This edition of The HELIOS Report takes up an issue readers may be feeling increasing urgency about — the rapid growth and influence of AI in higher education. You no doubt have many dozens of playbooks, handbooks, checklists, case studies, and argumentative articles filling your inbox on the many questions AI raises about ethical use, academic integrity, equity, institutional transformation, and workforce development.

To help readers of The HELIOS Report reset on this expansive topic, we selected a range of recent surveys of multiple stakeholders with an eye toward identifying common themes that establish what conditions currently exist at many institutions. The surveys we discuss below present a sector in motion but not yet in command.

This executive briefing addresses that gap between awareness and coordinated response. Following a discussion of the cross-cutting themes and implications of the surveys we look at, we argue that individual colleges and universities need a productive near-term methodology for acting in conditions of uncertainty. We propose a set of four diagnostic questions that give campuses a disciplined way to move forward and make decisions. Each question works for either broad strategic conversations or isolated tactical decisions without requiring perfect information to be useful.

We hope this report will help you better understand the situation at your institution and identify what information and ways of thinking you need to move conversations forward productively. We expect this to be the first of several HELIOS Reports on AI, especially as higher education experiences the addition of agentic AI on top of generative AI. This edition tries to give leaders a reasonable way to channel the flood of information and questions they are encountering.

We welcome any feedback you have about this report and look forward to hearing if it is useful to you and your colleagues.

- Ilene and Robert

Download and share a PDF version

659KB ∙ PDF file

Download

Download

I. Recent surveys on generative AI in higher ed

Presidents

The 2026 Survey of College and University Presidents from Inside Higher Ed (March 10, 2026) collects 430 responses from U.S. presidents across public, private nonprofit, and for-profit institutions. While the survey is not primarily about AI (it covers financial stability, enrollment trends, political pressures, campus culture, student well-being, and technology), it presents a sector lacking adept responses to the challenges of AI.

Source: 2026 Survey of College and University Presidents, Inside Higher Ed

Faculty

The AI Challenge: How College Faculty Assess the Present and Future of Higher Education in the Age of AI from American Association of Colleges and Universities and Elon University (January 21, 2026) reports on a survey of 1,057 U.S. college faculty to assess how they perceive AI’s impact. Faculty view generative AI as a net risk to student learning and institutional integrity, despite acknowledging limited instructional benefits. The report frames AI as a systemic pressure on teaching, assessment, credentialing, and the definition of learning.

College Faculty Perceptions of Generative Artificial Intelligence in Higher Education, from College Board (February 25, 2026) reports widespread high levels of concern about the effects of AI on critical thinking, academic integrity, and student independence. Most faculty report using AI in their own work, though in limited ways. Policies and instructional approaches vary widely, and most faculty report low confidence in managing AI use.

Source: College Faculty Perceptions of Generative Artificial Intelligence in Higher Education, College Board

Operational administration and staff

AI in Higher Education: From Widespread Adoption to Strategic Integration (March 4, 2026) is the third annual report from the edtech vendor Ellucian. It draws on survey responses across roles including executive leadership, academic affairs, IT, and operations. The report details role-specific adoption, operational use cases, and the need for training, governance, and data infrastructure to support responsible implementation. It describes a sector where AI use among administrators has reached saturation and which is transitioning to institutional adoption and integration. It finds AI use concentrates in chatbots in financial aid, IT, and enrollment functions.

The Impact of AI on Work in Higher Education from EDUCAUSE (January 12, 2026) explores how faculty and staff are using AI and how institutions are responding. It finds that AI adoption is already widespread, largely informal, and outpacing institutional strategy. It documents institutional responses such as upskilling and emerging risks around governance, workload, and ROI.

Source: The Impact of AI on Work in Higher Education, EDUCAUSE

High school students, parents, teachers, and administrators

How Teens Use and View AI from the Pew Research Center (February 24, 2026) reports on a survey of U.S. teenagers examining their awareness, access, and use of generative AI tools. It shows AI use among teens is already widespread, though unevenly distributed, with significant implications for learning behaviors, academic integrity, and digital literacy development.

