This blog was kindly authored by Liam Earney, Managing Director, HE and Research, Jisc.
The REF-AI report, which received funding from Research England and co-authored by Jisc and Centre for Higher Education Transformations (CHET), was designed to provide evidence to help the sector prepare for the next REF. Its findings show that Generative AI is already shaping the approaches that universities adopt. Some approaches are cautious and exploratory, some are inventive and innovative, and most of it is happening quietly in the background. GenAI in research practice is no longer theoretical; it is part of the day-to-day reality of research, and research assessment.
For Jisc, some of the findings in the report are unsurprising. We see every day how digital capability is uneven across the sector, and how new tools arrive before governance has had a chance to catch up. The report highlights an important gap between emerging practice and policy – a gap that the sector can now work collaboratively to close. UKRI has already issued guidance on generative AI use in funding applications and assessment: emphasising honesty, rigour, transparency, and confidentiality. Yet the REF context still lacks equivalent clarity, leaving institutions to interpret best practice alone. This work was funded by Research England to inform future guidance and support, ensuring that the sector has the evidence it needs to navigate GenAI responsibly.
The REF-AI report rightly places integrity at the heart of its recommendations. Recommendation 1 is critical to support transparency and avoid misunderstandings: every university should publish a clear policy on using Generative AI in research, and specifically in REF work. That policy should outline what is acceptable and require staff to disclose when AI has helped shape a submission.
This is about trust and about laying the groundwork for a fair assessment system. At present, too much GenAI use is happening under the radar, without shared language or common expectations. Clarity and consistency will help maintain trust in an exercise that underpins the distribution of public research funding.
Unpicking a patchwork of inconsistencies
We now have insight into real practice across UK universities. Some are already using GenAI to trawl for impact evidence, to help shape narratives, and even to review or score outputs. Others are experimenting with bespoke tools or home-grown systems designed to streamline their internal processes.
This kind of activity is usually driven by good intentions. Teams are trying to cope with rising workloads and the increased complexity that comes with each REF cycle. But when different institutions use different tools in different ways, the result is not greater clarity. It is a patchwork of inconsistent practices and a risk that those involved do not clearly understand the role GenAI has played.
The report notes that most universities still lack formal guidance and that internal policy discussions are only just beginning. In fact, practice has moved so far ahead of governance that many colleagues are unaware of how much GenAI is already embedded in their own institution’s REF preparation, or for professional services, how much GenAI is already being used by their researchers.
The sector digital divide
This is where the sector can work together, with support from Jisc and others, to help narrow the divide that exists. The survey results tell us that many academics are deeply sceptical of GenAI in almost every part of the REF. Strong disagreement is common and, in some areas, reaches seventy per cent or more. Only a small minority sees value in GenAI for developing impact case studies.
In contrast, interviews with senior leaders reveal a growing sense that institutions cannot afford to ignore this technology. Several Pro Vice Chancellors told us that GenAI is here to stay and that the sector has a responsibility to work out how to use it safely and responsibly.
This tension is familiar to Jisc. GenAI literacy is uneven, as is confidence, and even general digital capability. Our role is to help universities navigate that unevenness. In learning and teaching, this need is well understood, with our AI literacy programme for teaching staff well established. The REF AI findings make clear that similar support will be needed for research staff.
Why national action matters
If we leave GenAI use entirely to local experimentation, we will widen the digital divide between those who can invest in bespoke tools and those who cannot. The extent to which institutions can benefit from GenAI is tightly bound to their resources and existing expertise. A national research assessment exercise cannot afford to leave that unaddressed.
We also need to address research integrity, and that should be the foundation for anything we do next. If the sector wants a safe and fair path forward, then transparency must come first. That is why Recommendation 1 matters. The report suggests universities should consider steps such as:
define where GenAI can and cannot be used
require disclosure of GenAI involvement in REF related work
embed these decisions into their broader research integrity and ethics frameworks
As the report notes that current thinking about GenAI rarely connects with responsible research assessment initiatives such as DORA or CoARA, that gap has to close.
Creating the conditions for innovation
These steps do not limit innovation; they make innovation possible in a responsible way. At Jisc we already hear from institutions looking for advice on secure, trustworthy GenAI environments. They want support that will enable experimentation without compromising data protection, confidentiality or research ethics. They want clarity on how to balance efficiency gains with academic oversight. And they want to avoid replicating the mistakes of early digital adoption, where local solutions grew faster than shared standards.
