The conversation in higher education around AI has shifted.

A few years ago, faculty and administrators were asking whether AI belonged on campus at all. Today, most leaders have moved past that question. Students are using AI tools, regardless of whether institutions sanction them. Employers are already factoring AI fluency into how they evaluate graduates.

The debate about adoption is largely over. What remains unsettled is the harder question underneath it: what does this actually require of us?

That question is easy to defer. AI feels like a technology problem, and technology problems can feel like someone else’s domain. But what’s becoming clear, especially in professional and continuing education, is that the implications extend well beyond any IT department or curriculum committee. They reach into how we think about the purpose of a degree, how we assess students, and whether the learning models we’ve inherited still fit the world our graduates are entering.

Those are not new questions. AI has simply made them urgent.

S. Sriram, Associate Dean for Graduate Programs at the University of Michigan Ross School of Business, frames it this way: “AI is not a new disruption landing on an otherwise stable system. Instead, it is an accelerant, surfacing structural questions that have long been present in higher education.”

That framing resonates with what we see across Noodle’s university partners. The stress points AI is exposing were already there: assessment models that reward task completion over judgment, curricula that lag behind industry by years, and faculty development structures that assume a slower pace of change. AI has compressed the timeline for addressing all of them.

Business education illustrates how this looks in practice. Ross graduates are entering fields where AI already shapes how strategy is developed, content is produced, and decisions are made.

A student without AI fluency is not merely missing a skill; they are missing a fundamental way of working.  As Sriram puts it, “A student who leaves a top business school today without genuine AI fluency is entering a job market that has already moved on.”

The same dynamic is arriving, at varying speeds, in health professions, law, engineering, and public policy. The specifics vary. The underlying challenge does not.

In my own work with university partners, a consistent pattern emerges. During the initial Discovery process, the AI conversation surfaces quickly, and it rarely starts with curriculum. It starts with capacity; faculty who feel unsupported, staff managing more with less, and leaders who know change is necessary but aren’t sure where to begin or what to prioritize. The technology question sits on top of a more fundamental one: do we have the conditions in place to adapt at all?

That’s where the real work is: not in identifying the right AI tools, but in building the organizational readiness to use any of them well.

What remains durable in all of this is worth naming. The experience of learning in community, with real peers and diverse perspectives, is something AI cannot replicate.

Furthermore, the signaling function of a credential from a trusted institution has not diminished. If anything, employers need greater assurance that a graduate possesses genuine judgment rather than just access to powerful tools. The ability to apply knowledge in ambiguous, real-world situations remains irreducibly human. AI raises the bar on that work; it doesn’t replace it.

For leaders in professional and continuing education, the most useful questions right now may not be about tools at all. They’re about design. To move forward, institutions must address three critical inquiries:

  1. Industry alignment: Are we preparing graduates for the industries that exist today, or the ones we remember?
  2. Authentic assessment: What does evaluation look like when AI can complete most traditional assignments?
  3. Institutional support: Are we providing faculty the time and resources to experiment, or leaving them to navigate this shift alone?

These are not rhetorical questions. They are practical challenges forward-thinking practitioners are actively wrestling with, the ones that surface the most useful conversation when the right people are in the room together.

That conversation is happening. The institutions willing to ask harder questions than the technology itself demands are the ones most likely to lead.

Join us June 25, 2026 · 2–2:45 p.m. ET for a live webinar, “What AI Is Really Changing in Higher Education.” Register to attend (a recording will be shared with all registrants).

S. Sriram, Associate Dean for Graduate Programs and Dwight F. Benton Professor of Marketing, Michigan Ross, and Lindsey Gallo, Coopers and Lybrand/Norman E. Auerbach Assistant Professor of Accounting, Michigan Ross, in conversation with Elissa Lappenga, VP Learning Operations, Noodle.

Elissa Lappenga, M.Ed., SHRM-CP is Vice President of Learning Operations at Noodle, where she leads learning designers and technologists in building academic offerings for university partners. She also co-leads Noodle’s Discovery team, working closely with new partners to capture their unique needs and context in ways that set service teams up for success.

Noodle is a higher education growth partner that helps institutions increase enrollment, expand access, and improve learner outcomes through aligned technology and services. From first inquiry to completion, we connect systems, simplify workflows, and deliver 24/7 support and insights that empower teams and strengthen the learner journey.

Major Updates

Workforce Pell Final Regulations Released: What Changed from the Proposed Rule
The Department of Education has released the final rule on Workforce Pell Grants and the new Pell Grant exclusion for students whose non-Federal grant and scholarship aid meets or exceeds their cost of attendance. The Department reviewed approximately 440 comments but, in keeping with its stated intent to track the statute closely, kept the overwhelming majority of its proposed language unchanged. Substantive revisions were confined to a few provisions, some noted below which could be notable for the UPCEA community.

One of the most significant changes responds to widespread pushback on the 25 percent ceiling for instruction delivered through written arrangements with ineligible institutions or organizations. While the Department rejected calls to raise the cap broadly (to 50 percent or higher), they did allow a change that Registered Apprenticeships can have an ineligible entity provide more than 25 percent but less than 50 percent of an eligible workforce program when the program is a related instruction component of a Registered Apprenticeship. Two value-added earnings changes also stand out: a new change excludes students who were enrolled in another educational program during the relevant earnings year — addressing concerns that counting students who “stack” into further education would unfairly depress earnings figures. They also simplified and aligned with the STATS and Earnings Accountability NPRM, combining aggregation steps and dropping the minimum cohort size from 50 to 30 completers. The Department also revised the bar on counting remedial coursework applied to clock-hour as well as credit-hour programs, and concluded the statute did not allow the narrower credit-hour-only approach proposed originally.

For institutions and States, the broader takeaway is what did not change. The Department repeatedly invoked statutory constraints to decline requests to expand program-length limits, bar bachelor’s-degree holders or admit graduate-degree holders, replace bilateral agreements between states with multilateral or SARA-style frameworks, soften the 70 percent completion and job-placement thresholds for high-barrier populations, exclude WIOA or employer-provided assistance from the cost of attendance exclusion, or adopt an interim value-added earnings metric. It likewise rejected proposed new guardrails (categorical bars on “risky” institutions, private-loan/ISA prohibitions, and prescribed OIG audit frameworks), generally finding it lacked authority or that existing program-integrity rules already applied.

Stakeholders should expect any additional flexibility to come from forthcoming sub-regulatory guidance, and the discretion the rule extends to Governors, especially those provided during the first several award years. Read the final rule here.