We also incorporated into our analysis a pair of reports from College Board on surveys administered in 2024 and 2025, including large samples of high school students, parents, AP teachers, principals, and school or district administrators: U.S. High School Students’ Use of Generative Artificial Intelligence: New Evidence from High School Students, Parents, and Educators (October 2025) and Variation in High School Student, Parent, and Teacher Attitudes Toward the Use of Generative Artificial Intelligence (December 11, 2025). Three themes emerge from the reports. First, student use is widespread and increasing, with most students reporting at least occasional use for schoolwork. Second, parents and administrators are broadly supportive of students learning to use AI, while teachers and school leaders express concern about academic integrity, skill development, and preparedness. Third, school and district policies vary substantially in access, rules, and delegation of authority to teachers or departments. The reports describe high school environments where adoption is moving quickly, adult stakeholders recognize both opportunity and risk, and formal policy and instructional practice remain unsettled.

College students

Recent surveys of college students are harder to find at present. How AI Is Changing—Not ‘Killing’—College (August 29, 2025) reports on Inside Higher Ed’s July 2025 Student Voice survey of 1,047 students. It finds AI is widely used and reshaping learning behaviors but it has not reduced the perceived value of college. Students report mixed effects on critical thinking and call for institutional guidance focused on ethical use rather than enforcement. We supplemented our analysis below with two older sources: 1.) Student Research Into How Students and Faculty Use AI published in January 2026 by Every Learner Everywhere (which Robert wrote). It reports on work done by student interns in the 2024-25 academic year to survey their peers and faculty. At that early date AI was normalized for many students, and a key finding is that many students were thinking critically about the influence of AI on their academic development. 2.) Time for Class 2025: Empowering Educators, Engaging Students, Tyton Partners from Tyton Partners (June 11, 2025) surveyed current college students, along with administrators and instructors on a range of issues. Its core finding is that regarding most digital learning in higher ed including AI, policy clarity and data integration lag behind implementation.

II. Cross-survey patterns

1. AI is nearing universal use by high school and college students. They are building their own norms in the absence of institutional norms. They use AI pragmatically to complete tasks. Their AI use is concentrated in research and writing workflows.

2. Faculty engagement is high but individualized and highly varied. They are adapting course by course, not through coordinated academic program planning. Faculty are experiencing and grappling with a disruption to how learning is defined and assessed.

3. Adoption in administrative or operations functions is ahead of academic uses. Operational integration is moving faster than institutional decision making. Staff in advising, enrollment, marketing, and IT report efficiency gains and clearer use cases.

4. Leadership awareness exists but has not translated into coordinated strategy. Ethical and pedagogical norms are being set informally by users, not institutions. Institutions and their leaders describe themselves as “not ready.” However, individual users behave as if systems and procedures for incorporating AI are already in place.

Share The HELIOS Report

III. Interpretations

The institutional condition

AI magnifies existing institutional strengths and weaknesses. This applies to organizational capacity, not institutional size. AI pressures will compound the advantages of strong hiring practices and IT infrastructure. Conversely, AI pressures will compound operational gaps and procedural dysfunction. A small institution with strong culture and clear decision rights can punch above its weight; a larger institution with diffuse authority and weak processes will find AI sharpens fault lines leadership has deferred addressing.

AI adoption is outpacing institutional understanding. The surveys summarized above describe a field with inconsistent norms and organizational response. Large numbers of students, faculty, staff, and leaders are using AI in real academic and operational settings, but shared sense making has not kept pace. Campuses are operating with inconsistent expectations and unresolved questions about what requires protection, what can change, and who decides. That gap challenges decision structures, role definition, and professional identity. Some students and personnel will experience this as deeply destabilizing. This is a social transition as much as a technology transition.

AI adoption is jagged. Different parts of the institution are trying to solve different problems at different speeds. The surveys suggest the uneven pace results less from a lack of awareness and more from a lack of institutional mindset: a shared sense of how to make decisions related to AI that are mission aligned and that account for second- and third-order effects across the organization.

Institutions already have significant new technology risk exposure. Tools are in place that institutions did not choose, train for, or standardize. Few institutions are measuring ROI or improvements in learning. Selecting and implementing tools effectively requires data infrastructure, controls, and staff capacity that are not yet in place. Risk may be greatest in areas overlooked in the dominant conversations, such as financial aid offices that handle sensitive data and make high-stakes decisions.

Teaching and learning

Faculty concerns are broader and deeper than integrity alone. Faculty feel AI exposes how many current instructional and assessment practices reward output rather than learning. It raises theory-of-learning questions as much as classroom management questions. Respondents in the surveys anticipate significant change in how students complete academic work, how faculty evaluate that work, and how institutions signal the value of a degree. Some faculty see this disruption as an opportunity to clarify principles and to sharpen course design and teaching practices. Many feel an urgent requirement to communicate expectations about knowledge, judgment, and responsible use. They are looking to their institutions to provide faculty development for course redesign and to develop critical AI literacy.

