By Vickie S. Cook, Ph.D.

The recently released UPCEA Hallmarks of Excellence in Digital Learning Enterprises reflect the continued evolution of digital learning from an emerging modality to a mission-critical component of institutional strategy. This series explores each Hallmark, why it matters for today’s institutions, and how institutional leaders can begin using the accompanying benchmarks to assess progress and guide continuous improvement. 

Nearly a decade has passed since UPCEA first published the Hallmarks of Excellence in Online Leadership. During that time, higher education has experienced profound changes in learner expectations, technology, workforce demands, institutional finances, and the role digital learning plays in fulfilling institutional missions. The updated Hallmarks of Excellence in Digital Learning Enterprises reflect that evolution. 

The most significant change is not technological. It is organizational. Digital learning is no longer viewed as a specialized initiative or a separate organizational unit. Instead, the Hallmarks position digital learning as an enterprise-wide capability that influences institutional strategy, academic planning, learner success, technology governance, enrollment management, and long-term sustainability. 

Hallmark 1: Internal Advocacy and Leadership Within the Institution establishes the foundation for the remaining Hallmarks because meaningful transformation begins with institutional alignment.

From Advocacy to Institutional Integration

The 2016 Hallmarks emphasized the importance of advocating for online learning. At that time, many institutions were still working to establish credibility for online education and to demonstrate that quality learning could occur outside the traditional classroom.

While early digital learning leaders focused on demonstrating the value of online education, today’s leaders face a different responsibility. Today it is critical that integrating digital learning into the institution’s mission, planning, culture, and long-term strategy. The updated Hallmarks describe this evolution as a shift “from advocacy to integration and cultural alignment.” 

This integration focuses institutions on digital learning at the enterprise level whenever discussions center around strategic enrollment, academic portfolio development, learner support, workforce partnerships, financial sustainability, technology investments, or institutional resilience. Rather than operating alongside these conversations, digital learning becomes a significant component that moves the institution forward. 

Why This Matters

Learners expect flexibility, accessibility, and high-quality experiences regardless of instructional modality. At the same time, institutions face demographic shifts, increased competition, financial pressures, and rapidly changing workforce needs. The Hallmarks recognize that responding effectively requires more than expanding online offerings; it requires aligning institutional structures, policies, and leadership around a shared vision for highly effective digital learning environments. 

Hallmark 1 emphasizes that internal advocacy is not simply about communicating the value of digital learning. It is about creating durable partnerships across academic affairs, student affairs, enrollment management, information technology, finance, institutional research, and other areas across the institution so that digital learning becomes an integrated institutional strategy rather than a separate initiative. 

Putting Hallmark 1 into Practice

Hallmark 1 identifies several characteristics that distinguish institutions where digital learning has become part of the institutional enterprise.  Among them are:

• Embedding digital learning into the institution’s mission and learner success strategy.
• Developing shared definitions and consistent data standards across the institution.
• Building cross-functional coalitions that support institutional priorities.
• Establishing clear leadership accountability for digital learning strategy and performance.
• Communicating a consistent value proposition for digital learning to both internal and external audiences. 

For many institutions, these represent opportunities for reflection as much as implementation.

For example:

• Is digital learning explicitly referenced in the institution’s strategic plan?
• Do institutional leaders share a common understanding of what constitutes an online learner, an online program, or multimodal learning?
• Are discussions about enrollment, technology, learner success, and academic planning connected through a shared digital learning strategy?

These questions shift the conversation away from organizational charts and toward institutional integration.

Measuring Progress

One of the strengths of the updated Hallmarks is that they move beyond describing effective practice by identifying measurable indicators of institutional progress.

Rather than viewing the Key Performance Indicators as a compliance checklist, institutional leaders can use them as benchmarks to assess organizational maturity and identify opportunities for continuous improvement.

Hallmark 1 encourages institutions to examine evidence such as:

• Inclusion of digital learning goals within institutional strategic plans and accreditation activities.
• Existence of a strategic enrollment management plan that addresses digital learning.
• Common institutional definitions related to digital learning.
• Evidence-based improvements in learner success, completion, faculty satisfaction, and employer outcomes.
• Ongoing compliance with accreditation requirements and state and federal regulations. 

Viewed together, these indicators help institutions move beyond asking whether digital learning exists to asking how effectively it has become embedded within the institution’s mission and operations.

Leadership Reflection

If your institution evaluated itself using only Hallmark 1’s Key Performance Indicators, what evidence would demonstrate that digital learning has become part of your institutional strategy rather than a separate initiative?