 

Federal Accreditation Overhaul Clears Negotiated Rulemaking — What You Should Be Watching
On May 21, 2026, the Department of Education’s Accreditation, Innovation, and Modernization negotiated rulemaking committee reached final consensus on a slate of regulatory changes that will substantially rewrite the rules accreditors must follow. The near-unanimous vote (12 of 14 primary negotiators, with student and veteran representatives abstaining) obligates ED to publish a proposed rule that largely tracks the negotiated language. ED is targeting publication and finalization by November, with an effective date of July 1, 2027, though widespread legal challenges are expected. The package implements key pieces of the 2025 executive order on accreditation, easing the path for new accreditors to gain recognition by eliminating the current two-year waiting period and allowing upstart agencies to seek recognition after accrediting just one institution or program, while also forcing programmatic accreditors to sever formal organizational ties and stop sharing personnel, equipment, or infrastructure with affiliated trade associations, a shift that could directly affect accreditors that many professional and continuing education programs rely on.

For administrators of online and professional continuing education units, several specific provisions warrant attention. Standards of student achievement will have to be examined at the program level, with a new required focus on post-completion earnings alongside graduation, licensure, and employment outcomes; a meaningful change for short-term, non-degree, and stackable credentials where earnings data is often thin. Transfer credit is also highlighted, with institutions required to award transfer credit for comparable undergraduate coursework completed at recognized institutions unless they provide a written basis for denial, give students a 15-day window to appeal denials, and publish clear upfront disclosures about how credits are evaluated. Accreditors will also gain new oversight responsibilities around intellectual diversity and viewpoint assessments, including mechanisms to measure student and faculty perceptions (with an exemption for institutions with explicit religious missions), research misconduct procedures covering plagiarism and citation manipulation, verification of consistent enforcement of First Amendment and civil rights policies, and monitoring of institutional expenditures for cost efficiency. Institutional leaders should begin auditing program-level outcomes data, transfer articulation documentation, and faculty evaluation practices now.

Stay tuned for the upcoming proposed rule when it drops this summer or fall, as the public comment window will be the last meaningful opportunity to shape language before the July 2027 effective date. Read more.

 

Other News

Much is being said about the impact of Artificial Intelligence (AI) in the hands of students resulting in “too many “A”s being granted! We are seeing colleges and universities across the country cracking down on “grade inflation.”

It is my long-held belief that striving to have a near-equal number of “A”s and “F”s in a college class is a grossly misdirected goal. This always seemed to me to be better applied to a sorting guideline for assembly line manufacturing. It strikes me that the more “A”s earned in a well–designed class using quality grading rubrics, the better. If viable, relevant, up-to-date learning outcomes are well-assessed, then higher grades on average are commendable.

Yet, in recent weeks we have learned that a couple of our higher education institutions, the venerable Harvard and Yale universities, are considering policies designed to reduce the percentage of “A”s that may be earned in a given class. Writing in the New York Times, Mark Arsenault, reports “One Solution for Too Many A’s? Harvard Considers Giving A+ Grades.” That seems a bit disingenuous; merely changing the title of the grade by adding a plus sign in order to reduce the disreputable number of “A”s posted in a class.

The Times article goes on to quote the dean of undergraduate education. “A number of you tightened up your grading this fall, and your efforts have made a meaningful difference,” the dean, Amanda Claybaugh, wrote in an email to the faculty Monday afternoon. Grades of A fell to 53.4 percent of grades awarded in the fall semester, from 60.2 percent in the prior academic year, Dr. Claybaugh reported. “I know this change wasn’t easy,” she added, noting that some faculty members had said they were receiving less favorable course evaluations from students.

The concern at Yale seems to be similar. Jaeha Jang writes in the Yale Daily News:

While grade inflation may seem like a self-imposed, correctable problem for professors, they told the News that the reality is more complicated. The pressure for student enrollment in their courses, as well as the significance of student evaluations in their review process, incentivize instructors to give generous grades, they said.

The issue is not a wholly new topic, nor one that is confined to a few Ivy League schools. Jane Nam shares essential data in the Best Colleges Web Site in the May, 2024 report on “Grade Inflation in College: Trends and Why It Happens.”

    • The average college GPA was 3.15 in 2020.
    • The median college GPA increased by 21.5% in the span of 30 years (1990-2020).
    • Public, four-year institutions saw the largest GPA jump of all school types, increasing their average GPA by 17% over a decade.
    • Economics students experienced the highest increase in GPA of all majors, with an 18% rise from 1990-2020.
    • Lighter grading standards during the pandemic, schools’ efforts to boost student retention rates, and pressure on faculty to improve student reviews may be drivers behind grade inflation.

There are multiple points of pressure that tend to inflate grading at both the institutional and individual faculty member levels. The cycle builds upon itself, year by year with incremental increases in higher grades.

Yet, I believe, we are operating under a system flawed at its very foundation. The very tool that may be able to rescue us from this building crisis of poor assessments, AI, is the one that is blamed for inflated evaluations of final papers and projects. The flaw is not inherent in AI, rather it is in the failure of faculty members to apply the technology in a way that cultivates learning among all students and accurately assesses mastery of the course content. It is more of a pedagogical breakdown in assessment than a technological artifact.

Our current system of classes deposits 20, 30 or more students in a class with varying depth and breadth of subject matter knowledge; diverse skill levels with analytical and synthesizing tools; and uneven critical and creative abilities. One faculty member teaching several classes in a semester cannot easily guide the diverse groups of students to success, since that would require a more personalized mode of instruction that is responsive to the differing needs of each student. The current model is loosely based on an assembly line of pouring information, knowledge, and skills into the brains of students as they move together at a rigid calendar-driven pace. The hope is that they all will achieve wisdom. Yet, without adapting to the varying needs of the individual students, a significant number are almost always left behind, receiving lower scores on assessments and “C”, “D” or worse at the end of the term.

Fortunately, we are now equipped by AI to effectively and efficiently implement Mastery Learning and supplant the age-old assembly line model with a framework designed to enable all students, over time, to achieve mastery of the desired learning outcomes. The current outmoded system carries the assumption that students can progress even in cases where their achievement is at a less than mastery level. Given the scaffolding nature of knowledge and skill building, we risk creating a flawed scaffold in many of our students who do not fully understand and cannot adequately apply all of the necessary principles, methods, skills and knowledge in a class or degree program. Some are graduated with a substandard grade point average lacking mastery of the topic and likely to see the scaffold of their learning fail them in their subsequent careers.

The Mastery Learning model instead ensures that students do not progress through the course without achieving mastery of each module.

Mastery learning (or, as it was initially called, “learning for mastery”; also known as “mastery-based learning”) is an instructional strategy and educational philosophy, first formally proposed by Benjamin Bloom in 1968.[1] Mastery learning maintains that students must achieve a level of mastery (e.g., 90% on a knowledge test) in prerequisite knowledge before moving forward to learn subsequent information. If a student does not achieve mastery on the test, they are given additional support in learning and reviewing the information and then tested again. This cycle continues until the learner accomplishes mastery, and they may then move on to the next stage.