The REF AI report gives the sector the evidence it needs to move from informal practice to a clear, managed approach.
The next REF will arrive at a time of major financial strain and major technological change. GenAI can help reduce burden and improve consistency, but only if it is used transparently and with a shared commitment to integrity. With the right safeguards, GenAI could support fairness in the assessment of UK research.
From Jisc’s perspective, this is the moment to work together. Universities need policies. Panels need guidance. And the sector will need shared infrastructure that levels the field rather than widening existing gaps.
I’ve been thinking a lot about the elements that prevent us from most deeply practicing Personal Knowledge Mastery (PKM) in our lives. A big piece involves fear, the worries that we couldn’t possibly know enough, or being talented enough, to contribute anything to the discourse. I’m at the POD Conference this week in San Diego and have been thinking about my own, long-term desire to get better at sketchnotes, while realizing that the only way you do something like that is to start out not-so-good, and establish a regular practice that could contribute to you getting better.
People often use the metaphor of a gap existing between where we are and where we want to be… We forget the value we might possess along the way. Daniel Sax starts out his video called THE GAP by Ira Glass with text that appears on the screen, in the form of a dedication of sorts. The words initially say:
For everyone in doubt
After a few seconds, an additional line of text appears:
Especially for myself
How many of us can relate to those feelings of doubt?
How often do we ponder what they prevent us from achieving?
After that compelling two-line introduction, Sax shows what I think is a printing press in action, though I’m not entirely sure what I’m looking at, during the first part of the video. Ironically, I wrote in my last post about how Bryan Alexander embodied PKM at a dinner, recently, but I didn’t write much about the other people who were there. However, I realize now that one of the people is working on her doctoral research and it is on Black women who were printmakers in the 1930s, I believe it was. My mind flashed, as I revisited watching Sax’s video, thinking that this doctoral researcher would surely know if what I think I’m seeing here is actually that.
Before now, I hadn’t really paid much attention to Sax’s video description on Vimeo. However, my curiosity was rewarded, by getting to discover that Sax made this video, because he was inspired by another one and wanted to experiment with his own creation. He writes:
I made it for myself and for anybody who is in doubt about his/her creative career. I also think that Ira Glass’ message isn’t only limited to the creative industry. It can be applied to everyone who starts out in a new environment and is willing to improve.
I encourage you to stop and watch Sax’s video: THE GAP by Ira Glass and reflect on the different ways he conveys his messages and ideas, throughout. I wonder how long it took him to do the spoon full of noodle letters, spelling out his thoughts for that 2-3 second part.
Back to Sax’s video description, he ends with a series of expressions of gratitude, to all of those who got him to the point of creating his piece. He thanks David Shiyang Liu, who has a graphical, text-based depiction of Ira’s words about storytelling (which really could be about any new pursuit). Sax continues to thank the people who made his video possible (I suggest going to the video description and witness a wonderful example of giving credit where credit is due).
As Jarche begins to wind down the PKMastery Workshop and invites us to start our PKM practice (if we haven’t, already), he quotes Tim Kastelle:
The biggest gap is between those doing nothing and those doing something.
Jarche uses his book reviews and Friday’s Finds as examples of his PKM practice lived out. He’s been at that for such a long time now, I look forward to each post, as they get released and show up in my RSS feeds. Despite having learned so much over the 10+ years I’ve been following his work, taking this PKM workshop has accelerated my learning exponentially. There’s nothing like doing all the sensemaking and sharing that I set myself up to do when I committed to blogging publicly throughout the six weeks of the workshop.
Of course producing and hosting the Teaching in Higher Ed podcast is a huge part of PKM for me. Here are some unpolished thoughts about how seek-sense-share shows up through this 11-year adventure.
Seek
I get new guest ideas from past podcast guests, conferences I attend, books I read, PR people I now know from book publishers, and from things that show up on my RSS feeds. The point I’m at in my seeking process is actually more so that I need to find ways to filter out the vast number of ideas for possible interviews that come my way and be more disciplined and discerning about saying no (either to myself, or to others).
Sense
In preparing for interviews, I do a ton of sensemaking, thinking through the themes that are narrow enough to not be all over the place, but also not overly prescriptive, lest I miss what is emerging in the moment. I read digitally and typically highlight way too much of the book. Sometimes I mindmap my ideas, or just type up themes and reorder ideas. Creating the show notes for each episode also helps me extend the learning opportunities from each conversation.