The answer to that question may identify your institution’s next leadership conversation.

The Hallmarks describe what excellent digital learning enterprises look like. Leadership determines whether institutions become excellent digital learning environments.

Continue the Conversation …

To explore Hallmark 1 in greater depth:

• Download the UPCEA Hallmarks of Excellence in Digital Learning Enterprises.
• Review the Key Performance Indicators for Hallmark 1: Internal Advocacy and Leadership Within the Institution.
• Register for the upcoming Hallmarks webinar series:
  • August 12 at 1:00 PM Eastern: Internal Advocacy & Entrepreneurial Initiatives
  • August 26 at 1:00 PM Eastern: Instructional Support & Learner Support
  • September 9 at 1:00 PM Eastern: External Advocacy & Professionalism
  • September 22 at 1:00 PM Eastern: Technology & Digital Innovation

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, and is a Contributing Editor for the Hallmarks of Excellence in Digital Learning Enterprises.

When we reflected on the first three weeks of the UPCEA AI Study Groups in early June, one theme had already become clear: AI implementation is not simply a technology challenge. It is a leadership challenge. 

By the end of the six-week pilot, that theme had deepened. Across two cohorts of UPCEA members, participants moved from broad discussion about AI experimentation, governance, and institutional readiness, into more focused conversations about use cases, roadmaps, change management, student support, faculty development, and the practical work required to move AI from possibility to responsible implementation. 

The Study Groups were designed to help members build community and engagement around AI at their institutions. They also served as a listening and learning space for UPCEA as members continue to navigate fast-moving questions about AI strategy, policy, operations, teaching and learning, and workforce readiness. 

The final weeks reinforced a core insight: institutions do not need to have every answer before they begin. But they do need intentional structures, realistic expectations, and communities of practice that help them learn as they go. 

AI Implementation Is Moving from Curiosity to Infrastructure 

The early weeks of the Study Groups focused heavily on where institutions are in their AI journeys. Some participants described early-stage experimentation, while others shared more mature approaches involving AI councils, task forces, enterprise tools, faculty development programs, AI literacy initiatives, and operational pilots. 

By the final weeks, the conversation had shifted from “What are institutions doing?” to “How do we make this work responsibly and sustainably?” 

Participants discussed AI as part of institutional infrastructure, not just as a set of tools. Successful implementation requires attention to data systems, privacy, procurement, cybersecurity, workflow mapping, staff capacity, faculty engagement, student communication, and governance. Several participants noted that the most difficult barriers are not always technical. They are often cultural, organizational, and procedural. 

This matters especially for online and professional continuing education units, which often operate at the intersection of academic innovation, adult learner support, workforce alignment, enrollment strategy, and operational agility. For these units, AI is not an abstract future issue. It is already shaping advising, marketing, course design, student communication, curriculum planning, employer engagement, and administrative workflows. 

Practical Use Cases Helped Move the Conversation Forward 

The second half of the Study Groups featured practical institutional examples that helped participants think more concretely about implementation. 

One student support pilot demonstrated how AI can help staff consolidate data from multiple systems, identify students who may need timely outreach, and personalize communications at scale. The pilot significantly increased proactive outreach while reducing the time needed to prepare individual student communications. Just as importantly, the example surfaced the implementation realities behind the results: data integration, workflow mapping, system testing, staff trust, transparency, vendor partnership, and return-on-investment conversations with institutional leadership. 

Another institutional example focused on faculty development and teaching and learning. Participants explored how AI can be introduced through workshops, faculty cohorts, mini-grants, instructional design support, AI literacy modules, and learning management system tools. The discussion also addressed persistent concerns about academic integrity, privacy, regular and substantive interaction, student comfort levels, and the importance of ensuring that AI amplifies rather than replaces human teaching and support. 

Together, these examples helped participants see that promising AI work does not begin with sweeping transformation. It often begins with a specific problem, a defined audience, a manageable pilot, and enough structure to learn from the effort. 

“Crawl, Walk, Run” Remains a Useful Discipline 

One of the most resonant frameworks across the Study Groups was the “crawl, walk, run” approach. Participants repeatedly returned to the importance of starting with a contained use case, building trust, demonstrating value, and scaling only after the institution understands what is working and what is not. 

