How Technology Can Transform the Higher Education Accreditation Process and Drive Continuous Improvement at Colleges and Universities | American Enterprise Institute

Key Points

• The higher education accreditation system relies on infrequent, resource-intensive reviews that occur every five to 10 years, often creating lags in identifying and addressing quality concerns at colleges and universities.
• Recent federal policy proposals—including the April 2025 executive order “Reforming Accreditation to Strengthen Higher Education”—create an opportunity to modernize accreditation through technology-enabled continuous monitoring, which could improve transparency for consumers while reducing the regulatory burden on institutions.
• Leading sectors including health care and financial services have demonstrated how real-time monitoring can enhance quality assurance and accountability—a proven approach that the higher education sector could consider.
• Technology-enabled platforms that support continuous accreditation review processes, carefully designed pilot programs, and uniform data standards could provide students and families with more reliable, up-to-date information about institutional quality while giving policymakers better tools to ensure taxpayer dollars support educational outcomes.

Introduction

While the higher education accreditation process has evolved through competition among agencies and federal policy shifts, there are opportunities to refine agency approaches and implement technology to bring the process into the modern age. Other sectors have embraced technological transformation, yet accreditation remains reliant primarily on a model of periodic, point-in-time evaluations that consume institutional resources without driving just-in-time improvement and responsiveness.

As public confidence in higher education erodes and congressional scrutiny intensifies, accreditation reform is one area where policymakers and institutional leaders could find bipartisan agreement to transform the procedural formality into a more modernized, substantive engine for continuous improvement.¹ The National Advisory Committee on Institutional Quality and Integrity (NACIQI)—the federal quasi-governmental entity that reviews and recognizes accreditors—may also benefit from technological advancements, reducing paperwork burdens and expediting reviews.²

This report examines how integrating data analytics, artificial intelligence, and digital platforms could revolutionize accreditation, creating a more dynamic, evidence-based approach to quality assurance in higher education.

The Problem: Outdated Accreditation Processes

The Current Policy Context

The April 2025 executive order “Reforming Accreditation to Strengthen Higher Education” identifies several shortcomings in the accreditation system, creating a timely opportunity to reimagine accreditation.³

A recent example highlights the overall system’s dysfunction. In 2022, the Southern Association of Colleges and Schools Commission on Colleges—which oversees more than 750 institutions serving nearly five million learners—failed to submit basic required documentation to NACIQI during its review process, highlighting the challenges inherent in the documentation-heavy approach.⁴ The documentation requirements that the US Department of Education (ED) imposes on agencies can reach 800,000 pages or more per review cycle. Even if reduced to 400,000 pages, the documentation would require five years of full-time work for a single reviewer to complete if they were reading 40 pages per hour—making comprehensive review literally impossible for staff to accomplish with current resources and methods.⁵

The Current Process of Decennial Accreditation

The traditional accreditation model follows a cycle with comprehensive reviews every five to 10 years, creating the potential for inefficiencies and quality-oversight gaps. While institutions complete their review and even file annually, problems can develop and persist before the next formal assessment.

Colleges and universities often dedicate years to preparing self-studies and documentation, diverting attention and funding from core educational missions. Faculty withdraw from teaching and research obligations to craft narrative reports. Administrators hire consultants to coach them through peer evaluators’ often opaque expectations. Despite institutions’ good-faith efforts to meet standards, the system has evolved into what some organizational theorists may recognize as a classic case of Goodhart’s law—when a measure becomes a target, it ceases to be a good measure.

The metrics themselves often lack an empirical foundation, becoming self-fulfilling prophecies as institutions aim to meet them regardless of their validity or connection to meaningful student outcomes. Some regional accreditors employ qualitative rubrics with aspirational language about “appropriate resources” and “sufficient support” without executing these concepts in measurable ways. Examining accreditation across stakeholders—including accreditors, institutions, policymakers, and consumers—reveals widespread agreement that accreditation systems need improvement.⁶

Learning from Other Sectors

While collecting comprehensive data from institutions—and accreditors, in the case of NACIQI—may seem thorough, it often creates more noise than signal. Actors in other industries that face similar challenges, such as health care providers monitoring patient safety or financial institutions ensuring regulatory compliance, have developed targeted, technology-driven oversight that actually improves outcomes. As Beth Akers writes in her recent report on accreditation reform, these models suggest more focused approaches that could work equally well in higher education accreditation.⁷

Health Care’s Quality Revolution

The health care industry offers valuable lessons for accreditation, having shifted from periodic quality reviews to continuous quality-monitoring systems that provide real-time insights into patient care outcomes. Hospital accreditation now increasingly incorporates ongoing data analysis rather than relying on scheduled site visits, leveraging data analytics to track key performance indicators and allowing the early identification of quality concerns.