Students are not necessarily naive users of AI. The surveys show students recognize the risks of AI to their academic development, and they make active, situational decisions about how to use it. They tend to position AI as support rather than replacement. They describe using AI for drafting, brainstorming, editing, and research support. Many students express concern that AI will become a shortcut that limits their critical thinking or developing their own perspectives and voices. That does not mean, however, that students have stable frameworks for AI use or that their caution reliably shapes their behavior. Under pressure or when expectations are unclear, students default to what helps them efficiently complete graded assignments. Educators have an opportunity to convert student awareness and inquiry into productive academic norms.

Source: How AI Is Changing—Not ‘Killing’—College, Inside Higher Ed

AI will amplify equity gaps. Though many students are experienced with AI, they are not a monolith. The reports above, including those on surveys of both current and prospective college students, show they will arrive in and progress through college with varied AI experiences. Those differences will run along multiple axes: socioeconomic background, access to premium versions of AI tools, disciplinary practices, and prior instruction. Students are developing varying belief systems about authorship, effort, and learning. Assumptions that all students are tech savvy flattens a population whose preparation will differ significantly by background and context. Equity demands that advising, course design, and academic expectations account for a range of experiences and readiness.

In short — Individual actors are not waiting for consensus

AI summons existential conversations about purpose, pedagogy, and institutional identity. Those conversations are necessary, but campuses cannot wait for a philosophical consensus before acting. The work requires parallel tracks: making space for deeper questions while acknowledging that AI will keep imposing its influence without regard to a community’s timeline for discussions. Leaders who hope to settle every foundational question before moving forward will find the environment has moved without them.

IV. Implications by stakeholder group

President and cabinet. Only 55 percent of presidents in the Inside Higher Ed survey say their institution is responding appropriately to AI. The cabinet needs to determine who has authority to set AI standards in academic contexts and operational contexts and where those two domains require coordination before they collide. The four questions in Section V of this report offer a shared orientation that can cross those lines. Cabinet members who adopt them as a common frame give the institution something more durable than a policy: a consistent way to evaluate individual decisions before the policy catches up.

Provost, deans, and academic program design. The AAC&U/Elon University and College Board surveys describe faculty experiencing a disruption to what counts as student work, what assessment measures, and whether current course designs reward output or development. Faculty redesigning courses without institutional support will do so inconsistently. Academic affairs leadership needs a guiding framework to build structured faculty development that is coherent across departments.

Institutional research, analytics, and the registrar. A notable absence in the surveys reviewed above is institutions measuring what AI actually changes in learning outcomes, retention, or academic performance. Data is not informing decisions about AI policy and tool adoption. IR and the registrar’s office have a shared interest in defining what consistent, AI-aware academic standards look like before variation hardens into inconsistency that is difficult to audit.

Advising and career services. The Pew Research Center and College Board surveys show that incoming students will arrive with sharply different AI experiences shaped by prior instruction, family resources, and school policies. Students who used AI extensively in high school with no instructional scaffolding and students who had thoughtful guidance present different challenges for academic integration. Meanwhile, employers are developing their own AI expectations, and students need help understanding which uses of AI in professional settings signal competence. Advising and career services need clear institutional positions to communicate, and they need faculty and academic affairs engaged in developing those positions with them.

Health, counseling, and cultural programming. Students experiencing financial stress, mental health challenges, or identity-related marginalization may reach for AI as a substitute for support they cannot easily access, or they may encounter AI-assisted services that handle their cases without the relational attention those cases require. Health, counseling, and cultural programming staff are often the first to see when institutional processes fail students. They should be included in any campus AI task force, because their vantage point identifies equity risks not visible from elsewhere.

Finance, budget, and IT leadership. The Ellucian report describes AI use in financial aid, enrollment, and IT as already at saturation among administrative staff. CFOs and CTOs need to know whether those tools were selected through any institutional process, whether they involve student data, and what the liability exposure is if performance or privacy problems emerge. AI tools adopted through deliberate institutional process carry implementation and training costs that need to show up in budget planning.

Faculty development. Centers for teaching and learning are positioned to convert individual effort into institutional knowledge if they are clear about what they are trying to accomplish. A center that builds workshops, peer learning communities, and redesign consultations around a limited set of questions like those in Section V gives faculty a common language without demanding consensus on contested pedagogical questions. The goal is shared vocabulary and deliberate reflection across departments that are currently operating in isolation.