The distinction between what is possible, plausible, and probable also helped participants frame conversations for different audiences. Senior leaders may need to understand the broad possibilities of AI, but end users often need clarity about what is realistic, safe, and immediately useful. A project that sounds simple in theory may require significant process documentation, data review, testing, training, and communication before it can be implemented responsibly. 

This was especially evident in conversations about AI agents. Participants were interested in agents for advising, financial aid communication, reporting, student outreach, and internal process support. But the Study Groups also highlighted important questions: Who is accountable for an agent’s output? How will students know when AI is being used? What policies govern the use of real images, voices, or institutional branding? What data can be used safely? What human oversight is required? 

The lesson was not to avoid these tools. It was to implement them with discipline. 

Governance Needs to Enable, Not Paralyze 

Governance was a recurring topic throughout all six weeks. Participants described a range of institutional approaches, including AI committees, executive councils, task forces, working groups, acceptable use policies, data governance structures, procurement reviews, and faculty-led guidance. 

Yet many institutions are still operating in a decentralized environment. Some participants noted unclear directives, uneven communication, leadership transitions, cybersecurity concerns, and inconsistent faculty messaging to students. Others described progress through cross-functional groups, grassroots communities of practice, and unit-level leadership even when institution-wide policy was still emerging. 

A key takeaway is that governance should not become a reason for inaction. Institutions need guardrails, but they also need ways to learn. The most promising approaches appear to combine broad principles with practical pathways for pilots, review, training, and iteration. 

Participants also emphasized that AI strategy should not be disconnected from digital strategy. Institutions need to understand how AI fits into broader technology, data, learner success, workforce, and academic innovation goals. In the strongest examples, AI was not treated as a side project. It was connected to mission, strategy, and institutional capacity. 

AI Literacy Is Becoming a Shared Responsibility 

The Study Groups made clear that AI literacy is no longer relevant only to technologists, faculty innovators, or instructional designers. It is becoming a shared responsibility across the institution. 

Participants identified many roles affected by AI, including faculty, students, instructional designers, advisors, IT staff, administrative assistants, marketing and enrollment professionals, financial aid staff, curriculum leaders, and senior administrators. Each group needs different kinds of support. 

For some, AI literacy begins with basic understanding: what AI can and cannot do, how to prompt effectively, how to recognize bias or inaccuracy, and how to use approved tools safely. For others, the need is moving toward AI fluency: understanding how to move across tools, apply AI in context, evaluate outputs, design workflows, and use AI as a “power tool” while maintaining human judgment. 

This distinction between literacy and fluency became particularly important in conversations about workforce preparation and professional education. Students need guidance not only about academic integrity, but also about how AI is changing their fields, their future work, and the expectations of employers. Institutions need to help learners use AI ethically, critically, and effectively. 

The Human Side Is Still the Hardest Part 

Across both cohorts, the most consistent implementation challenge was not tool selection. It was people. 

Participants discussed faculty fatigue, student anxiety, staff concerns about job displacement, uneven access to tools, lack of time, inconsistent policies, and the challenge of building buy-in across departments. They also shared the importance of framing AI as a way to enhance human work rather than erase it. 

This framing matters. In the most compelling examples, AI was used to help humans do more meaningful work: advisors could spend less time compiling information and more time supporting students; faculty could experiment with new forms of engagement; staff could reduce repetitive tasks and focus on judgment, strategy, and relationship-building. 

But that outcome is not automatic. It requires intentional communication, training, transparency, and trust-building. Institutions must acknowledge fear and uncertainty while also creating space for experimentation and learning. 

Peer Learning Was the Greatest Value 

The participant feedback reinforced the value of community. In the post-pilot survey, 80% of respondents rated the AI Study Group as valuable or very valuable. When asked what was most valuable, participants repeatedly pointed to learning from peers, hearing how other institutions are approaching AI, seeing examples of use cases, and understanding that others are wrestling with similar questions. 

Respondents also reported a meaningful increase in confidence. Before the Study Group, fewer than half of respondents rated their confidence as somewhat high or very high in feeling equipped to advance AI work or build a plan. After the Study Group, 90% rated their confidence as somewhat high or very high. 

The survey also showed that participants left with forward momentum. Forty-five percent reported having a specific AI workflow or use case to implement, while 55% said they had a direction and next steps that needed further refinement. In other words, every respondent left with either a concrete implementation idea or a clearer path forward. 

The UPCEA AI Hub as a Continuing Resource 

The AI Study Groups are part of UPCEA’s broader effort to support responsible, practical AI adoption in online and professional continuing education. The UPCEA AI Hub serves as a central resource for members seeking guidance, tools, research, use cases, and peer learning related to AI implementation. 