Using AI, we can continuously monitor each student’s progress through frequent formative assessments of the students. These assessments can be delivered via AI. If a student falls below the 90% (or whatever level is designated), AI can assess the “wrong” answers that were submitted in order to prescribe the best content and pedagogical model to employ in engaging the student. This is repeated as many times as necessary in order to achieve true mastery before moving to the next module with the infinitely patient AI program presenting materials in the best learning context for the individual learner. The supported scaffolding model ensures that students are not lost along the way. What varies is the time or calendar of completing all the modules and final assessment at the mastery level. The instructor monitors progress and intervenes with individual students as needed. Hence, in the Mastery Learning model, all students effectively earn an “A”, however, they do not uniformly complete each class in an 18-week semester.

Prior to AI, we lacked tools to efficiently assess and address the underlying shortcomings of individual students in each of the modules. Now, we can ensure that every student completing a class has mastered the material. However, some students may master the material in a month, others in six months. It seems to me that the time taken for mastery is well spent, rather than an incomplete or erroneous understanding of the course content. You may want to check out more resources on this topic:

At last, perhaps the Bell Curve will come to John Donne’s conclusion: “Ask Not for Whom the Bell Tolls… It tolls for thee.”

 

This column was originally published in Inside Higher Ed. 

“Higher ed doesn’t have a strategy problem. It has an execution problem,” says Jim Lummus, Executive Vice President at University Solutions. For Jim, that tension sits at the heart of almost every challenge facing universities today. “Universities can identify the problem. They can plan for it. But getting things done is so challenging,” he says. “The pace of change is so fast, and traditional processes don’t allow institutions to keep up with that pace.”

Jim has spent more than two decades helping U.S. universities navigate exactly those pressures, working closely with institutions trying to launch and scale online programs. That is also why many of Lummus’s ideas resonate so strongly with OES Learning Solutions’ Leading with S.C.A.L.E. framework. Rather than treating digital transformation as a technology initiative, S.C.A.L.E. positions it as an institutional operating model.

“I think I am a little bit different from others working in the higher education sector,” he says. “While many have made a career within academia and working as faculty members, I have spent the last 20 years doing business with universities to help them to enable their teams to deliver on their strategic goals more efficiently.”

That market-facing lens is precisely what makes his contribution so valuable. Jim understands the internal complexity of universities, but he also understands what happens outside the institution: how students compare options, how families assess risk, how employers think about outcomes, and why some competitors move when others hesitate.

“My perspective is a little more market-oriented,” he says.

In S.C.A.L.E. terms, Jim is speaking directly to the need for ‘Strategy First’: the discipline of aligning institutional mission with market positioning and program economics. For university leadership, this means acknowledging that academic quality alone is no longer enough. Quality must be visible, valuable, accessible, and delivered through models that meet learners where they are.

One of Jim’s most urgent concerns is the growing skepticism around the value of a degree. “Students are questioning the value of degrees,” he says. “Universities are struggling financially, and they unfortunately think the answer is to keep raising their prices, which is like a death spiral.”

It is a stark assessment, but one many leaders will recognize. Rising costs, student debt, demographic pressure, alternative credentials, and public scrutiny have all impacted the higher education value equation. Institutions can no longer assume that reputation will carry demand, or that students will settle for traditional models simply because they once had to.

For Jim, the answer is to modernize how that institutional mission reaches learners.

“If OES [Online Education Services] is helping people to modernize their product, to modernize their content, to make it more accessible, I think of that as a competitive advantage that helps institutions to actually have students in the courses,” he says. “It is one thing to create a great course, but if nobody signs up for it…”

That final point is crucial. In higher education, “product” can be an uncomfortable word. But for leadership teams responsible for institutional sustainability, it is also a necessary one. A program may be academically sound, but if it is poorly positioned or slow to reach the market, its impact is limited.

Jim also challenges institutions to think more rigorously about what “student-centered” really means. Universities often use the phrase as a statement of intent. Jim pushes it toward operating discipline.

In commercial environments, organizations constantly test, listen, refine, and adapt based on customer behavior. Higher education does not need to mimic business blindly, but it does need stronger mechanisms for understanding how learners experience its offerings, and for converting that insight into action.

That is why S.C.A.L.E.’s emphasis on ‘Learner-centric Quality’ matters. Student-centeredness cannot live in strategy documents alone. It has to be built into governance, design processes, faculty engagement, program development, market testing, and continuous improvement.

Jim is equally candid about the internal barriers that can slow progress. Universities are complex political environments. Priorities compete. Decision rights are sometimes unclear. Procurement processes are spooled. Bureaucracy can become a substitute for strategy.

“Sometimes private schools will say they have to do an RFP, and I will say: no, you do not. You are choosing to do an RFP. If you are choosing to do an RFP, that means you are really interested in comparison shopping. So, what are you comparing? What are you going to look at: vendor X versus vendor Y? If you tell me what outcomes or capabilities you actually care about, I can already tell you which vendors are likely to succeed or fail against those criteria.”

For university leadership, the lesson is not to avoid due process. It is to be intentional. If an institution is evaluating partners, programs, or investments, it needs clarity on the outcomes it is trying to achieve. Otherwise, process can create the illusion of progress while strategic momentum disappears.

That need for action is becoming more urgent as AI accelerates both the pace and the nature of change in the sector. Simply put, Jim sees AI as a test of institutional responsiveness. Universities are under pressure both to capitalize on new opportunities and to manage new risks. The challenge is that many are not built to move quickly enough to do either well.

In Jim’s view, the danger is not only that institutions make the wrong decision about AI. It is that they remain structurally unable to evaluate, pilot, govern, and scale emerging models at the speed required.

That is the logic behind S.C.A.L.E.’s focus on ‘Evergreen Innovation’: building digital education systems that can evolve over time, rather than treating every new technology wave as an emergency.

For Jim, the leadership imperative is clear: “It is about offering leadership on what it takes to be bold and succeed in this kind of market.”

Boldness, in this context, means having the courage to ask difficult questions about value, cost, access, differentiation, and speed. It means recognizing that alternative pathways like employer-backed credentials and shorter-form skills programs are, in fact, increasingly loud market signals that learners are rethinking so much that had been taken for granted in the past.

Universities still have extraordinary assets: faculty expertise, trusted credentials, deep communities, research strength, and missions that truly matter. But those assets need to be activated through models that are both educationally excellent and commercially realistic.

