Higher education is going through a structural shift that is happening faster than most institutions want to admit. Artificial intelligence (AI) is changing how professional work gets done, and the students showing up on campuses today no longer resemble the ones the college system was designed to serve.

For much of the last century, higher education was organized around a clear picture of how education fit into a student’s life. Students arrived from high school, enrolled full time, moved through general education into a major, and graduated into a profession. Institutions built themselves around that expectation. Academic departments defined programs. Semesters set the pace. Advising helped students navigate a structure built for a steady, uninterrupted path.

Now, the population of traditional college-age students is declining in many regions as birth rates fall (Colorado Department of Education, 2025). The learners relying on higher education look very different from the ones the system was built to serve. Many are working adults returning to school to change careers or advance in current roles. Many attend part time while balancing employment and family. Most college students now work while enrolled.

Institutions have tried to respond by adding flexibility. Online courses, evening schedules, and expanded student services help, but these adjustments mostly sit on top of a structure built for another era. The result is a widening gap between how higher education is organized and how learners experience it. College students accumulate excess credits because pathways are hard to navigate. Credentials designed to take two years often stretch into three or more. Advising systems struggle to keep pace with demand. Higher education is still largely organized around the students it used to serve and the economic conditions that once supported that design.

The Work Learners Are Preparing for Is Changing

AI is not just another technology trend. It is changing professional work itself. In software development, AI systems can now generate working code, debug functions, and build usable prototypes from plain-language prompts (Udinmwen, 2025). In legal and administrative work, AI tools draft contracts, summarize case material, and produce first-pass research memos (Mulligan, 2025). In finance and analytics, they clean data, generate models, and explain patterns in natural language.

Human expertise does not suddenly become irrelevant, but the nature of what people do in professional roles is shifting. Work that once depended on a person carrying out each technical step increasingly depends on someone defining the goal, evaluating the output, correcting the direction, and deciding what happens next.

Project managers, analysts, marketers, accountants, technical writers, and entry-level developers increasingly work in environments where AI is part of the workflow. In some cases, it is already performing a meaningful share of the production work (Pendell, 2025). If education is still built around mastering the manual production of technical work, institutions may be preparing students for a version of those jobs that is outdated.

Colleges still need to help students learn technical skills. However, they also need to prepare them to work in environments where intelligent systems shape how those skills are used, and what readiness now means.

Higher Education Is Not Built to Respond Fast Enough

AI alone is not what makes this moment consequential. The deeper issue is that higher education is not built to respond at the speed workforce change now demands.

Most institutions still move through long cycles of review, approval, and revision before meaningful change reaches students. Curriculum changes move through committees. New credentials can take months or years to design and approve. Employer input often arrives through advisory structures that meet once or twice a year and speak in broad terms about trends that may already be shifting again by the time a program responds.

Learners accumulate excess credits because pathways are difficult to understand. Two-year credentials often take three or more years to complete (National Student Clearinghouse Research Center, 2019). Advising systems remain largely reactive. These are signs of an institutional design that puts too much of the burden on learners to navigate complexity on their own.

Adding a few AI courses, updating a handful of programs, or layering more flexibility onto an inherited model will not solve this. Institutions are trying to respond to a rapidly changing workforce with operating models built for a far more stable relationship between education and work.

Why Community Colleges Are Positioned to Lead

Community colleges do not sit outside these pressures. In many cases, they feel them more intensely, but they are also structurally closer to the realities that matter most. Their learners are more likely to attend part time, work while enrolled, stop out and return, and move between education and employment multiple times across their lives (Koller, 2025). Community colleges are already serving the kind of learner that a more fluid workforce will produce in larger numbers.

These institutions are also built closer to the labor market. Community colleges routinely operate in direct relationship with employers, workforce boards, economic development partners, and regional industries. They offer short-term credentials, customized training, applied programs, transfer pathways, and technical degrees. This mix, which colleges often treat as complexity, is, in this moment, a strategic advantage. But proximity is not readiness. Many community colleges remain organized around the same academic calendars, committee structures, and curriculum cycles that slow change across the rest of higher education. They may sit in the right place without being structured to move at the speed presently required.

Community colleges already carry the expectation of workforce relevance. The work now is to redesign institutions to meet that responsibility at a different scale and with far more intentionality.

Four Shifts Community Colleges Need to Make

Incremental adjustment will not be enough. This moment calls for real transformation. The colleges that matter most will not be the ones that add a few new programs or purchase a chatbot. They will be the ones willing to rethink how learning is designed, how employer input shapes education, how learners move through pathways, and how work-based experience becomes the organizing principle of the institution rather than an optional add-on. At minimum, community colleges will need to make four core shifts.

The first shift is toward precision learning. Most community colleges still ask learners to navigate a surprisingly complex institution with limited individualized guidance. Students choose programs, sequence courses, interpret requirements, and make career decisions inside systems that were not built to understand much about their individual circumstances. For learners balancing work, family, and financial pressure, those wrong turns are expensive (National Student Clearinghouse Research Center, 2025). A learner trying to find the shortest path into a healthcare role at 10:00 p.m. should not have to wait three weeks for a 15-minute appointment to get a usable answer.

Precision learning is not just better advising software. It is a wholesale institutional commitment to organizing around the learner's actual lived experience. That means using data and AI-enabled tools to deliver accurate, personalized guidance at scale. It means proactive outreach when a learner shows early signals of struggle, not reactive intervention after they have already stopped out. It means advisors shifting from answering routine questions to doing the high-value human work that systems cannot replace. Every part of how the institution guides learners, from first inquiry through completion, gets redesigned together, not patched one piece at a time.