The next release of the AI Hub later this month will add more resources and tools to support member institutions. The release will include new and reorganized content aligned around foundational elements, applied areas, and transformation, as well as institutional case studies that highlight practical examples of AI implementation. Members will also see additional resources connected to AI readiness, maturity, governance, use cases, and operational planning. 

The Study Groups helped surface what members need most: examples they can learn from, frameworks they can adapt, tools they can use with colleagues, and opportunities to connect with peers who are asking similar questions. 

Final Takeaways 

After six weeks, several final insights stand out: 

• AI implementation must be mission-aligned. The goal is not to adopt AI for its own sake, but to improve learning, support students, strengthen operations, and prepare institutions and learners for a changing world. 

• Institutions should start with real problems. The strongest use cases begin with a clear challenge, defined users, and a manageable scope. 

• Governance should support responsible action. Institutions need policies and guardrails, but they also need room to pilot, evaluate, and iterate. 

• AI literacy must be broad and role specific. Faculty, staff, students, and leaders all need support, but not the same support. 

• Human trust is essential. AI adoption depends on transparency, communication, training, and a clear message that AI should enhance human work and judgment. 

• Peer learning accelerates progress. Members benefit from hearing what others are trying, what barriers they face, and what lessons they have learned. 

The Study Groups confirmed that UPCEA members are not waiting passively for AI to reshape higher education. They are asking hard questions, testing new approaches, building frameworks, and looking for ways to lead responsibly. 

The work ahead will require continued experimentation, shared learning, and practical tools. Through the AI Hub, CORe, future Study Groups, and member-contributed case studies, UPCEA will continue helping institutions move from AI exploration to action—grounded in the needs of adult learners, professional education, workforce alignment, and the future of online and professional continuing education. 

Thank you to the guest speakers, Eric McGee, Strategic Consultant for Membership Growth and AI Engagement with UPCEA, Jessica Salata, Director, Student Success for Arizona Online, Caleb Simmons, Vice Provost for Arizona Online, Heather Breittholz, Lead Instructional Developer at UConn eCampus, and Desmond McCaffrey, Interim Director of the Center for Excellence in Teaching and Learning (CETL) at the University of Connecticut for sharing their insights and expertise. And thank you to all of the participants for their active engagement in the study group throughout the six weeks. 

The AI Study Groups were facilitated by Vickie Cook, Senior Fellow and Strategic Advisor for UPCEA, with support from Stacy Chiaramonte, Senior Vice President Operations and Strategy for Research and Consulting, Mel Edwards, Director of Membership and Business Development, and Kathleen Ives, Chief Business Development Officer and Senior Vice President of Member Engagement.  

Content for this resource was refined with the assistance of AI. All text has been thoroughly reviewed, edited, and approved by UPCEA staff with subject matter expertise. References and links have been verified for accuracy and reliability. 

By Derek Bruff

The New Assessment Challenge in Online Learning

Generative AI is complicating how we teach working adults in online and professional continuing education. I recently asked a group of educators who teach online about their biggest concerns regarding AI and assessment of student learning. They had many! These educators were worried about: 

• enforcing academic integrity when AI use by students is hard to detect,
• treating students fairly when some are using AI and some are not,
• trusting students to do their “own best work,”
• students failing to develop critical thinking skills because of AI overuse,
• and helping students to use AI effectively and ethically.
  

Do these concerns resonate with you? Have you heard them expressed by colleagues who teach online?

Students, Shortcuts, and the Conditions We Create

There are no quick fixes for the disruptions that generative AI has caused in online education, but faculty and instructional technologists do have agency in how they respond to these disruptions. As James Lang pointed out in his 2013 book Cheating Lessons: Learning from Academic Dishonesty, most students who turn to various forms of unauthorized aid do so because of environmental factors, not a lack of morals. When there’s an emphasis on performance instead of mastery, when there are high stakes riding on an assessment outcome, when students have extrinsic motivations for and low expectations of success, that’s when they’re most likely to lean too much on AI or other shortcuts.

Designing for Mastery, Not Just Performance

The good news is that we can design against these factors!

• We can be more transparent about the purposes of our courses and assignments, so students see more value in the knowledge and skills they’ll gain. 
• We can design more authentic assessments, drawn from real-world professional contexts that help students develop durable skills like communication and collaboration. 
• We can lower the stakes in our courses through alternative grading practices that prioritize feedback over grades and progress over perfection.
• And even in online courses we can create more embodied experiences for students, like this clever approach to teaching calculus online. 