Listening to Jim, the future belongs to institutions that can connect mission with market discipline; bring faculty, partners, and leadership into genuine alignment; move high-quality programs from idea to launch with greater speed; and design learning experiences that students experience as worth the investment.

That is why his ideas resonate so strongly with S.C.A.L.E. The framework gives leaders a way to organize the work, so that ambition becomes execution, and digital transformation becomes a durable operating capability.

Jim’s final reflection captures the spirit of the conversation:

“I think the OES S.C.A.L.E. framework is a really interesting piece and I would encourage everyone in higher ed to read it. It definitely outlines principles that matter and gives you tactical and practical steps to move forward.”

Click here to download Leading with S.C.A.L.E.

OES partners with universities to design and deliver scalable, student-centered learning experiences. With over 14 years of experience, we bring deep expertise, proven infrastructure, and a collaborative approach to every engagement. Our end-to-end services span learning design, market analysis, media production, student support, and more—enabling institutions to deliver online programs that are academically rigorous and built to scale. We have a deep understanding of how people learn online. We know what engages learners and what keeps them motivated, and this shapes every solution we offer. By combining data insights, technology, and innovation, we create seamless teaching, learning, and student experiences that support learner success in a rapidly evolving world.

 

Will artificial intelligence close the student success gap or widen it into a permanent caste system?

That is the question higher education leaders should be asking as AI advising tools move from pilot to procurement. The temptation is to treat AI as a cost-savings lever: deploy a chatbot, deflect the tickets, claim a productivity win. But the most instructive case study of the last three years points in a different direction, one where AI is deployed precisely because equity is the goal, not the casualty.

In Chapter 8 of AI Applications in Online Higher Education Administration, I trace the evolution of AI student support technologies from rule-based chatbots to context-aware AI assistants to autonomous AI agents. The question is not what AI can do, but what we choose to let it do and for whom.

The Hybrid Advising Co-Op as Proof of Concept

Founded in 2022 by the Bill & Melinda Gates Foundation and facilitated by Shift, the Hybrid Advising Co-Op brought together six organizations that share a common student population: first-generation, low-income, and historically underserved learners. The partners like Bottom Line, Let’s Get Ready, OneGoal, College Advising Corps, KIPP Public Schools, and technical partner Mainstay, each developed a distinct hybrid model.

Bottom Line’s Blu leans bot-forward, automating the high-volume transactional layer of advising. Let’s Get Ready built a near-peer coaching system where AI supports human coaches rather than substitutes for them. Across all six models, the design principle was the same: AI absorbs the predictable, repetitive load like appointment reminders, FAQ responses, deadline nudges, registration logistics, so that human advisors can invest their finite time in the relational, high-stakes mentoring that actually moves the needle for vulnerable students.

The Two-Tier Risk Is Already Here

The ethical concern Chapter 8 names explicitly is the emergence of a two-tier advising system: privileged students continue to receive high-touch human advising, while underserved students are routed to automated agents. This is not a hypothetical. It is the path of least resistance for institutions facing high caseloads, advisor turnover, and pressure to scale.

The data should make us cautious. Khan Academy’s MAP Accelerator study, what is now called the “5 Percent Problem”, found that only the small fraction of students who engaged with the platform more than 30 minutes per week showed measurable academic gains. The other 95% were effectively excluded from the reported benefit. Automated systems tend to reward students who already possess the time, infrastructure, and self-direction to use them. The students who most need advising are precisely the ones least likely to extract value from a self-service AI experience.

Add cultural competency to the equation and the risk sharpens further. AI models trained on colorblind assumptions can perpetuate bias against the students they are nominally serving. The U.S. Department of Education’s Office for Civil Rights, acting on Executive Order 14110, has already flagged discrimination risks in AI deployments affecting minority students, multilingual learners, and students with disabilities.

Chapter 8’s Five Ethical Standards

For UPCEA members designing AI-enabled advising in professional, continuing, and online divisions, divisions that disproportionately serve adult, first-generation, and underrepresented learners, Chapter 8 offers five ethical anchors:

  • Autonomy. AI suggestions should never override student agency or professional judgment.
  • Fairness. Equitable treatment is not a feature; it is a precondition.
  • Transparency. Students should know when and how AI is shaping their advising experience.
  • Accountability. Responsibility must be assignable when AI-generated advice fails.
  • Human-Centered Design. Some advising scenarios require empathy that only a human can provide. Design for that, do not engineer around it.

Three Moves for UPCEA Practitioners

If your institution is implementing AI in student support, three practical moves protect against the two-tier outcome.

First, audit the routing logic. Inequity rarely lives in the algorithm itself. It lives in the decision about which students get the chatbot and which students get the appointment. Map the distribution. Look for patterns by income band, first-generation status, and program modality.

Second, automate the transactional, protect the relational. The Co-Op’s design discipline is replicable. Identify the advising tasks that are genuinely repetitive like registration FAQs, milestone reminders, document collection and let AI carry that load. Then deliberately reinvest that recovered advisor time in deeper relationships with the students whose success is most fragile.

Third, do not let efficiency refill the caseload. Chapter 8 warns that the natural institutional response to advisor capacity gains is to expand caseloads, not deepen service. If your AI deployment results in advisors carrying 400 students instead of 300, you have not solved a problem. You have entrenched one.

The same design logic that drives the Hybrid Advising Co-Op operates at the individual faculty level. In my open-access guidebook The Learn-It-All Educator (Machajewski, 2026), I argue that AI should function as a cognitive gym, not a cognitive elevator, a tool that adds productive friction rather than removing it. The Cognitive Triage framework distinguishes FLUFF (Formatting, Layouts, Under-the-hood, Filing, Filtering: work worth delegating) from SPARK (Specific, Persuasive, Authentic, Rigorous, Keen-insight: ideas worth thinking and protecting for human thought). The Co-Op essentially applied this split at the institutional scale: AI handles the FLUFF of advising (reminders, FAQs, registration logistics) so advisors can invest in the SPARK of mentorship. The principle scales from one faculty member’s workflow to a six-organization consortium. Free OER: https://doi.org/10.5281/zenodo.19041123

 

The Real Standard

The standard for AI in higher education advising is not efficiency. It is whether the deployment expands access to human relationship for students who have historically been denied it. The Hybrid Advising Co-Op met that standard. The question for each of us is whether our institutions will.

 

Dr. Szymon Machajewski is Associate Director of Academic Technology and Learning Innovation at the University of Illinois at Chicago and Adjunct CIS Professor & Fellow for Teaching with AI. Dr. Machajewski is the author of The Learn-It-All Educator: A Guidebook for Training Brains, Not Replacing Them with AI and “Chapter 8: AI in Online Administration” in AI Applications in Online Higher Education Administration: Strategies for Maximizing Returns and Improving Outcomes. 

