The Academic Pace Problem

Why University Processes Struggle in the AI Era

The deliberate pace of traditional academic governance, once a hallmark of thoughtful decision-making in higher education, now seems increasingly misaligned with our rapidly changing world. As sophisticated AI systems transform education at an unprecedented rate, universities face mounting pressure to reimagine their core academic processes while maintaining their commitment to quality and rigor.

In this post, I want to explore three critical academic processes that are struggling to keep pace with AI-driven change: curriculum approval, peer review, and institutional decision-making. Conceived for a time of gradual knowledge evolution, the basic principles governing higher education are now being tested by the accelerating pace of technological progress.

The Curriculum Approval Conundrum

The traditional curriculum approval process—with its countless committee reviews, departmental votes, and sometimes accreditor consultations—creates a structural challenge in responding to rapid technological change. This process typically stretches over months or even years, during which entirely new technological capabilities might emerge and reshape a field before students ever experience the revised curriculum.

A UK university example from a recent study illustrates this problem clearly: approving a single degree program took about 18 months from proposal to final approval. In practical terms, this means a data science program designed today might be teaching outdated AI techniques by the time it launches, simply because the curriculum approval process couldn’t keep pace with technological advancement.

Many institutions compound this problem with infrequent curriculum committee meetings and sequential layers of approval. While these processes aim to ensure thoroughness and quality control, they appear increasingly misaligned with the pace of AI-driven innovation. The result is a growing gap between what universities teach and what graduates need to know when entering the workforce.

The Peer Review Predicament

The scholarly peer review system—another cornerstone of academic quality—also faces mounting challenges in the AI era. Traditional peer review timeframes span several months from submission to publication, a delay that becomes increasingly problematic as AI speeds up the pace of research and discovery.

According to a 2015 research publication, the average peer review cycle in some scientific fields takes about 14 weeks, though many researchers believe it should take about half that time. This creates a situation where research findings may be outdated by the time they reach the broader academic community, particularly in fast-moving fields related to artificial intelligence.

The system has been described as “under stress,” with reviewer fatigue and lengthy backlogs now compounded by the need to evaluate AI-assisted research and AI-generated content. As research methodologies and findings evolve more rapidly, the traditional review process struggles to keep pace, potentially slowing scientific progress when timely insights are more valuable than ever.

Institutional Decision-Making: Organized Anarchy

Perhaps the most fundamental challenge lies in institutional decision-making processes, where strategic decisions traditionally pass through layers of consultation and governance boards. This approach is especially problematic when dealing with the rapid changes brought about by AI, which can affect teaching, research, and administration in a matter of weeks or months.

Many universities still operate as what some scholars have called “organized anarchies,” with diffuse authority and lengthy deliberation processes. Higher education’s shared governance model, involving faculty, administrators, and sometimes students, struggles to accommodate the urgent action needed to address AI-driven transformation.

The bureaucratic management style common in academia stands in stark contrast to more agile organizations, highlighting how routine decisions can impede rapid response to technological change. When a new AI tool emerges that could transform teaching or research methods, universities often lack the governance structures to evaluate and implement it quickly, potentially missing valuable opportunities for innovation.

The Growing Misalignment

These three examples illustrate a growing misalignment between traditional academic processes and the pace of technological change. This gap creates several concerning outcomes:

1. Universities struggle to keep curricula relevant, potentially graduating students with skills that are already outdated.

2. Scholarly knowledge dissemination slows, reducing the impact of important research and delaying scientific progress.

3. Institutions become reactive rather than proactive in addressing technological change, often implementing solutions long after they would have been most beneficial.

4. Faculty and administrators experience increasing frustration as they attempt to navigate governance structures not designed for rapid adaptation.

The tension between maintaining academic rigor and responding to technological advancement creates stress throughout the system, affecting everyone from executive leadership to faculty, staff and students.

The Challenge Ahead

The central challenge for higher education isn’t whether to change these processes, but how to do so while preserving academic values. Universities must become more responsive without sacrificing the thoughtful deliberation and quality assurance that have long defined higher education.

This challenge is heightened by several factors unique to the academic environment. Faculty already face substantial workloads, and the demands of continuous development work are compounded by the need to stay current with rapidly evolving AI technologies. Quality assurance processes are deeply embedded in institutional cultures and are often linked to accreditation requirements. And the complex relationships between stakeholders make governing structures difficult to change.

As AI continues to transform education, research, and administration, universities face a defining moment. Those that preserve their core values while becoming more adaptive will be better positioned to fulfill their educational mission in a rapidly changing world. Those that cannot evolve risk becoming increasingly disconnected from the needs of students, employers, and society.

The question isn’t whether academic processes will change—it’s whether that change will be deliberate and thoughtful or forced by external pressures. As we navigate this complex landscape, educators and administrators must engage in honest conversations about how to honor academic traditions while embracing the realities of an AI-accelerated world.