From AI Anxiety to AI-Aware Teaching

None of these strategies alone will “solve” the problem of AI and assessment in online courses, but together they create conditions that motivate students to do the hard work of learning. We need students to engage in robust assignments in our online courses—the assignments that provide students opportunities for deep learning and provide us with meaningful ways to assess that learning. We can’t let AI take these assignments away from us! And we don’t have to, if we can be AI-aware in the design of these assignments. 

Practical Strategies for More Trustworthy Assessment

What does that look like in practice? My co-authors Annette Vee and Marc Watkins and I share concrete examples in our new book, The Norton Guide to AI-Aware Teaching, published this summer with W.W. Norton. While not written exclusively for online educators, the book offers practical strategies for instructors who teach online to respond to the challenges and opportunities that generative AI presents in their teaching. We argue that being “AI-aware” means:

• being clear on course learning goals and objectives,
• understanding something about how AI works, and
• exploring what students think about AI and how they’re using AI tools.

When we’ve done that, we’re in an excellent position to decide how to adapt our assignments and assessments to work in a world where students have ready access to AI. 

The book is full of examples of AI-aware instructional practices, illustrating the “carrot” approaches I mentioned above but also the “sticks” of more secure online assessments—providing students clear boundary lines for their AI use, implementing transparency measures like process tracking and oral exams, using AI detectors thoughtfully and appropriately, and building any interventions with students on a foundation of trust. 

We cover a lot of ground in the book, and no matter what approach an instructor takes to AI, from resistance or restriction to exploration or engagement, I think they’ll find examples of AI-aware guidelines and assignments they can easily adapt to their particular teaching context.

Keeping Opportunity Open for Adult Learners

Online education has opened up so many doors of opportunity for students in online and professional continuing education programs. We don’t want to close those doors because of generative AI. Keeping them open, however, will require changes to what and how we teach. We can learn from and with each other as we figure out those changes together, and I hope the new book will be a part of that collaborative work. Find out how to order the Norton Guide to AI-Aware Teaching here.

Derek Bruff is an educator, author, and higher education consultant. He is currently associate director at the Center for Teaching Excellence at the University of Virginia and host of the UPCEA-sponsored podcast, Intentional Teaching, as well as a Strategic Advisor for UPCEA Research and Consulting. To learn more about UPCEA Research and Consulting, please contact [email protected].

By Dave Jarrat

The 2026 State of Continuing Education report makes one thing unmistakably clear: UPCEA member institutions know where they want to go. What they need is help getting there.

The findings from this year’s study read less like a progress report and more like a stress test. Microcredential adoption has hit an all-time high (88%). Workforce Pell looms large. Adult learner enrollment continues ticking upward. Institutions are energized and ambitious.  They are also increasingly strained.

Sixty-two percent of respondents say their unit lacks the staffing to execute on institutional goals. Marketing support gaps nearly doubled year-over-year, from 14% to 30%. Fewer than one in three institutions report seamless technology integration between their continuing education systems and main campus. Administrative burden and market uncertainty outrank faculty resistance as the top barriers to expansion, a striking reversal from earlier years that signals the bottlenecks have shifted from academic to operational.

This all points to significant opportunity for UPCEA corporate partners to support institutional members.

UPCEA institutions are not short on vision. They are short on infrastructure, staff, data visibility, and time. The partners who will thrive in this environment are the ones who show up as genuine problem-solvers, willing to meet institutions where they are and help them move forward.

Here are five suggestions on where to start.

1. Lead With the Data Gap, Not the Product Demo

The report reveals a quiet crisis in institutional data confidence. Only 39% of respondents agree it is easy to access real-time enrollment data.  While slightly above the average of the last 5 years, this still means that most institutions are partially flying blind.

Meanwhile, demand for dashboards and reporting tools jumped from 52% in 2025 to 69% in 2026, the sharpest increase from last year of any capability tracked in the survey.

Institutions are not asking for more data. They are asking for data they can actually use.  They need data that is visible in real time, accessible across units, and connected to the outcomes that matter. Workforce Pell will sharpen this need dramatically, with 65% of respondents citing the ability to connect learner data to state longitudinal systems as their top implementation concern.