New guidance from UPCEA highlights the growing urgency of data quality, learner mobility, and outcomes reporting for four-year institutions

WASHINGTON, D.C., May 14, 2026 — As Workforce Pell and new federal accountability measures reshape expectations for postsecondary outcomes reporting, colleges and universities must rethink how they track, verify, and communicate learner success, according to a new report from UPCEA, the online and professional education association.

The report, Synchronizing Pathways: Advancing the National Dialogue on Data Quality and Learning Mobility, examines the growing pressure on institutions to build more unified, transparent, and interoperable systems for non-degree credential data. Focused specifically on four-year institutions, the report outlines the operational, policy, and governance challenges institutions face as federal scrutiny around workforce outcomes intensifies.

The report synthesizes recent UPCEA member discussions, emerging federal requirements, and field-tested institutional practices into a practical framework institutions can adapt to their local and state contexts.

Data Quality Has Become a Strategic Imperative

The report argues that Workforce Pell and the One Big Beautiful Bill Act (OBBBA) have moved non-degree data from the margins of academic operations into a campus-wide strategic priority. Institutions are now expected not only to measure completion, but also to be accountable for placement, earnings, and workforce value.

According to the report, many colleges and universities are still operating with disconnected credit and non-degree systems, inconsistent credential definitions, and limited access to verified employment and wage data.

“Higher education is entering a new accountability era where data quality is directly tied to institutional credibility, learner mobility, and access to federal funding,” said Amy Heitzman, Ph.D., Deputy CEO and Chief Learning Officer at UPCEA. “Institutions cannot treat non-degree data as a side issue anymore. Having unified learner records and trustworthy outcomes reporting is becoming foundational to the future of credential innovation.”

Key Challenges Identified in the Report Include:

  • Disconnected systems and fragmented learner records that make it difficult to track stackable and non-degree pathways
  • Limited access to verified wage and employment data, especially as self-reported outcomes become insufficient for federal accountability
  • Privacy and consent concerns related to sensitive learner data and wage record matching
  • Inconsistent terminology and credential taxonomies across institutions and systems
  • Cross-state and third-party reporting complexities that complicate workforce outcomes tracking for multi-state learners and external credential providers

The report notes that these challenges are particularly significant for four-year institutions, many of which operate robust non-degree portfolios that serve as entry points for adult learners and workforce-aligned education.

Opportunities for Institutional Alignment and Learner Mobility

While the report outlines substantial barriers, it also emphasizes that this moment presents an opportunity for institutions to align systems, improve learner experiences, and strengthen workforce storytelling.

Among the report’s recommendations are:

  • Developing institution-wide governance and taxonomy frameworks for non-degree credentials
  • Creating comprehensive learner records that connect credit and non-credit learning
  • Implementing privacy-protective and interoperable digital credential systems
  • Building partnerships with employers, workforce boards, and state longitudinal data systems
  • Establishing cross-functional task forces that include registrars, institutional research, IT, financial aid, and online and professional continuing education leaders

“Online and professional education units have long operated at the intersection of workforce responsiveness and institutional innovation,” said Julie Uranis, Ph.D., Senior Vice President for Online and Strategic Initiatives at UPCEA. “What this report demonstrates is that institutions now need infrastructure and governance models that match the pace and complexity of modern learning pathways.”

The report also spotlights UPCEA’s Workforce Pell Resources, including our Workforce Pell Readiness Checklist, designed to help institutions assess preparedness across governance, technology, financial aid, compliance, and student support systems.

Grant-Funded National Initiative

The report is part of a broader national initiative launched by UPCEA to advance credential transparency and learner mobility through improved data quality and institutional capacity-building. The project, titled “Synchronizing Pathways: Expanding Institutional Capacity for Improving Credential Data Quality and Learning Mobility,” is funded through an incubator grant from Strada Education Foundation.

The initiative focuses on helping institutions build clearer, more transferable, and career-relevant non-degree credential pathways for adult learners navigating evolving educational and workforce landscapes.

To read the full report and access UPCEA’s Workforce Pell Resources, visit https://upcea.edu/synchronizing-pathways-advancing-the-national-dialogue-on-data-quality-and-learning-mobility/. 

 

About UPCEA

UPCEA is the online and professional education association. Our members continuously reinvent higher education, positively impacting millions of lives. We proudly lead and support them through innovative research, professional development, networking and mentorship, conferences and seminars, and stakeholder advocacy. Our collaborative, entrepreneurial community brings together decision makers and influencers in education, industry, research, and policy interested in improving educational access and outcomes. Learn more at upcea.edu.

 

A person (Vickie Cook) smiling

By Vickie S. Cook, Ph.D.

Each spring, campuses quietly rehearse a familiar transition. The cadence shifts. Energy returns. Commencement ceremonies are scheduled, and multiple beautification processes are underway, from reenergized flower beds to window washing and clean walkways.  What was dormant begins to move again. In higher education, this seasonal rhythm offers more than symbolism. It provides a useful leadership lens for how institutions can approach one of the most significant disruptions of our time: the integration of artificial intelligence.  Just as campus teams intentionally plan seasonal improvements, institutional leaders must be equally deliberate in preparing for AI-driven change. 

AI is not a future-state consideration. It is already reshaping academic work, student services, enrollment operations, and institutional decision-making. Yet, like any meaningful transformation, the barrier is not access to tools. It is the human experience of change that ultimately determines success. 

Leadership, therefore, must move beyond advocacy for innovation and toward a disciplined, strategic approach to managing this transition. 

Recognize That Resistance Signals Loss, Not Opposition 

Even in seasons of renewal, not all aspects of change are experienced positively.  A consistent finding in change management literature is that resistance is rarely about the change itself. It is about perceived loss. As William Bridges (2009) argues in his transition model, individuals do not resist change; they resist endings. This transition model leans heavily on ensuring people have a purpose, a plan, and a part to play.   

In the context of AI adoption, these losses are tangible: 

  • Established workflows that provided predictability and safety 
  • Professional identity tied to expertise that is now augmented by AI 
  • Familiar student engagement practices that are no longer meeting needs 
  • Decision-making processes increasingly incorporate AI-supported insights 

To help build the purpose, plan, and part others will play, Kathleen Ives, Marie Cini, and Ray Schroeder, in AI Applications in Online Higher Education (2026), emphasize that institutional transformation is less about technological deployment and more about human adaptation to new modes of work and interaction. Effective AI strategy begins with acknowledging what faculty, staff, and administrators believe they are losing. Creating structured space to process this transition is critical to leading a campus toward AI integrations. 