The parallels between health care’s Joint Commission accreditation and higher education’s historically regional accreditation model are striking.⁸ Both initially relied on episodic site visits by peer evaluators employing subjective standards. Yet health care has dramatically outpaced higher education in its evolution toward data-driven continuous improvement, developing systems that, despite initial documentation increases, ultimately create more efficient and meaningful oversight processes. For example, the Johns Hopkins Hospital’s dashboard for monitoring patient activity uses 10 key performance indicators, enabling hospital administrators to identify bottlenecks immediately rather than discovering problems months later.⁹

Financial Services’ Continuous Compliance

Financial institutions also operate under intensive regulatory scrutiny and have developed sophisticated compliance monitoring systems. Modern financial compliance platforms allow continuous monitoring of transactions with real-time alerts for potential issues, shifting from a periodic audit mindset to one of ongoing compliance management. These platforms centralize documentation, automate routine aspects of compliance reporting, and facilitate ongoing communication between financial institutions and regulators. For example, JPMorgan Chase has implemented AI-enhanced anti-money-laundering systems that monitor transactions in real time, achieving a 95 percent reduction in false positive alerts while maintaining high detection accuracy.¹⁰ Similarly, Bank of America has developed automated regulatory monitoring systems that process millions of transactions per minute and provide real-time alerts for potential compliance issues, reducing response times from up to two months to immediate notification when corrective action is needed.¹¹

The Opportunity: Technology-Enabled Accreditation

From Periodic Reviews to Continuous Monitoring

Technology can transform accreditation from a resource-intensive documentation exercise to a system of continuous quality monitoring and improvement.

Picture this scenario: An accreditor’s dashboard flags that a small private college is showing clear signs of financial trouble, with a declining cash flow over three consecutive quarters. Simultaneously, the student-to-faculty ratio climbs as departing faculty aren’t being replaced, and student retention rates are starting to drop. In today’s accreditation system, these warning signs might fester for years before the next review. In a continuous monitoring environment, however, the accreditor could initiate a more focused review immediately, potentially averting institutional crisis or even failure.¹² One can imagine that a continuous improvement process, providing just-in-time signaling, could have prevented Union Institute & University’s closure in 2024.¹³

The shift to continuous monitoring may also distribute the accreditation workload more evenly over time, reducing the resource burden associated with preparing for major periodic reviews. Instead of dedicating months or years to comprehensive self-studies, institutions could maintain ongoing documentation and communication with accreditors, making the process more manageable and less disruptive to core educational functions.

Enhancing Assessment Through Artificial Intelligence

Artificial intelligence tools can analyze vast amounts of data to identify patterns, correlations, and anomalies that might not be apparent through conventional analysis. AI systems could analyze student outcomes data to identify institutional practices that align with stronger graduation outcomes in the labor market, helping institutions focus their improvement efforts on high-impact areas.

AI could also help predict potential problems before they manifest by analyzing historical data patterns from numerous peer institutions. These systems could flag early warning signs of financial instability, enrollment challenges, or quality issues. Beyond data analysis, AI could transform how accreditors review narrative materials and qualitative evidence. Natural language processing could help identify patterns across institutional documentation, faculty feedback, and student comments, providing deeper insights into campus culture and practices.

A New Focus on Economic Outcomes

The April 2025 executive order emphasizes the importance of student outcomes in accreditation. Advanced analytics platforms could help accreditors develop metrics for value-added earnings to measure wage gains that institutions generate for students relative to attendance costs, creating balanced incentives to lower costs, improve graduation rates, and raise earning potential.

Rather than relying on simple outcomes measures, technology-enabled analytics could consider student demographics, regional economic conditions, and similar factors to produce more nuanced assessments. These approaches could avoid penalizing institutions that serve underrepresented student populations while still maintaining high expectations for student success.

By integrating labor market data systems, accreditation platforms could also better align educational programs and workforce needs. Accounting for institutional mission, this practice could allow accreditors to consider whether institutions are offering programs that lead to viable career paths with sustainable wages in their respective regions.

Platforms for Evidence and Communication—for Institutions and NACIQI

Technology platforms could address the entire accreditation ecosystem—from ED’s oversight of agencies to institutions managing relationships with multiple specialized and programmatic accreditors—creating system-wide transparency rather than isolated improvements.

