By Vickie S. Cook, Ph.D.

Technology may be the most visible element of a digital learning enterprise. It is also one of the easiest to misunderstand. Institutions invest considerable resources in learning management systems, student information systems, customer relationship management platforms, analytics, communication tools, instructional technologies, cybersecurity, and an expanding array of emerging technologies. Each promises opportunities to improve some aspect of institutional operations, teaching, learning, or learner support. Many of you may find that this thought reflects your own experiences; the presence of sophisticated technology does not, by itself, create an excellent digital learning enterprise.

The Technology Hallmark within the updated UPCEA Hallmarks of Excellence in Digital Learning Enterprises intentionally takes a broader view. Technology is not simply infrastructure to be purchased and maintained. It is part of an institutional ecosystem that must connect strategy, people, processes, data, teaching, learning, and learner support. Institutions will continue to encounter new technologies. The pace of change is unlikely to slow and quite likely to increase. The more important question is whether institutions have developed the capacity to make thoughtful choices about which technologies advance their mission and how those technologies work together.

Technology with Institutional Purpose

Higher education has never lacked technology solutions. What institutions sometimes lack is a shared understanding of the problem they are trying to solve. A CIO I worked with for many years always asked, “What problem are you trying to solve?” before considering a technology purchase. A new platform may offer capabilities that appear valuable. An existing vendor may introduce new functionality. Faculty or staff may discover a tool that improves an individual process. Emerging technologies, including artificial intelligence, continually introduce possibilities that institutions could not have anticipated even a few years earlier.

The Technology Hallmark asks institutions to look beyond individual tools and consider the larger digital ecosystem. Systems must be reliable and secure. Data must move appropriately between platforms. Accessibility must be considered from the beginning rather than addressed after implementation. Learners and instructors need timely technical support and appropriate training. Technology investments also need to be financially and environmentally sustainable. 

Academic affairs may focus on teaching and learning. Information technology may emphasize infrastructure, security, integration, and support. Procurement and legal counsel bring additional considerations. Enrollment management, learner support, institutional research, finance, and other units interact with many of the same systems and data. The challenge is not simply coordinating those interests. It is ensuring that technology decisions ultimately serve institutional purpose across campus units and divisions.

Building an Ecosystem Rather Than a Collection of Tools

Over time, institutions can accumulate technology one decision at a time. Each decision may have been reasonable when it was made. Collectively, however, the result can become a complicated digital environment with overlapping functionality, disconnected data, uneven adoption, multiple vendor relationships, and systems that do not always work together as effectively as intended. Sometimes, institutions purchase the same types of solutions from competing vendors that increase the overall cost of a product and decrease integration of data.

Students, faculty, and staff experience the consequences. They may move between systems that require different logins or repeat information the institution already possesses. Faculty may have access to tools they do not fully understand or rarely use and may not even be aware of other technologies already available on their campus. Staff may create workarounds because systems do not communicate effectively. Institutions may continue paying for capabilities that duplicate other investments.

A mature digital learning enterprise periodically steps back from individual technologies and examines the ecosystem as a whole. How well do our systems work together? Where are learners or instructors encountering unnecessary barriers? What technology are we paying for but not fully using? Where does information fail to move when it should? Are our technology investments improving the experiences and outcomes they were intended to support? Are newer, more cost-effective solutions available? Those questions move the conversation from technology acquisition to technology stewardship.

Putting the Hallmark into Practice

The Technology Hallmark reflects the complexity of managing a digital environment that must be dependable today while remaining adaptable for tomorrow. That requires institutional governance. It also requires a willingness to continuously review assumptions about what the institution needs. Interoperability and security are examples of areas that should be reviewed regularly. The Hallmark emphasizes the importance of connecting systems and using data standards so that LMS, SIS, CRM, analytics, and other platforms can contribute to a more complete understanding of learners while maintaining appropriate security and privacy. Increasingly, institutional data must also interact with a larger learning and employment ecosystem beyond the university itself.

Accessibility provides another example. It cannot be treated as a final compliance review after a technology has already been selected. The Hallmark positions accessibility as a consideration within procurement and contract renewal, alongside security, privacy, functionality, and institutional need. 

The same thinking applies to emerging technologies. Artificial intelligence will certainly influence the digital learning ecosystem, but AI is one example of a much larger institutional challenge: technologies will continue to change. Institutions need governance and decision-making processes capable of evaluating new opportunities without allowing the newest tool to determine institutional direction. The goal of this Hallmark is not to predict every technology that comes next. It is to build an institution capable of responding thoughtfully when it arrives.