Focus Institutional Energy on What Can Be Controlled 

Periods of disruption tend to expand institutional anxiety. AI introduces uncertainty around policy, ethics, workforce implications, and academic integrity. Attempting to resolve all of these simultaneously often results in organizational inertia. 

Here, a principle aligned with modern leadership theory becomes useful: focus on controllable actions. John Kotter’s (2012) work on leading change reinforces the importance of creating short-term wins and actionable steps to sustain momentum. 

For institutions, this translates into: 

  • Identifying 2–3 high-impact AI use cases (e.g., student communications, advising triage, enrollment analytics) 
  • Establishing clear governance and ethical guardrails 
  • Measuring operational improvements (time saved, responsiveness, engagement rates) 

Rather than pursuing comprehensive AI transformation or introducing an overwhelming array of tools, institutions benefit from disciplined, incremental progress that builds confidence and organizational learning. 

Anchor Stability While Introducing Innovation 

One of the more overlooked aspects of change-leadership is the need to preserve continuity. Excessive simultaneous change, such as new tools, new policies, and new expectations, can destabilize even high-performing institutions. 

In Leading Change, Kotter highlights the importance of maintaining core cultural anchors while introducing new behaviors. 

Within higher education, these anchors include: 

  • Institutional mission and access commitments 
  • Shared governance structures 
  • Core academic values (quality, rigor, integrity) 

AI should not replace these anchors; it should enhance their execution.  Leaders should frame AI not as a disruption to institutional identity, but as an extension of existing commitments particularly around access, student success, and operational effectiveness. 

Normalize Discomfort as Part of the Process 

A common leadership misstep is overemphasizing optimism and enthusiasm. While positive framing has value, it can inadvertently invalidate legitimate concerns. 

Heifetz, Grashow, and Linsky (2009), in their work on adaptive leadership, argue that productive change requires maintaining a level of “constructive disequilibrium” with enough discomfort to prompt growth, but not so much that institutional systems shut down. 

AI adoption inherently creates this tension: 

  • Faculty questioning academic integrity implications 
  • Staff concerned about role evolution 
  • Leaders balancing innovation with risk management 

Institutions should not attempt to eliminate discomfort, but rather structure it through dialogue, professional development, and clear expectations. This requires a continued focus on people, their roles in implementation, and the purpose for which AI is introduced into specific functions. 

Shift from Certainty to Curiosity in Institutional Culture 

Perhaps the most critical leadership shift is cultural. AI introduces a level of uncertainty that traditional higher education governance models are not historically designed to manage efficiently. 

In this context, curiosity becomes a strategic asset. Amy Edmondson’s (2018) research on psychological safety underscores that organizations learn faster when individuals feel safe to experiment, question, and iterate. 

The Ives, Cini, and Schroeder framework similarly points to the need for institutions to move toward: 

  • Continuous experimentation 
  • Iterative implementation 
  • Cross-functional collaboration 

Leaders must explicitly model curiosity by asking better questions, encouraging exploration, and rewarding learning rather than perfection. 

A Seasonal Model for AI Change Leadership 

Spring offers a useful metaphor, but also a practical framework: 

  • Prepare the ground (Acknowledgment): Identify losses and concerns 
  • Plant deliberately (Action): Launch focused AI use cases through a planned structure 
  • Stabilize roots (Continuity): Reinforce institutional anchors of mission and vision 
  • Allow growth tension (Adaptation): Normalize discomfort and environmental safety 
  • Cultivate curiosity (Culture): Encourage experimentation and create space for informed risk-taking 

This approach aligns with both classic change management theory and emerging guidance on AI integration in higher education. 

AI will continue to reshape higher education, regardless of institutional readiness. The differentiator will not be which institutions adopt AI, but how they lead through the transition toward success. 

Spring reminds us that change is not inherently disruptive; it is developmental. With intentional leadership, AI can follow the same pattern: not a force to be managed reactively, but a season to be led strategically. 

References 

  • Bridges, W. (2009). Managing Transitions: Making the Most of Change. Balance. 
  • Edmondson, A. (2018). The Fearless Organization. Wiley. 
  • Heifetz, R., Linsky, M., & Grashow, A. (2009). The Practice of Adaptive Leadership. 
    Harvard Business Review Press. 
  • Kotter, J. P. (2012). Leading Change. Harvard Business Review Press.  
  • Ives, J., Cini, M., & Schroeder, R. (2024). AI Applications in Online Higher Education. Routledge.  

Vickie Cook is a nationally recognized higher education leader specializing in enrollment strategy, online and digital learning, organizational transformation, team development, and leadership growth. She currently serves as a Senior Fellow and Strategic Advisor for UPCEA.  To learn more about UPCEA Research and Consulting, please contact [email protected].   

The Chief Online Learning Officers at colleges and universities are increasingly charting the future of teaching and learning.

It was three decades ago that my career in higher education took a turn. I was promoted to full Professor and given the golden opportunity of my career to lead our campus in the use of the internet to enhance teaching and learning. Little did I know in 1997 just how significant this enhancement in the development and delivery of learning opportunities would become. It continues to expand while the rest of higher education modes in America are threatened by the realities of new federal regulations, shifting political priorities, return on investment value concerns, flagging funding, and sluggish responsiveness to the priorities of learners and employers.

The then new President of the University of Illinois system, Dr. James J. Stukel saw the enormous potential of the internet in higher education. He assigned his new Vice President of Academic Affairs, Dr. Sylvia Manning to lead the charge to infuse the opportunities of the net across the Illinois campuses. In turn, she acquired the assistance of Engineering Professor, Burks Oakley to help lead the implementation. I was the beneficiary on the Springfield campus to receive release time and funding to create an Office of Technology-Enhanced Learning to foster use of the internet in classes as a source of new information; promote an opportunity for inter-institutional collaboration; and encourage the delivery of credit classes online to students who lived around Illinois, the U.S. and the world. It began a career-long collaboration with Burks Oakley, one that lasts to this day as we share information and perspectives about online higher education.

It was a heady time when we launched the initiative. Just a few years earlier, the National Center for Supercomputing Applications on the Urbana campus released Mosaic, the first visual browser that stimulated an explosion of growth for the World Wide Web. In 1997, we began in earnest the online learning initiative, bringing the university to the student rather than requiring the student to travel to the physical campus. This was a revolutionary democratization of access to higher learning; bringing professors, scholars and researchers to the global public at large.

At that time, only some 750,000 students were taking a course online. That was about 5% of total American university enrollments. Twenty years ago, the number had risen to nearly three-and -a-half-million students or 20% of all students. Ten years ago, we rose above six million students or 31%. Last academic year, some eleven million students or nearly 55% of American college students were taking one or more courses online. Certainly, the Covid-19 pandemic shutdown of most campuses in Spring 2020 accelerated the already established growth of online distance learning. With campuses generally closed across the country, we all scrambled to move campus-based classes to the internet. When campuses re-opened following the shut-down, many students, faculty members and college departments recognized the advantages of the online delivery format, especially in professional programs. That added momentum to the already-established trend toward online delivery.