“Middle management.” Leaders in department chair, program director, and dean roles are being asked to make AI-related decisions without shared frameworks, consistent information, or organizational cover for uncertainty. Leadership development for this crucial “interpretation” layer of the institution should prioritize four capabilities: holding institutional uncertainty openly; maintaining space for human judgment; adopting an institutional mindset; and gaining the financial, regulatory, and cross-functional literacy to evaluate AI use cases against mission and constraints.

Marketing and communications. The surveys offer something more useful than narratives of alarm about academic integrity: evidence that students, faculty, and leaders all care about learning, that the field has not surrendered its educational commitments, that meaningful questions remain genuinely open, and that creative scholars are engaging students in exciting new intellectual projects presented by AI. Institutions should acknowledge the disruption while articulating principled, mission-grounded frameworks for navigating it.

Workforce and L&D partners. Employer partners and workforce development organizations share the equity exposure the surveys describe. Students exiting workforce programs with habits formed under pressure rather than principle create downstream challenges for employers and L&D professionals who assume a baseline. Partners that co-develop AI literacy expectations with institutions, rather than waiting for graduates to arrive, are better positioned to shape what those expectations look like.

Philanthropic partners. Nonprofits have an opportunity to make field-building investments that develop shared language and cross-functional coordination, grow understanding of the impact of scattered activities, and promote coherent institutional practices. Philanthropic organizations with a sector-wide perspective are also positioned to support institutions and units where AI risks magnifying existing inequities in advising, financial aid, teaching support, and student preparation. Colleges and universities need help building the faculty development, student-support capacity, and data literacy that create conditions for better judgment.

Share

V. The leadership demand: A common frame for an unsettled environment

College and university communities have an opening to consolidate and raise the quality of conversations and activity already underway. The groups represented in these surveys care intensely about learning, integrity, authorship, fairness, and preparation. The argument on campuses remains fundamentally about education, mission, and strategy. However, definitive responses to the questions being raised are not available as quickly as the environment demands. People across the institution are feeling simultaneous pressures to act and to engage in protracted information gathering and planning.

Leaders can respond in the short term by articulating a stable orientation and productive guiding questions like the four below. These questions are designed to function at every level of the institution, from an individual staff member evaluating a new workflow to a cabinet discussing institutional risk to a faculty member redesigning an assignment. They do not resolve the uncertainty, but they give people a disciplined way to move through it and avoid paralysis.

1. How does this use of AI preserve human judgment?

AI can accelerate processes, surface patterns, and generate drafts. Increasingly, it can act on its own toward a stated goal without guidance. The question for any use case is where the humans involved retain the capacity and the responsibility to evaluate and decide. A financial aid office using AI to flag application anomalies preserves judgment if counselors review and act on the flags. It erodes judgment if the system makes determinations that staff simply accept. A student using AI to brainstorm an argument preserves judgment if the student evaluates and develops the ideas. The distinction applies across every function: advising, hiring, assessment, communications, program review, research, teaching, and learning.

2. Does this use of AI respect human processing speed?

AI produces output faster than humans can evaluate it well. That gap is where quality erodes. When AI accelerates output, workers tend to expand their scope, blur work boundaries, and manage more simultaneous tasks, often without realizing the cumulative effect on their judgment. (We are influenced here by AI Doesn’t Reduce Work—It Intensifies It by Aruna Ranganathan and Xingqi Maggie Ye in The Harvard Business Review (February 9, 2026.)) Careful people, working in good faith, can find themselves processing AI output at a pace that doesn’t leave room for the judgment their roles require. A faculty member prompting an AI tool to generate course materials; an advisor using AI to draft outreach to students at risk of stopping out; a financial aid officer using AI for ideas to optimize yield: In each case, the speed of production needs to be matched by the time and cognitive space to evaluate what was produced. Institutions that adopt AI primarily to accelerate work without addressing the processing-speed question are likely to find quality trade-offs showing up later in enrollment, retention, learning outcomes, and morale.

3. How does this use of AI respect human privacy and dignity?

AI applications in higher education touch student records, health information, financial data, and employment decisions. That data needs to be handled securely, but privacy should be paired with dignity. When AI tools profile student behavior to predict attrition or flag risk, institutions should examine whether those interventions treat students as agents or as objects of management. When AI assists in hiring decisions or personnel evaluations, the user needs to ask whether the people affected would recognize the process as fair and respectful if they could see it.