What this means for you: If your solution touches data in any form, lead with what your platform makes visible, not just what it captures. Show institutional leaders specifically how your tools reduce the number of steps between a question (“Are our microcredential completers finding jobs?”) and a reliable answer. 

2. Make Workforce Pell Readiness Part of Your Value Proposition

Workforce Pell is the most significant new policy development in the 2026 report, and institutions are split. Sixty-seven percent say they are at least somewhat likely to expand programs in response to it. Forty-two percent say they are not ready to meet the associated reporting requirements.

This is a rare moment where institutional need and partner opportunity are almost perfectly aligned. The compliance and data infrastructure that Workforce Pell demands (credential outcome tracking, program CIP code identification, connection to state longitudinal data systems, external credential attainment capture) is exactly the kind of unglamorous but mission-critical work that UPCEA corporate partners are well positioned to deploy.

What this means for you: Do not wait for your institutional contacts to bring up Workforce Pell. Bring it to them, with a clear point of view of how best to capture the opportunity. Audit your platform or service against the four implementation challenges identified in the report (data connectivity, eligibility determination, learner data capture, credential attainment tracking) and develop an honest map of where you add value and where you do not. Then help your contacts build the internal case for investment. 

3. Help Fill the Marketing and Instructional Design Staffing Void

The 2026 report documents something that many institutional leaders will privately confirm but rarely say out loud: they don’t have the right skill sets in house. The marketing support gap widened from 14% to 30% in a single year. Instructional design vacancy reports nearly tripled, from 9% to 22%.

These gaps are not primarily about compensation or hiring pipelines. They reflect the reality that PCE units are being asked to do more programs, more modalities, more employer partnerships, and more short-form credentials without a proportional increase in the people who make those things work.

What this means for you: If your company offers services, this finding should be a central theme in your pitch. Use it to reframe the conversation from cost to capacity. You are not an expense; you are a substitute for headcount that institutions cannot hire fast enough. Make the case in terms that institutional leaders can take to a provost: “We free up your team to focus on strategy while we handle execution.”

4. Reduce Time-to-Market, Not Just Time-to-Launch

Fifty-six percent of 2026 respondents cite time-to-market as a significant barrier to expanding credentials. Governance bottlenecks, approval processes, and cross-unit coordination are the culprits. This is where institutions consistently lose ground to nimble private providers.

This is also an area where well-designed partner solutions can create genuine competitive advantage. For example, a registration system that cuts three weeks out of program setup is not a feature, it is a strategic answer to one of the most persistent frustrations among UPCEA member institutions.

What this means for you: Identify relevant points in the program development and launch process where time is lost and your solutions can help.  Build your demonstration around those moments. If your platform enables non-credit certificate setup in hours rather than weeks, show that. If your content catalog lets institutions launch a new workforce program without starting from scratch, lead with the timeline. The key is making the time-to-market narrative explicit, not assumed.

5. Become a Collaborator in Cross-Campus Coordination, Not Just a Vendor to One Unit

One of the more subtle but significant findings in this year’s report is the decline in perceived cross-campus collaboration. Agreement that other units collaborate with online and PCE on continuing education development dropped from 71% in 2025 to 62% in 2026. Only 13% say their continuing education offerings are well integrated into the broader institutional portfolio. Siloing remains stubbornly at 41%.

Institutions are not just struggling to build programs. They are struggling to get the right people in the same room, or at least the same workflow. This creates an unusual opportunity for corporate partners who are willing to operate beyond their traditional lane.

What this means for you: Think about who else at the institution should be in the conversation you are already having. The partners who distinguish themselves in 2026 will be the ones who help institutions see across their own silos, not by overstepping, but by asking the connective questions no one else is asking.

The Bottom Line

The 2026 State of Continuing Education report is, at its core, a document about institutional will running ahead of institutional capacity. UPCEA member institutions have made a decisive commitment to workforce-aligned, short-form, outcomes-focused learning. They have the leadership buy-in (79% report strong senior support), the learner demand (adult enrollment up 29% since 2022), and in many cases the strategic clarity. 

What they lack are the systems, staff, and operational structures to execute at scale.

That is not a gap that institutions can close alone. The UPCEA corporate partner community exists precisely to help institutions do things they cannot do as effectively on their own. The question is not whether you have a role to play. The question is whether you are playing it in a way that meets this particular moment.

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. 

Content for this resource was developed with the assistance of AI. All text has been thoroughly reviewed, edited, and approved by UPCEA staff with subject matter expertise. References and links have been verified for accuracy and reliability.