We are now on the cusp of a significant adjustment in the model of higher education. This comes in the context of significant defunding of college and university research grants resulting in dropping federal revenues; current trends toward higher tuition and fees; eroding confidence in the return on investment of college degrees among consumers; and rapidly shrinking international student enrollments due to significant cuts in the number of approved student visas on campuses. Yet, one of the few bright signs is the continuing surge in online learning enrollments, particularly for self-paced and professional certificate programs.

Also at this moment in the history of higher education, we are being introduced to Agentic Artificial Intelligence. Over the past three years, most universities have worked out effective policies and best practices in using Generative AI by students for classroom assignments, theses and dissertations. In many institutions, it is the Chief Online Learning Officer (COLO) who is leading the planning for best uses of the technology to deliver the curriculum in the context of the many aforementioned challenges.

Legendary researcher and theorist Clayton Christensen embraced online learning as a prime example of Disruptive Innovation in education. I believe that, were he alive today, Christensen would have embraced Artificial Intelligence (AI) as another disruptive technology in our field. Certainly, it has already become that in the business realm. In the higher ed realm we have utilized AI to power adaptive and personalized online learning models.

Sue Ebbers, PhD writes in “Adaptive and Personalized Learning Through AI: A Realistic Assessment of Value” we must use both caution and judgement in embracing the enormous power and advantage of AI in higher education, “As this trend will inevitably go forward, may we balance the many amazing affordances that adaptive and personalized AI clearly deliver to learners with a healthy dose of caution and care.” That measure of caution and care will come from those who have confronted analogous challenges while leading online learning through the formative and more recent years.

It is a natural extension of the current AI programming. Given the nature of the technology, applications are delivered online. Utilizing these emerging and developing technologies to make online delivery even more efficient and more effective is a key challenge facing higher education. Without this, we are threatened with becoming less relevant and more expensive than we are now. As we remake ourselves to meet the challenges discussed earlier, I see the potential of adaptive and personalized learning as the pathway to meeting societal needs and individual learner needs for depth of understanding, creativity and just-in-time workforce learning that is so pressing in today’s rapidly changing economy. Utilizing AI technology to provide learning customized to the individual needs, we can ensure greater student satisfaction than we could ever do with the age-old model of teaching to a classroom filled with students of varying competencies and desired outcomes.

Who else within the institution’s administration has the combination of technological, pedagogical and innovative knowledge and experience to lead us into the future? The COLO’s knowledge of advanced technologies coupled with the experience of overseeing the application of the vast array of online technologies as they have evolved over the past 30 years, is the combination we need to succeed. Our Chief Online Learning Officers bring credibility and sagacity to the table in leading us while making this critically important next step in enhancing online learning in higher education.

 

This column was originally published in Inside Higher Ed.

A man (Bruce Etter) is dressed in a blue suit smiling for a headshot.

By Bruce Etter

I have always appreciated how honest wood is. Look at a cut stump and the rings tell a story: good years, lean years, drought, recovery. That feels like the right way to read the newly released 2026 State of Continuing Education report from UPCEA, Modern Campus, and The EvoLLLution. As this partnership reaches its five-year mark—the traditional “wood” anniversary—the framing feels especially fitting. Wood symbolizes strength, resilience, and continued growth, and after five years of data, the grain of continuing education is becoming easier to read. 

This year, readers can do more than skim the key takeaways. There is also an interactive version of the report that allows people to explore the charts and move through different dimensions of the data for themselves. That feels fitting, too. After five years, we are not just counting rings, we are studying the grain, testing the joints, and considering what kind of structure higher education is building with continuing education at its core. 

The 2026 ring is a strong one. The report shows continuing education gaining real momentum, especially where workforce alignment is concerned. Microcredentials reached an all-time high, offered by 88% of online and PCE units. Stackable credentials and test or industry credential preparation also hit their highest levels to date. Average enrollment ticked up to 16,046, and continuing education’s reach into corporate, alumni, and government audiences continues to expand. That is not the picture of a side enterprise. It is the picture of a part of the institution that is increasingly central to relevance, responsiveness, and revenue. 

What I also appreciate about this year’s findings is that they do not confuse growth with ease. Anyone who has worked with wood knows that strength is not just about what you can see on the surface. A sturdy beam carries weight because of what is inside it: the density, the grain, the way it has been shaped and supported over time. The same is true in continuing education. Sixty-seven percent of institutions say they are at least somewhat likely to expand short-term, workforce-aligned programs in response to Workforce Pell. Institutions broadly see that as a meaningful opportunity. However, many may not be able to seize that opportunity, at least in the short term, as 42% are not prepared to meet the data collection and reporting requirements that come with it. In other words, the appetite is there, but the infrastructure is not always seasoned enough for the load. 

That tension between ambition and infrastructure appears throughout the report. Leaders clearly understand where the market is going, yet many are short on the supports needed to move at market speed. Thirty percent of units report lacking marketing support, 22% lack instructional designers, and business development gaps remain notable as well. Meanwhile, the biggest barriers to expanding credentials are not the old familiar talking points about faculty resistance. They are concerns about market demand, administrative burden, and time-to-market. That is an important shift. The knots in the wood are no longer at the margins. They are embedded in the operating model. 

The joinery matters, too. Even the strongest wood can fail if the joints are weak, and this report makes clear that too many institutions are still asking continuing education to scale on top of disconnected systems. Only 27% of respondents say their unit’s technology integrates seamlessly with main campus systems. Agreement that other units collaborate with continuing education on program development dropped from 71% in 2025 to 62% in 2026. Only 13% say continuing education offerings are well integrated into the institution’s traditional portfolio. Continuing education may be growing stronger, but too often it is still being bolted onto the institution instead of built into it. 

That is why the wood anniversary is more than a framing device. Wood is strong not because it avoids pressure, but because it grows through it. Over the last five years, this report series has documented a sector weathering pandemic aftershocks, shifting learner demand, new policy possibilities, and persistent technological friction. What the 2026 report makes clear is that the question is no longer whether continuing education should be workforce-aligned, flexible, and outcomes-focused. The question is whether institutions are willing to build the institutional frameworks of staffing structures, data systems, and cross-campus coordination needed to support that ambition. 

 

Bruce Etter serves as the Senior Director of Research and Consulting at UPCEA, where he leads the development and management of research initiatives for UPCEA’s Research and Consulting division and its clients.