4. How does this use of AI align with the institution’s mission and its understanding of learning?

Every institution holds commitments, sometimes implicit, about what education is for, what counts as knowledge, and what the relationship between teacher and student should look like. AI decisions that proceed without reference to those commitments risk optimizing for efficiency at the expense of purpose. A center for teaching and learning helping faculty redesign courses around AI should ground that work in the institution’s own educational philosophy. An enrollment office adopting AI-driven communications should test whether the resulting messages reflect the institution’s voice and values. This question forces alignment between operational decisions and the educational identity the institution claims.

Share The HELIOS Report

VI. Strategic conversations

The questions in Section V help individuals and teams make better decisions inside an uncertain environment. The questions below ask whether the institution is building the capacity to learn from those decisions over time.

For cabinet discussions

1. Which AI decisions currently being made at the department or office level require cabinet-level coordination, and what is the cost of continuing to let those decisions happen informally?

2. Where is individual AI experimentation generating useful institutional knowledge, and where does that knowledge disappear when people change roles or leave?

3. The surveys show that operational AI adoption is running ahead of academic adoption. Does that asymmetry hold at our institution? Does it reflect a deliberate institutional choice? If not, who is responsible for closing it?

4. Which functions carry the most AI risk right now, and who is accountable for that exposure?

5. What would the institution need to measure to know, two years from now, whether its current AI posture served its educational mission or undermined it?

For board discussions

1. The surveys show that only 55 percent of presidents believe their institution is responding appropriately to AI. Do you have a good sense if your institution’s leadership places you in that group and why?

2. Leadership requires the steadiness to navigate uncertainty about AI and the analytical skills to evaluate AI decisions against mission rather than peer behavior. How is the board assessing whether this institution has that capacity at the leadership level?

3. If the institution’s AI posture over the next three years drifts toward efficiency and away from educational mission, does it have the governance mechanisms to make timely decisions and correct that drift?

4. Institutions that lack data infrastructure, decision-making processes, and staff capacity to implement AI responsibly accumulate liability they have not priced in. What does the board currently know about this institution’s readiness in those areas?

5. Board members make decisions in their own organizations about when to adopt new technology, how to manage the risks, and how to know whether it is serving the organization’s mission. What would it take to bring that same discipline to how this board thinks about AI at this institution?

Can we help?

Ilene Crawford works directly with presidents, cabinets, provosts, and their teams navigating the same coordination problems the surveys describe. Connect with her via her website or LinkedIn.

Robert McGuire develops briefings, trend reports, and playbooks that translate sector signals into actionable insights for universities, nonprofits, and edtech companies. Connect with him via his website or LinkedIn.

Welcome to the inaugural edition of The HELIOS Report. In each report, you will find an executive-level briefing on significant developments in higher education that demand leadership response. You can learn more about The HELIOS Report and how it was produced on the About page of www.heliosreport.com. We hope you find this informs your thinking and decision making as a higher ed leader. We look forward to your feedback.

- Ilene and Robert

Get a PDF version of this article

320KB ∙ PDF file

Download

Download

Share The HELIOS Report

Leadership in an era of program-level scrutiny

Increasingly, programs are expected to “pull their weight” individually. The developments discussed below illustrate several recent examples of that: states exploring three-year bachelor’s degrees, impending federal loan restructuring around questions of individual program value, new workforce Pell eligibility rules, a new “Credentials of Value” benchmark from Lumina Foundation, and a multidimensional credential-quality framework from Burning Glass. We read these as examples of incrementally tightening the link between specific programs and measurable economic outcomes. That puts higher education leaders into a position of deciding what to redesign, what to defend, what to subsidize, and what to close with integrity.

However, managing academic and co-curricular programs like an investment portfolio is unfamiliar and disorienting for many higher education leaders. Their professional identity may be tied to stewardship of a holistic curriculum whose value they are more accustomed to measuring in intellectual rather than financial terms. The news and reports discussed below are obviously not value neutral and, in our view, some of them are misguided. Therefore, leading through this shift requires the steadiness to hold academic integrity and performance accountability in the same conversation, to name trade-offs without panic, and to convert external pressure into disciplined decision making.

