valid information for human evaluators—precisely what accreditation should provide but currently struggles to deliver at scale.
However, Toyoda acknowledges the challenges that come with implementing continuous monitoring at scale. “A lot of the income and debt data has a lag time. The dream would be that we would be able to dashboard these data elements to be able to actually see in real time what is going on with a particular program at an institution of higher education,” she says. To achieve this dream, institutions and accreditors would need better access to current federal data—an uphill battle that, if won, could radically transform the way institutional quality is assessed.
The shift toward data-driven oversight has already streamlined WSCUC’s processes. Instead of voluminous documentation, institutional presentations now average about 60 pages and have more focused appendices. Most importantly, the approach has streamlined peer review from broad institutional assessments to targeted inquiries. “The lines of inquiry from peer review can come out of that data, so reviewers have a focused set of questions that ultimately helps to keep the process more efficient and targeted to areas of potential concern or of innovation,” says Toyoda.
The Stakes Couldn’t Be Higher
As public confidence in higher education erodes and congressional scrutiny intensifies, accreditation reform is a rare area of bipartisan agreement. The current system’s consumption of resources and limited, just-in-time quality assurance is increasingly unsustainable in an era of heightened accountability.
Technology-enhanced accreditation isn’t just about efficiency; rather, it’s about reimagining the relationship between quality assurance and institutional improvement. The success of platforms like WSCUC’s Key Indicators Dashboard proves that technology can enhance transparency while reducing administrative burden. When quality monitoring becomes continuous rather than intermittent, it fundamentally changes organizational behavior. Problems get addressed before they become crises, attention shifts from retrospective documentation to prospective improvement, and resources have the potential to flow to initiatives with demonstrated impact.
The health care and financial services industries have proven that technology-driven oversight can improve outcomes while reducing regulatory burden. Higher education has the opportunity to follow their lead, creating an accreditation system that both serves students and supports institutional innovation and accountability.
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