A person (Dave Jarrat) smiling

By Dave Jarrat

In the world of online and professional continuing education, we often talk about “meeting students where they are.” When it comes to the military community, that isn’t just a metaphor, it’s often a physical location.

For marketing teams at UPCEA member institutions, military installations are a unique ecosystem. To succeed here, you cannot simply port over your civilian digital strategy. You need a “boots on the ground” approach that respects the culture, understands the regulations, and, most importantly, recognizes the diversity of the military-connected student.

1. Defining Military-Connected

A common pitfall in higher ed marketing is treating the military as a monolith. To build a truly effective enrollment strategy, your messaging must distinguish at the highest level between three key subgroups:

  1. Active Duty: Currently serving members. Their primary concerns are portability, Tuition Assistance (TA) caps, and asynchronous schedules that survive deployments.
  2. Veterans: Those who have separated or retired. They are often focused on the GI Bill®, transitioning to civilian careers, and finding a “tribe” on campus.
  3. Military Dependents: Spouses and children. Spouses, in particular, are a massive, underserved market. They face high unemployment rates due to frequent moves (PCSing) and need programs that move with them.

2. Navigating Student Personas

Beyond the major subgroups above, it’s also important to understand the various personas that make up the military-connected student community.  The Guide to Military-Connected Students from MissionWise provides an excellent starting point.  Leveraging these personas, you can outline a more tailored approach to on-base marketing.  For example:

Persona Key Motivation Best On-Base Touchpoint
 

The Transitioning Specialist

 Converting military skills into a high-paying civilian job. Transition Assistance Program (TAP) briefings.
 

The Career Advancer

 Earning a degree to qualify for a promotion or commission. Education Center “Lunch & Learns.”
 

The Resilient Spouse

 Finding a portable career that survives the next move. MWR Family Readiness Group (FRG) meetings.
 

The Veteran Re-skiller

 Using remaining GI Bill® benefits to pivot industries. On-base Veteran Service Organization (VSO) events.

 

 

3. Mapping the Local Mission

Another common mistake is assuming that a general recruitment pitch works on every installation. In reality, the academic needs of a base are dictated by its specific mission and the primary units stationed there. To be effective, your program offerings must align with the local job descriptions.

  • Analyze the Unit Mission: Is the installation a Logistics and Sustainment hub, a Cyber/Intel center, or a Medical command? If you are outside a major maintenance depot, lead with Engineering or Supply Chain Management. If it’s a training base for junior soldiers, focus on General Education Mobiles (GEM) or Associate degrees that help them earn promotion points quickly.
  • Identify the “High-Density” MOS/Ratings: Every base has a dominant career field (Military Occupational Specialty). Research the largest tenant units on the installation to identify which degrees translate most naturally to their daily work. For example, a base with a heavy Aviation mission is the perfect place to highlight Aviation Management or Safety programs.
  • Rank-Specific Academic Pathing:
    • Junior Enlisted: Usually seeking fast, stackable credits to “check the box” for promotion to Sergeant or Petty Officer.
    • Senior NCOs: These are the “middle managers” looking for Organizational Leadership or HR degrees to prepare for a corporate “second career.”
    • Officers: Almost all possess a bachelor’s degree; they are your primary market for specialized Master’s programs (MBA, MPA, or STEM) required for advanced promotion boards.
  • The “Dwell Time” Factor: Understand the deployment cycle of the local units. If a unit is in a high-rotation cycle, your marketing should lead with asynchronous flexibility and “deployment-proof” digital platforms. If it is a stable “shore duty” or training command where families stay for 3–4 years, emphasize your local campus community and networking opportunities.

Finally, don’t ignore the “Gray Suit” population. Most bases have a massive contingency of DoD Civilians and Contractors who work alongside the military. They often have stable schedules and generous professional development budgets, making them a prime audience for graduate certificates and executive leadership programs.

 

4. The Gatekeepers: ESO, MOU, and MWR

You can’t just walk onto a base and start handing out flyers. You need to navigate the administrative landscape first.

The Educational Services Officer (ESO)

The ESO is your most important relationship. They manage the voluntary education programs for the installation. Your goal is to be a partner, not a pest. Show them how your programs fill a specific gap in their troops’ professional development.

The DoD MOU

Compliance is the baseline. To recruit on-base or receive TA funds, your institution must be a signatory to the Department of Defense Memorandum of Understanding (MOU). This document dictates how you can market; for example, “high-pressure” recruitment tactics are strictly forbidden.

Partnering with MWR

While the ESO handles the books, Morale, Welfare, and Recreation (MWR) handles the life. MWR manages gyms, bowling alleys, and community events. Partnering with MWR through sponsorships (like a “Back to School” 5K or a holiday festival) allows you to build brand affinity with dependents and families in a relaxed environment.

 

5. High-Impact, Cost-Effective Tactics

You don’t need a Super Bowl budget to win on-base. You need consistency.

  • The “Payday” Presence: Base traffic spikes on the 1st and 15th of the month. Ensure your information tables are staffed at the Exchange (PX/BX) or Commissary during these windows.
  • Asset Authenticity: If you are sending a recruiter to a base, send a Veteran. A “Green-to-Gold” alum or a military spouse staff member has immediate credibility that a civilian recruiter might lack.
  • Localized Digital Geofencing: While this post focuses on “on-base” physical marketing, you can support your physical presence by geofencing the installation’s coordinates with mobile ads. When a servicemember checks their weather app before heading out for the day, they should see your targeted banner.

6. Speaking the Language

Military-connected students have a high “BS detector.” Avoid stock photos of people in “vaguely military” uniforms with long hair or unpolished boots.

Focus your copy on the “Big Three”:

  1. Transferability: “How many of my JST (Joint Services Transcript) credits will you take?”
  2. Affordability: Mention the Yellow Ribbon Program and how you handle the $250/credit hour TA cap.
  3. Flexibility: Use terms like “Deployment-proof” or “PCS-friendly.”

Note: Always include the required disclaimer: “Appearance of U.S. Department of Defense (DoD) visual information does not imply or constitute DoD endorsement.” It shows the ESO you know the rules.

 

The Bottom Line: Relationships Over Transactions

On-base marketing is a long game. It’s about being there for the 5K run, the education fair, and the transition briefing. When a spouse is looking for a degree that won’t break during a move to Germany, or a Sergeant is looking to become a Civilian Manager, your institution’s name should be the one they’ve seen consistently at the MWR and the Ed Center.

 

Dave Jarrat serves as a Senior Fellow for UPCEA and as a Strategic Advisor to a broad range of higher education institutions and organizations, including the University of Cambridge, Edquity and Scholarships360. He is a social impact executive focused on improving educational opportunities and outcomes for historically underrepresented populations.