I. Developments

States allowing three-year bachelor’s degree proposals

Two more state systems are signaling renewed willingness to compress the bachelor’s degree timeline. North Dakota’s State Board of Higher Education approved several three-year pathways at public institutions. (Inside Higher Ed, Feb. 3, 2026). In Massachusetts, the Board of Higher Education voted to consider proposals for bachelor’s degrees requiring fewer than the standard 120 credits, described as “90-credit” degrees and framed as a three-year option. (Inside Higher Ed, Feb. 12, 2026). The reporting notes the regulation does not specify criteria, while stating proposals should respond to significant changes in society, demographics, technology, educational research, or expectations for postsecondary education. Supporters frame the move as affordability and speed to entering the workforce; critics frame it as weakening the degree by reducing learning that matters and by compressing electives and breadth.

ACE briefing on the U.S. Department of Education’s OBBB

The American Council of Education’s Government Relations team summarizes the work of two rulemaking committees implementing parts of the One Big Beautiful Bill Act (OBBB) at the Education Department: RISE (Reimagining and Improving Student Education) and AHEAD (Accountability in Higher Education and Access Through Demand-Driven Workforce Pell). RISE is directed to phase out graduate and professional PLUS loans, set new annual loan limits and lifetime borrowing caps, and simplify repayment plans. It tightens definitional machinery that matters for institutional oversight, including definitions for “expected time to credential,” “program length,” and how a student remains “in the same program of study” when switching majors. (ACE One Big Beautiful Bill Briefing (PDF), Feb. 1, 2026.)

New America on Workforce Pell implementation

An article from the think tank New America argues that decisions made before and beyond the pending implementation of Workforce Pell in July 2026 will shape which short-term programs gain access to Title IV funding and under what conditions. The article emphasizes that eligibility standards — such as program length, labor market alignment, quality assurance, and performance benchmarks — will determine how institutions design and oversee workforce credentials. It frames Workforce Pell as a structural shift that embeds federal definitions of value and demand directly into program approval and ongoing oversight. (New America, “Why Workforce Pell Implementation Matters Beyond July 2026,” Feb. 2026.)

Lumina’s “Credentials of Value” framework and benchmark

Lumina Foundation introduces a new “Credentials of Value” tool that measures the share of graduates ages 25–64 in the labor force who hold a post-high school degree or certification and earn at least 15% more than the median wage of a high school graduate. The methodology connects attainment to economic value, with a national goal of 75% by 2040. They publish an initial national baseline (43.6%) and explicitly frame the shift as “annual accountability,” tracking whether credentials translate into economic opportunity (Lumina Foundation’s Stronger Nation, Feb. 5, 2026):

Source: https://strongernation.luminafoundation.org/credentials-of-value

Burning Glass Institute: “Measuring What Matters”

The workforce and education data analytics nonprofit Burning Glass Institute published a new report proposing a broader framework for evaluating non-degree credential quality beyond first-year wage gains. “Drawing on the Credential Value Index,” which links 23,000 credentials to career data from over 65 million workers, the report argues that wage-only accountability frameworks misclassify two-thirds of effective credentials. By incorporating career entry, field transition, and long-term mobility alongside immediate wage growth, they find that roughly one in three credentials generate measurable advancement, while still concluding that a large share of credentials provide minimal market value. It introduces a four-category typology: Launchpads, Promotion Catalysts, Lateral Moves, and Dead Ends (Measuring What Matters, Burning Glass Institute, Feb. 2026):

Source: https://www.burningglassinstitute.org/research/measuringwhatmatters

II. Interpretation — Portfolio orientation

These recent sources illustrate how external actors are tightening the link between educational offerings and outcome screens (while disagreeing about what filters and time horizons count.) That combination pushes provosts and deans away from stewardship of “the curriculum” and toward portfolio-oriented decision making at the program level. Program-level accountability itself is not new, but multiple actors are converging on compatible evaluation frameworks at the same time: federal financing rules, philanthropic benchmarking, labor market analytics, and state policy experimentation.

Lumina makes the portfolio approach explicit by defining value through an earnings screen and publishing a baseline for tracking and comparison. Lumina frames the tool as the start of a new era of accountability focused on whether credentials translate into opportunity. When a system adopts an earnings threshold tied to a national median, academic leaders must decide which credentials the institution can publicly stand behind under that definition, which credentials need redesign, which ones require clearer student guidance about likely economic outcomes, and which ones require either a defensible mission rationale or a clean exit.