Rather than documentation management platforms that organize institutional submissions, imagine data integration systems that automatically pull operational metrics—such as student outcomes, financial indicators, and faculty qualifications—directly from source systems into real-time monitoring dashboards. Instead of AI tools that help institutions present their best case, envision analytical engines that flag emerging issues before they become compliance problems.

This shift from episodic storytelling to continuous monitoring could change accreditation’s entire dynamic. Current platforms ask, “How can we help you make your case?” and the systems proposed here may ask, “What do the data actually show?” When accreditors can see real-time trends in student success rates, faculty turnover, or financial stability, the conversation moves from institutional advocacy to collaborative problem-solving.

Such platforms would still facilitate communication between institutions and accreditors, but the dialogue would center on data-driven insights rather than carefully crafted reports. Accreditors could then offer targeted support based on early warning indicators rather than responding to problems that accrue over time in self-studies. Peer reviewers’ expertise could focus on interpretation and improvement rather than verification and validation.

Finally, digital platforms could streamline ED’s recognition of agencies and the agencies’ own accreditation processes by centralizing documentation, document collection, and communication between institutions and the various accreditors to which they are accountable.

Implementation Considerations

Administrative Burden

A primary implementation concern is the potential administrative burden on institutions as they transition to a continuous monitoring system. Colleges and universities may face initial costs in staff training, technology infrastructure, and data collection processes. Smaller institutions and minority-serving institutions, in particular, may lack the resources to implement robust data management systems or dedicate personnel to ongoing compliance monitoring. Additionally, there are valid concerns that increased data requirements could paradoxically create more paperwork rather than streamlining the accreditation process. However, these upfront investments must be weighed against the long-term benefits of replacing periodic, intensive documentation cycles with more manageable, ongoing reporting that could provide earlier warnings and possibly targeted interventions.

Learning from Cross-Sector Applications

The health care industry’s use of quality-monitoring systems demonstrates the importance of focusing on a set of meaningful metrics, rather than attempting to measure everything.¹⁴ The financial services sector could also offer lessons about change management, demonstrating that successful implementation requires not just technological tools but also cultural change and professional development.

Both sectors highlight the importance of maintaining human judgment alongside technological tools. While technology can enhance data collection and analysis, trained professionals must still interpret findings and make judgments about quality and improvement. The historical peer-review process could be reimagined to ensure that even with the adoption of technology, the quality-assurance process does not become overly automated.

Advanced Monitoring and Predictive Analytics

To truly modernize accreditation, technology implementation could adapt advanced risk assessment practices used in other sectors to the higher education context. For example, in the financial services industry, financial institutions employ auditing protocols that go beyond scheduled assessments and include a random sampling of data and outcomes, which provide a more accurate picture of ongoing operations.

Higher education could adopt similar approaches, supplementing formal reviews with greater real-time monitoring of risk factors and occasional verification of self-reported data. This could better incentivize institutions to maintain quality standards rather than focusing compliance efforts on scheduled reviews.

Predictive modeling tools, similar to those in financial risk management, could identify institutions at risk of financial instability, enrollment challenges, or quality issues before the problems become severe. This would allow accreditors to direct resources and attention to institutions that need the most support, rather than treating all institutions with the same level of evaluation.

Data Standardization Challenges

A significant challenge in implementing technology-enhanced accreditation is the lack of standardized data across institutions. Addressing this would require developing common data definitions and reporting methods that allow comparison while respecting institutional diversity.

Rather than creating entirely new data requirements, technology could help accreditors integrate existing data collection efforts, using systems such as the Integrated Postsecondary Education Data System and state longitudinal data systems, where they exist.

Balancing Standardization and Institutional Diversity

A key strength of the US higher education system is its diversity, and any technology-enhanced accreditation system must preserve this. Technology platforms should be flexible to accommodate different institutional types, missions, and student populations while still enabling quality assurance.

This could be achieved through frameworks that allow institutions to demonstrate quality in ways aligned with their specific mission—or even by their designation in the Carnegie Classification of Institutions of Higher Education—while still addressing core characteristics that apply to all institutions.

Accreditation’s peer-review element could be preserved to the extent that it brings valuable professional judgment and context to the process. Technology can enhance, rather than replace, peer review by providing reviewers with data and analytics to inform their visits and evaluations.

Recommendations

Develop a Comprehensive Technology Framework

ED could convene a task force of accreditors, institutions, technology experts, and stakeholders to lay out principles for how to integrate technology into accreditation processes. A task force could establish guiding principles, identify core technology requirements, and outline implementation strategies. In addition, the task force could address the technical aspects of technology integration and any necessary policy changes. The task force should focus on enhancing quality improvement, not simply automating processes.