Measuring What Matters

The breadth of the Technology Hallmark is particularly visible in its Key Performance Indicators. The measures begin with strategy and institutional responsibility: an enterprise-wide digital technology strategy aligned with mission, current governance documents, ongoing security training and assessment, accessibility audits, technology optimization, and standards for interoperability and integration. 

But the KPIs do not stop with technology governance. They also examine system reliability and performance, user adoption, technical support responsiveness, infrastructure capacity, vendor performance, integration stability, instructor and learner satisfaction, enrollment effectiveness, faculty readiness, course and program development timelines, and the relationship between technology use and learner outcomes. Institutions are also encouraged to map their learning technology environment and identify gaps that affect the experience of students, faculty, and staff.  An institution could have excellent system uptime and still have a technology ecosystem that frustrates learners. It could purchase sophisticated tools that faculty and staff rarely use. Systems might operate exactly as designed while failing to share the information necessary to support timely learner interventions.

The KPIs invite institutions to consider technology performance from several perspectives at once: Is it reliable? Is it secure? Is it accessible? Is it being used? Does it integrate? Is it sustainable? And, ultimately, is it helping the institution achieve the outcomes for which the technology was acquired?  This final question may be the most important consideration for future technology planning and institutional decision-making.

Leadership Reflection

If your institution stopped evaluating technology by the capabilities of individual systems and instead examined the digital experience as a whole, what would you discover? Which technologies clearly advance institutional purpose and learner success, and where has the technology itself become the focus rather than the outcome it was intended to support?

Technology will continue to change. Platforms will be replaced, new capabilities will emerge, and institutions will face opportunities that were not anticipated when these Hallmarks were written. The enduring responsibility is not to choose every technology correctly. It is to create the institutional capacity to evaluate, integrate, support, and continually improve technology in service to mission and learners.

Bringing the Series Together

Across this series, we have considered four different dimensions of the Hallmarks of Excellence in Digital Learning Enterprises: integrating digital learning into institutional strategy, intentionally designing the conditions for learner success, extending institutional impact through external engagement, and building technology ecosystems that support institutional purpose.

Each Hallmark addresses a different part of the digital learning enterprise. Yet a common idea runs through all of them. Excellence is intentional.

It is reflected in how institutions align people and priorities, how they design experiences that support learners, how they listen and contribute beyond their institutional boundaries, and how they make choices about technologies that will continue to change.

Each Hallmark addresses a different part of the digital learning enterprise. Excellence is attainable. The Hallmarks provide benchmarks institutions can use to examine that work. They also provide a starting point for deeper conversations about what excellence means within different institutional missions, contexts, and communities.

Our final Hallmarks webinar will provide an opportunity to hear directly from the authors who wrestled with the complexity of the Technology Hallmark and how institutions can prepare for a digital environment that will inevitably continue to evolve. While this concludes our four-part blog series, the conversations surrounding the Hallmarks and what excellence means for digital learning enterprises will continue.

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

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.

By Bruce Etter

During arms-control talks in the 1980s Ronald Reagan made “trust, but verify” one of the most quoted phrases of the era. Four decades later, prospective online and continuing education students appear to have quietly borrowed it back and pointed it directly at their search bar. Trust, but click.

That is the story of AI Search in Higher Education: How Prospects Search in 2026, conducted in partnership between UPCEA and Search Influence. The headlines sound contradictory: prospects are using AI search more than ever, trusting it more than ever, and questioning it more than ever, all at the same time. Look closer and the contradiction disappears. A more sophisticated AI consumer is emerging in the online and professional continuing education market. Prospects are happy to accept AI’s opening argument. They just insist on seeing the evidence before they render a verdict.

Start with the use side of the ledger. Daily use of AI-powered search tools jumped 15 percentage points in a single year, from 24% in 2025 to 39% in 2026. The share of prospects who never use AI search fell from 17% to 12%. Fifty-eight percent now treat AI tools the way they would treat a traditional search engine, up from 50% a year ago. Adoption is no longer the question. The tools have moved into the routine.

Trust is the more interesting story, and it is moving in the same direction. Eighty-three percent of prospects read AI-generated overviews when they appear inside a Google or Bing result, up slightly from 79%. Among those readers, 64% say that being cited inside an overview makes them trust the website more, up from 56%. Fifty-seven percent of prospects report that their trust in AI Overviews has increased in the past year; only 12% say it has declined. Use and trust are not the same thing, but in 2026 they are pulling in the same direction.