Burning Glass accepts the accountability impulse but complicates the metric. It argues that wage-only evaluation produces a systematic blind spot because career pivots often require lateral moves or a temporary step back before gains show up. It demonstrates that some credentials function as launchpads into higher-mobility pathways rather than immediate wage maximizers. That critique does not reverse the portfolio shift but gives some nuance to what counts as valuable.

The trend to three-year degrees forces an explicit accounting of what the bachelor’s degree contains, what gets removed, and which learning claims the institution continues to make under compression. If this trend continues, the institution may not be able to rely on “the bachelor’s degree” as an undifferentiated unit to communicate. Institutions pursuing this will be making program-specific choices about which majors can survive compression without breaking academic integrity or licensure pathways.

ACE’s summary of impending student loan regulations, shows the portfolio orientation at work inside federal machinery: an environment that defines and polices program structure through eligibility rules, program definitions, and compliance obligations, while separating “accountability” and “Workforce Pell” into their own negotiated rulemaking lanes. Even when a rule targets financing, it still pushes institutions toward program-level clarity because definitions like “program length,” “expected time to credential,” and program-level consistency requirements create enforceable objects that do not care about campus narratives.

The Workforce Pell rules going into effect this year reinforce this same directional shift. Once short-term credentials gain Title IV eligibility under federally defined standards of labor-market alignment, duration, and performance, institutions must treat each eligible program as a discrete object subject to approval, monitoring, and potential loss of access to aid. An institution broadly supporting workforce preparation will not be sufficient. It must specify which certificates meet federal definitions of value and demand, which can sustain compliance over time, and which expose the institution to financial and reputational risk if performance falters.

In short: This selection of recent news stories and reports illustrate how higher ed is increasingly expected to attach funding, eligibility, and value metrics to program-level performance. If this continues, the academic catalog becomes a set of distinct profiles, each with a bright light on its own cost structure, outcome data, enrollment, and career outcomes.

III. Implications by stakeholder group

President and cabinet. The cabinet must treat “value” definitions as operating conditions that reshape academic decision making. Legislators and the benchmarking work of organizations like Lumina and Burning Glass will keep moving public expectations toward comparability, and compressed-degree and alternative credential proposals will keep asking whether the institution can defend what the degree includes. One of the cabinet’s jobs will be setting decision rights and guardrails: who decides thresholds, who decides exceptions, and who owns the public explanation when a program falls below a screen.

Provost, deans, and academic program design. Because portfolio frameworks force visibility into individual programs, Academic Affairs leadership will have to make intentional decisions which programs to subsidize. The Burning Glass report gives academic leaders a legitimate argument for longer time horizons and multidimensional outcomes, which can protect programs that serve mobility and field entry, while still allowing leaders to name “dead ends.” The work is codifying a decision process, a time window, and when mission rationale overrides an earnings screen.

Institutional research, analytics, and the registrar function. External frameworks are defining new metrics and asking for consistent application. Lumina’s tool sets a benchmark to compare to national medians and an age range; Burning Glass argues for trajectory analysis and multidimensional outcomes; ACE’s summary shows how federal definitions formalize “program length” and “expected time to credential” in ways that connect to catalogs and official publications. This will require data quality and category integrity to match internal program structures to external definitions.

Student affairs, advising, and career services. Portfolio logic changes advising obligations. Students will face, as Burning Glass puts it, more “high-stakes choices with limited, inconsistent information about what a credential actually signals to employers, what skills it confers, how it connects to real jobs, and what outcomes typically follow.” Advisors and career counselors need a message that does not pretend certainty. They need clear institutional guidance on which pathways the institution actively recommends, which ones it offers with explicit cautions, and which ones it is redesigning or phasing out.

Finance and budget leadership. Three-year degree proposals turn time-to-degree into a revenue and cost question, because credit volume and delivery design drive both pricing assumptions and instructional cost. Lumina’s benchmark logic and public accountability frame increase the likelihood that funders and state actors will pressure institutions to concentrate investment in “value-clearing” pathways. Finance leaders will need a shared operating picture with the provost so that budget rules do not silently punish the very redesign work that the external environment is demanding.

Faculty development, centers for teaching and learning, and program design. Portfolio orientation potentially influences syllabi, assignments, and curricular maps. If programs are judged on wage, mobility, and completion signals, then faculty development may emerge that helps departments articulate how learning outcomes connect to occupational pathways without collapsing into narrow training. Centers for teaching and learning will need to support faculty in mapping competencies, sequencing skill development across courses, and documenting where students practice applied work. Program review processes will need to ask not only whether learning goals are coherent, but whether graduates can demonstrate capabilities that align with labor-market entry and advancement. To resist pressure to instrumentalize the curriculum, programs need to be able to make visible how disciplinary depth translates into durable capability.