A similar review should be conducted for ED’s own processes that NACIQI uses to regularly evaluate and recognize accrediting agencies, which are charged with gatekeeping over $100 billion of federal financial aid.

Launch Pilot Programs for Continuous Monitoring

ED could also fund pilot programs with 25–40 institutions across various Carnegie classifications, sectors, and regions to test approaches to continuous data monitoring and analysis. The initial pilots would focus on a limited set of key indicators. These pilots would allow various institutions to experiment with different technology approaches and assess their effectiveness. Participating institutions could partner with their regional, specialized, or programmatic accreditors to identify existing standards appropriate for continuous monitoring.

The pilot programs should incorporate evaluation protocols that measure not just whether continuous monitoring identifies quality issues but also the potential cascading effects on institutional culture and decision-making.

Establish Data Standards and Integration Mechanisms

A coalition of accreditors, institutions, and technology providers could develop clear data standards and secure platforms to enable the aggregation and analysis of accreditation data across institutions. These standards could facilitate meaningful comparison while reducing redundant reporting requirements. The effort could include developing data governance frameworks that address privacy, security, and appropriate data use. Clear guidelines could build trust in the system and ensure data are used in ways that support quality improvement rather than punitive measures.

Research Benchmark Validity

Accreditors, in partnership with researchers and institutions, could conduct research to establish more scientifically valid and mission-appropriate performance benchmarks. This research would help move accreditation from arbitrary standards to evidence-based frameworks that more closely reflect quality.

Research efforts could analyze historical data to identify correlations between various benchmarks and actual student outcomes, which would allow accreditors to develop more meaningful standards. The research should be transparent to build credibility and support for the benchmarks. Findings could be published in an open-source format to inform accreditation processes across the higher education sector.

Enhance Economic Outcomes Metrics

Building on the executive order’s emphasis on student outcomes, stakeholders could develop tools to measure and track graduates’ wage gains and academic programs’ return on investment. These tools could help accreditors focus on what matters most to students and taxpayers.

Developing these tools should involve collaboration among economists, data scientists, institutional researchers, and policy experts to ensure the tools are methodologically sound and useful for accreditation purposes. Such metrics should consider factors including student demographics, regional economic conditions, and institutional mission to provide fair assessments of economic outcomes.

Foster Innovation While Maintaining Accountability

As accreditation evolves to incorporate more technological tools, stakeholders should ensure the process retains a balance between institutional innovation and accountability for student outcomes. Technology platforms should be designed to accommodate and assess innovative approaches rather than enforce standardization.

Accreditors could develop guidelines for evaluating innovative programs and practices that may not fit into traditional assessment frameworks. ED could consider creating a regulatory sandbox that allows controlled experimentation with new approaches that have appropriate safeguards to protect learners and public resources.

Ensure Technology Neutrality

All stakeholders should commit to ensuring that technological innovation in accreditation remains politically neutral and focuses on educational quality and student outcomes. Technology platforms should be designed to support evidence-based assessment rather than advancing ideological perspectives.

This would require transparent development processes, diverse stakeholder involvement, and an ongoing evaluation of technologies and tools to identify potential biases. Regularly reviewing and refining technology-enhanced accreditation frameworks could help ensure they remain focused on the core purpose of promoting educational quality.

Conclusion

With modern technology, higher education accreditation could transform itself from a periodic compliance exercise into a continuous improvement process. By leveraging data analytics, artificial intelligence, and integrated data platforms, accreditors could improve quality assurance in higher education more effectively, efficiently, and quickly.

The path forward isn’t only about adopting new technologies—it’s about reimagining the relationship between quality assurance and institutional improvement. The experiences in health care and financial services demonstrate that when quality monitoring becomes continuous rather than episodic, it changes organizational behavior: Problems are addressed before they become crises, attention shifts from retrospective documentation to prospective improvement, and resources flow to initiatives with demonstrated impact.

Technologically transforming accreditation won’t be easy. It will require substantial investment in new systems, personnel retraining, and cultural changes. But the alternative—continuing with a system that consumes resources while providing limited quality assurance—is increasingly challenging to sustain in an era of heightened accountability and scrutiny.

As higher education faces eroding public confidence and questions about its value proposition, technology-enhanced accreditation offers a path toward quality assurance that serves students first. The time is now for bold innovation to ensure educational quality in our nation’s colleges and universities.

About the Author

Alison Griffin served as a policy adviser to former Chairman John Boehner and the US House Committee on Education and the Workforce in 2001 and from 2003 to 2006. Ms. Griffin is the former board chair and a current trustee at Colorado Mesa University, having been appointed by Colorado Governor Jared Polis in 2019.