That trust curve should get the attention of any institution trying to reach the modern learner. AI did not just move into the search stack. It moved in, took off its coat, and got comfortable. Higher education understands the language of citation better than most industries, and this is a version of it: being named as a source inside an AI Overview functions much the way a peer citation functions in a piece of scholarship. It confers perceived standing. And a growing majority of prospective students respond to it accordingly.

Even as trust has risen, so has scrutiny, though not quite in the way the headline number suggests. In 2025, roughly a third of prospects (32%) reported no concerns whatsoever about AI search tools. In 2026, that share collapsed to 14%. The rise in “at least one concern” from 68% to 86% is not because any single fear went vertical; the top-cited concerns, validity and accuracy, actually ticked down slightly year over year. What changed is that fewer prospects are willing to say the technology raises no concerns at all. In parallel, verification became near-universal: 59% of overview readers click through to cited sources, and only 1% say they never do.

Read those numbers together and a very clear behavior emerges. Prospects are embracing AI and wisely not blindly accepting it as gospel either. They are doing what smart researchers do, just at a different scale: read the summary, then check the sources. AI provides the abstract. The linked website provides the evidence. The trust and the skepticism are not in conflict. They are two halves of the same verification loop.

The consequence for PCE marketing is that your program page now serves two visitors at nearly the same moment: an AI system deciding whether the page is authoritative enough to cite, and a prospective student who read the overview, saw your name inside it, and clicked through to see whether the AI got it right. Both want substance: concrete, verifiable detail on what the program is, who it is for, how long it takes, how much it costs, and what graduates go on to do. And it is worth remembering that your program page is not the only surface AI reads on your behalf. Third-party profiles, alumni listings, review sites, and social platforms all feed the answer AI eventually assembles, and inconsistency across those surfaces can keep otherwise-qualified programs out of the citation.

That consideration set has never been more precious. Fifty-six percent of prospects seriously consider three or fewer PCE programs before deciding where to apply, and 7% consider just one. Among prospects at least somewhat likely to use AI in program research, half (50%) would use it specifically to narrow their consideration set and 35% would let AI pick the best program for them outright. Another 56% of that same AI-open group would use AI to do additional research on programs they are already interested in, meaning AI is joining the deep dive, not replacing it, and 72% are more likely to consider a program that AI features prominently in its results. AI is being invited into the earliest, most consequential moment of the funnel, the moment a prospect goes from “everyone” to “these three.” Miss on either side of the citation, and the door quietly closes before an inquiry is ever made.

The 2026 findings point to a fairly disciplined to-do list. Treat inclusion in AI Overviews the way you already treat organic ranking: earned, engineerable, and now a direct trust and traffic driver. Write program pages worth citing, in plain English, structured for machines, and backed by specific, checkable details. Audit every surface AI reads on your behalf so the story stays consistent across them. Remember that traditional search still starts the journey for 80% of prospects and university websites remain the most trusted source in the mix (74%); AI supplements those behaviors, it does not replace them. And keep an eye on video: 67% of prospects search YouTube the way they would search Google.

Above all, resist the temptation to read “86% have concerns about AI” as a warning that adoption will slow down. It will not. What is happening is that a discerning user base is emerging, one that will keep asking AI to do more of the heavy lifting while also keeping receipts. Institutions that give both the AI system and the prospect something worth trusting will move into that shrinking consideration set. Institutions that do not will find themselves outside a door that was closed by an AI Overview they were never in.

Trust, but click. Prospects have already learned the discipline. Your program pages, and every other surface AI reads on your behalf, need to be worth both halves.

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 follow-up from the 2026 SOLAR COLO Convening

By Julie Uranis & Tim Lehmann

FSA awards more than $120 billion a year to roughly 13 million students. Some of those enrollments are fraudulent. Fraud rings use stolen identities, synthetic identities assembled from real and fabricated information, and sometimes complicit individuals to enroll, obtain aid, and collect student-aid refunds. The U.S. Department of Education says it prevented $1 billion in this kind of fraud in 2025 alone. California’s community colleges reported losing about $1.8 million in federal and state aid to fraudsters in early 2026. Utah’s public higher-education system reported about $834,000 in confirmed and potential losses from fraudulent applicants from 2025 to 2026. The Financial Crimes Enforcement Network issued a formal alert instructing banks to flag the pattern: new accounts funded solely by student-aid refunds, multiple unrelated students directing refunds to the same account, and funds rapidly moving to common recipients. These banking system flags may never be communicated to institutions.