The second shift is toward continuous cocreation with employers. Employer engagement in higher education is still too often organized around advisory boards that meet once or twice a year, validate what already exists, and disappear until the next cycle. That is not engagement. It is a formality. And it is not nearly enough for a rapidly changing labor market.

Cocreation means something fundamentally different. It means employers are not just consulted about what colleges are building. They are active participants in building it. They help define the skills and competencies that anchor credential design. They provide real-time feedback when hiring expectations shift. They open their organizations as learning environments and help validate what graduates can actually do. Colleges should know where their regional economy is moving, which sectors are growing, how role expectations are changing, and where graduates are finding traction or falling short (Cengage Group, 2025). That intelligence has to become part of how pathways are designed and refreshed continuously, not just how programs get reviewed after the fact.

The third shift is toward work-based learning as a transformational organizing principle. Community colleges have a long history of implementing work-based learning models. Internships, co-ops, clinical placements, and applied capstones already exist across the sector. But most institutions treat these as enhancements layered onto an academic model that still puts the classroom at the center. This is the part that must change.

The transformation is not adding more work-based components to existing programs. It is rebuilding programs around the logic that real production environments are primary sites of learning, not supplements to it. Courses and credentials get designed backward from what learners need to demonstrate in actual work contexts. Employer partners are embedded in curricula, not consulted after it is written. Faculty work alongside industry to keep learning current with how work is organized. Applied settings develop the judgment and adaptability that matter most as AI handles more of the procedural work (Love, 2025). For community colleges, this is not an unfamiliar direction. The shift is in treating it as the architecture, not the accessory.

The fourth shift is toward connected credential pathways. The assumption that education begins with entry, proceeds through a degree, and ends with graduation no longer reflects how many people will engage with learning across a lifetime. Credentials need to function less like endpoints and more like connected infrastructure.

Community colleges are already uniquely positioned here. They operate across credit and noncredit, short-term certificates and associate degrees, customized employer training and transfer pathways, all at the same institution. What they often lack is a clearly designed architecture that connects those pieces into coherent options for learners. A better model means short-term credentials that stack into certificates, certificates that apply toward degrees, prior learning that earns credit, and portable records of skill and experience that follow learners across jobs and institutions (Credential Engine, 2025). Someone enters through a four-week noncredit training, builds toward a certificate, returns two years later for a degree, and accumulates recognized value at every step. The institution is designed to efficiently give learners exactly what they need, when they need it, in the form most useful to them.

Shifting to Lead

Together, these shifts define a different kind of community college: not a smaller version of a traditional institution with a stronger workforce mission, but an institution that learns from the labor market continuously, organizes around learner mobility, treats work as a site of education, and uses technology to strengthen human guidance rather than merely automate transactions. Versions of each of these shifts already exist in pockets across the sector. The challenge is that they are treated as pilots or side strategies rather than as the emerging operating logic of the institution itself. The colleges that move first will be the ones willing to stop treating these ideas as experiments and start treating them as the next design brief for community college education.

Community colleges are positioned to lead. They are closer to the learners, employers, and regional economies most affected by this moment. But positioning alone is not enough. If community colleges want to define the next chapter of workforce education, they will need to redesign themselves around learner mobility, continuous employer cocreation, work-based learning, and connected pathways. In a period of rapid change, the future belongs to institutions that build for the realities already taking shape.

References

Cengage Group. (2025). 2025 employability report. https://www.cengagegroup.com/news/press-releases/2025/cengage-group-2025-employability-report/

Colorado Department of Education. (2025). News release: Enrollment 2026. https://ed.cde.state.co.us/newsbureau/newsrelease/news-release-enrollment-2026

Credential Engine. (2025). Learning and employment records (LERs) toolkit. https://credentialengine.org/toolkit/learning-and-employment-records-lers/

Koller, B. (2025, February). Adult learner full report. Jobs for the Future. https://www.jff.org/wp-content/uploads/2025/02/Adult-Learner-Full-Final-Report-2.20.25.pdf

Love, I. (2025). Making work-based learning work better for community college students. New America. https://www.newamerica.org/insights/making-work-based-learning-work-better-for-community-college-students/

Mulligan, M. (2025, October). Lawyers using AI to draft contracts. Law Practice Today. https://www.americanbar.org/groups/law_practice/resources/law-practice-today/2025/october-2025/lawyers-using-ai-to-draft-contracts/

National Student Clearinghouse Research Center. (2019). Completing college: Signature report 11. https://nscresearchcenter.org/signaturereport11/

National Student Clearinghouse Research Center. (2025). Some college, no credential: 2025 report. https://nscresearchcenter.org/wp-content/uploads/SCNCReport2025.pdf

Pendell, R. (2025). AI at work nearly doubled in two years. Gallup. https://www.gallup.com/workplace/691643/work-nearly-doubled-two-years.aspx

Udinmwen, E. (2025). AI coding tools are now the default: Top engineering teams double their output. TechRadar Pro. https://www.techradar.com/pro/security/ai-coding-tools-are-now-the-default-top-engineering-teams-double-their-output-as-nearly-two-thirds-of-code-production-shifts-to-ai-generation-and-could-reach-90-within-a-year

Jeff Nesheim is Chief Information Officer and Vice President, Strategy, at Arapahoe Community College in Littleton, Colorado.

Opinions expressed in Leadership Abstracts are those of the author(s) and do not necessarily reflect those of the League for Innovation in the Community College.