Marketing and communications. As external actors narrow value into measurable outcomes, institutions will need to articulate what a degree is for without leaning on abstraction. Portfolio logic forces leaders to name the mission precisely. If a program does not clear a short-term earnings screen but cultivates liberal arts skills, civic leadership, cultural stewardship, scientific inquiry, or long-term mobility, leaders must be able to state that case with evidence and discipline. Presidents, provosts, and faculty leaders will need language that connects disciplinary depth to durable capability, democratic participation, and adaptive learning, and they will need to explain why some commitments merit support even when immediate market signals are weak.

IV. The leadership demand

The shift to a portfolio orientation is a mission-performance opportunity if leaders handle it with steadiness. If portfolio management expectations keep growing, leaders still control how it lands on faculty, advisors, student support professionals, and other colleagues and how it reshapes institutional identity.

A deficit posture treats external screens as an indictment and treats every program review as a threat. An assets-based posture treats the same environment as permission to do overdue redesign work with more context and clarity. Burning Glass gives leaders language to defend programs that create long-run mobility and field entry, while still naming programs that fail students on both wages and mobility. Lumina gives leaders a public baseline and a goal structure that makes it easier to focus attention and resources. Three-year degrees give leaders an incentive to clarify what the bachelor’s degree means in different majors, rather than defending a single undifferentiated degree story. The three-year degree trend, whatever its practical merits, can also be read as a signal that the public is hungry for innovation and for the opportunities that higher education promises.

This will also bring up questions about professional identity. Faculty and academic leaders who see themselves as stewards of a shared intellectual project will experience portfolio logic as a value conflict.

In short: Leaders can set a decision-making practice that keeps academic integrity explicit: Define non-negotiables for learning, define acceptable evidence for outcomes, and define the conditions under which mission rationale can justify investment even when an earnings screen looks weak.

V. Strategic questions for the cabinet and board

1. What institutional capabilities must improve if program-level accountability expands? Data integration, labor-market analytics, program cost modeling, and advising capacity may determine whether portfolio management becomes disciplined leadership or administrative chaos.

2. Which programs require explicit mission defense rather than economic justification? If a program does not clear emerging economic benchmarks, can the institution clearly articulate the civic, intellectual, or cultural purpose that justifies continued investment?

3. Which metrics will the institution treat as informative rather than determinative? External benchmarks, employer data, and federal eligibility standards will shape the environment, but leadership must decide which ones actually govern internal decision making.

4. What time horizon will the institution treat as legitimate evidence of value? If external actors emphasize first-year wages while the institution believes value appears through long-run mobility or graduate study, where will leadership draw the line between short-term metrics and longer-term outcomes?

5. When will the institution stop offering pathways that produce minimal short-term value for students? Who has the authority to close a program, and what processes protect legitimacy when that moment arrives? Clear decision rights and transparent processes protect the institution and demonstrate respect for faculty and staff.

6. What story will the institution tell when a program is redesigned or discontinued? Leaders should determine whether the narrative emphasizes stewardship of student opportunity, institutional sustainability, or mission clarity.

VI. Postscript — Late developments

As this edition of The HELIOS Report neared the final stages of editing, we encountered additional relevant news on this theme. We encourage you to review the sources below about a growing list of states tying program-level defunding decisions to federal analysis of “low-earning” degrees, and we invite you to add resources in the comments you have come across to continue the conversation with your colleagues.

• Three Competing Ways States Are Defining the “Value” of Higher Education, Phil Hill & Associates, February 2, 2026

• Indiana governor signs law to cut ‘low earning’ college degrees, Higher Ed Dive, March 6, 2026

• Missouri Senate looks at funding cuts for ‘low-earning’ college degrees, Missouri Independent, February 25, 2026

The HELIOS Report is a joint project of Ilene Crawford Consulting and McGuire Editorial & Consulting. It is developed with HELIOS — Higher Education Leadership Intelligence Orientation System, a proprietary AI-assisted intelligence system that applies a defined set of analytical frameworks and strategic filters to generate usable insights for leaders in higher education, workforce development, and education technology. Learn more, provide feedback, and join the email list for future reports at www.heliosreport.com.

Get a PDF version of this article

320KB ∙ PDF file

Download

Download