The U.S. House of Representatives responded this year by passing the No Aid for Ghost Students Act, which would codify new fraud-detection requirements for the Department of Education. As of publication on September 10, 2026, the bill is pending in the Senate. Legislation is a necessary step at the federal level. It is not sufficient at the institutional level, as we both believe early substantive academic engagement is an underused layer in an institution’s financial aid strategy

When Just Enough Isn’t Enough

Here is the pattern, drawn from cases inside SNHU’s own fraud response: a synthetic application goes in roughly a month before a term starts. In the first days of the course there may be a  “pulse check” which can be a login or a click on the syllabus. It is just enough activity to seemingly indicate presence in a course, even if it does not necessarily substantiate engagement from a Title IV perspective. Then, depending on the design of the course, nothing meaningful happens until the financial aid disbursement date. This is the point at which the identity vanishes for good. Traditional admissions and identity checks don’t catch this, because the fraud isn’t built to beat those checks — it’s built to survive them long enough to look like a normal, if quiet, student until aid disburses, a matter of weeks.

That is a narrow window, and it’s a window institutions control since both course requirements and aid disbursal can vary. A course that expects nothing more than a login and a syllabus click in its first couple of weeks is also a course a fraud ring can exploit without much effort. A course that requires an individualized, substantive response from every student in that same window — a discussion post that engages the actual reading, feedback that requires the student to act on it, an assignment a chatbot can’t template — forces actions most fraud rings won’t make. Either the fraudster invests real, sustained effort to keep the identity alive, which erodes the economics of running the scheme at scale, or the identity goes quiet early, offering an opportunity to identify the fraudster earlier and hopefully before aid has been disbursed.

This is different from asking faculty to police their classrooms for criminals. The signals that separate a synthetic identity from a genuinely struggling student are not the same signals, and treating them as identical risks turning a compliance problem into a wave of false accusations against legitimate learners. That distinction matters because even federal fraud guidance cautions against treating any single behavior as proof. FinCEN’s July 2026 alert identifies patterns such as newly established accounts funded solely by student-aid refunds, multiple unrelated students directing refunds to the same account, and funds being rapidly transferred after refunds arrive. But FinCEN is explicit that no single red flag is determinative.

The same principle should guide institutions: an unusual login pattern, generic discussion response, or missed assignment may warrant attention, but none establishes fraud on its own. The useful signal is the pattern across multiple indicators and systems. A real student’s engagement is often hesitant, uneven, and imperfect. We have experience with those learners, they often apologize for a late post, provide a partial answer, or ask a question that shows they’re behind but trying. A synthetic identity’s pattern looks different: batched application timing, logins from the same IP address as several other “students,” discussion responses that read as generic or template-like regardless of the actual prompt. Faculty don’t need to become fraud investigators. They need syllabi that make early, individualized engagement unavoidable, so that the absence of it becomes visible on its own.

Why this was already good practice

The case for early, sustained instructor presence and learner activities in a course didn’t originate with fraud prevention, and it shouldn’t be sold to faculty as a compliance measure alone. The Community of Inquiry framework, and the research that followed it, has argued for two decades that teaching presence — an instructor who is visibly, consistently active in a course rather than passively grading at the end — is tied to how much students report learning and how satisfied they are with a course. That relationship has held up across a large enough number of studies that a 2020 meta-analysis found it to be one of the more consistent findings in online learning research. None of that literature was written with financial aid fraud in mind. It simply describes what already makes an online course work.

What’s changed is the regulatory stakes attached to the same behavior. New R2T4 regulations effective July 1, 2026, codify longstanding guidance requiring institutions that are required to take attendance to document the institution’s determination that a student withdrew no later than 14 days after the student’s last date of attendance. An institution that can’t produce evidence of engagement — because the course never asked for any until week eight — is exposed on two fronts at once: a compliance finding if regulators look closely, and a fraud loss if the “student” was never real. A course designed around regular, substantive interaction from day one, imbued with the instructor’s presence, can support both fraud mitigation and R2T4 documentation, in some cases using overlapping evidence. Recalibrating courses to be ‘friction-right’, a term coined by Tim during his 2026 presentation to COLOs at the SOLAR COLO convening in July, is an important step.

What institutional leaders should actually do

Three moves belong on a president’s or provost’s desk, not just a financial aid director’s:

First, direct academic leadership to redesign early-course pacing, in every modality, considering the institution’s financial aid calendar. Requiring individualized, substantive interaction, not just access to materials,  well before any disbursement or census date is an important first step. This is a curriculum and instructional design decision, informed by institutional financial aid disbursement information.

Second, train faculty to recognize the behavioral difference between a struggling student and a synthetic one, explicitly, so the response is course redesign rather than suspicion aimed at slower or less confident learners.

Third, connect the data. Learning management system engagement analytics and financial aid’s fraud-monitoring systems are frequently maintained by offices that never compare notes. They’re looking at the same first few weeks of the term from two different systems. Closing that gap is an institutional decision, and it’s overdue.

Fraud rings adapted quickly to open enrollment, minimal early friction, and passive course design, because those are the paths of least resistance. These same fraudsters are far less efficient against an instructor and course that asks something real of every student before aid disburses. That’s not a new argument for good teaching. It’s an old one, with a new reason for leaders to act on it.

Julie Uranis, Ph.D., Senior Vice President for Online and Strategic Initiatives at UPCEA, leads UPCEA’s work in supporting digital learning professionals. She built her career across professional and continuing education, workforce-aligned and alternative credentials, competency-based education, and adult learner pathways, with earlier roles inclusive of teaching, distance-education administration, and state higher education policy at Eastern Michigan University and Western Kentucky University.

Timothy Lehmann is Vice President of Student Financial Services at Southern New Hampshire University, where he leads financial aid, fraud mitigation, and compliance with a student-first lens.

Sources

Caskurlu, S., Maeda, Y., Richardson, J. C., & Lv, J. (2020). A meta-analysis addressing the relationship between teaching presence and students’ satisfaction and learning. Computers & Education, 157, 103966. https://doi.org/10.1016/j.compedu.2020.103966

Federal Student Aid. (2026, March 27). Implementation of Return of Title IV Funds (R2T4) regulations effective July 1, 2026 [Electronic announcement]. U.S. Department of Education. https://fsapartners.ed.gov/knowledge-center/library/electronic-announcements/2026-03-27/implementation-return-title-iv-funds-r2t4-regulations-effective-july-1-2026

Financial Crimes Enforcement Network. (2026, July 24). FinCEN alert: Fraud schemes targeting federal student aid (FIN-2026-Alert004). U.S. Department of the Treasury.

https://www.fincen.gov/system/files/2026-07/FinCEN-Alert-Fraud-Schemes-Targeting-Federal-Student-Aid.pdf

Alonso, J. (2026, June 22). What Is FAFSA Fraud, and Why Is Congress Working to Stamp It Out?. Inside Higher Ed. https://www.insidehighered.com/news/students/financial-aid/2026/06/22/understanding-crackdown-fafsa-fraud

No Aid for Ghost Students Act of 2026, H.R. 7892, 119th Cong. (2026). https://www.congress.gov/bill/119th-congress/house-bill/7892

Pina, A. A., & Martindale, T. (2023). Regular and Substantive Interaction in Online Courses: Why it Matters for Administrators. Online Journal of Distance Learning Administration, 26(2), 1-14. 

Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2–3), 87–105.

Richardson, J. C., & Swan, K. (2003). Examining social presence in online courses in relation to students’ perceived learning and satisfaction. Journal of Asynchronous Learning Networks, 7(1), 68–88.

U.S. Department of Education. (2026, February 12). Federal Student Aid (FSA). https://www.ed.gov/about/ed-offices/fsa 

AI Use Disclosure

This post was developed with the assistance of Claude (Sonnet 5) and ChatGPT (GPT-5.6 Sol). 

The argument, the fraud-detection framework and the underlying professional expertise are Timothy Lehmann’s own, drawn from his presentation at UPCEA’s July 2026 COLO Convening ahead of SOLAR26.

Julie Uranis used Claude and ChatGPT to draft the post, synthesizing Tim’s presentation and presenter notes. Claude conducted independent research to source supporting evidence for Tim’s framework. Claude and ChatGPT were used to verify factual claims. Claude assembled the citation list in APA format.

Both authors reviewed and revised the AI-assisted draft, and take full responsibility for its accuracy, arguments, and final published wording.