Share
The podcast gets shared on all the major podcast directories and services. YouTube recently revised their policies to now allow for RSS feeds from audio-only shows to come through on their site (Teaching in Higher Ed podcast on YouTube). Spotify represents a growing Teaching in Higher Ed audience and has some nice features for more engagement than on other platforms, such as being able to ask listeners a question about what they took away from listening.
Hope
My hope is that I’ll forever continue to live in the gap and experience the positive benefits of being willing to be fueled by the vulnerability required to learn out loud.
We’re two admissions leaders working to reframe how families and institutions think about the gap year. I’m Carol, a former college admissions dean with more than 20 years in higher education, and I’m also a therapist who works with teens. My co-author, Becky Mulholland, is director of first-year admission and operations at the University of Rhode Island. Together, we’re building a new kind of gap year model, one that centers on intention, purpose and career readiness for all.
The gap year concept is overdue for a cultural reset. Most popular options on the market focus on travel, outdoor adventure or service learning, but they rarely emphasize self-exploration in conjunction with career readiness or curiosity about the future of work. The term itself is widely misunderstood and sometimes dismissed. Despite its reputation as a luxury for the privileged, it’s often the families juggling cost, stress and uncertainty who stand to gain the most from a well-supported pause.
For many families, college is the most expensive decision they’ll ever make. Taking time to pause, reflect and plan shouldn’t be seen as risky—it should be seen as wise. At 17 or 18, it’s a lot to ask a young person to know what they want to do with the rest of their life. A 2017 federal data report found that about 30 percent of undergrads who had declared majors changed their major at least once, and about 10 percent changed majors more than once. These shifts often lead to extra courses and sometimes an extra semester or even a year. That’s a lot of wasted money for families who could have benefited from a more intentional pause.
And yet for many parents, the phrase “gap year” still stirs anxiety. They imagine their child lying on a couch for three months, doing nothing, or worse, never learning anything useful and losing all momentum to return to school. The idea feels foreign, risky and hard to explain. They don’t know what to tell their friends or extended family. We push back on that fear and work to normalize the idea of intentional, structured time off. It’s not just for the elite—it needs to be reclaimed as a culturally acceptable norm. That’s why we champion paid, structured earn-while-you-learn pathways such as youth apprenticeships, paid internships, stipend-backed fellowships and employer-sponsored projects that keep income stable while skills grow.
We personally promote the value of intentional pauses when talking with families and prospective students about college, helping them reframe what a year of growth and clarity can mean. We also strongly support programs with built-in pause requirements before graduate school. I’ve read thousands of applications as a dean and witnessed how powerful that year can be when it’s well guided.
Gap years, when framed and supported correctly, can foster self-discovery, emotional growth and direction. But the gap year industry itself also needs to evolve. The industry should move toward models that prioritize intentional career exploration, rooted not only in personal growth and self-awareness but in helping students find a sense of fulfillment in their future careers and lives. If colleges acknowledged the value of these experiences more visibly in their advising models and admissions narratives, they could relieve pressure on families and students and potentially reduce dropout rates and improve long-term outcomes.
We believe it’s time for higher education to actively support and normalize the gap year, not as an elite detour, but as a practical and often necessary path to college and career success. It’s time to give students and their families permission to pause.
Carol Langlois is chief academic officer at ESAI, a generative AI platform for college applicants, and a therapist who specializes in working with teens. She previously served in dean, director and vice provost roles in college admissions.
Becky Mulholland is director of first-year admission and operations at the University of Rhode Island.
Becky and Carol both serve on the Policy Subcommittee of the National Association for College Admission Counseling’s AI in College Admission Special Interest Group.
After a 20-year career in higher education, including roles as a chief academic officer and faculty member, I left to have a child. I was one step away from a presidency on the higher ed career ladder, and in fact I had written my dissertation on what gets in the way of women moving into college presidencies. Yet it was not until I finally met my life partner and had the opportunity, in my 40s, to start a family that I understood how fully the higher ed career deck is still stacked against those seeking to have children, and especially those seeking to have children in nontraditional ways—largely women, LGBTQIA+ folks and anyone facing a difficult pregnancy, in vitro fertilization, adoption or fostering process.
In the United States, 2.6 percent of all births—95,860 babies in 2023—result from IVF, a time-consuming, costly and physically and emotionally challenging process. The percentage for women academics may be even higher, given their relatively high education levels, socioeconomic status and pressure to delay childbearing for academic careers. According to Pew, 56 percent of people with graduate degrees have gone through or know someone who has undergone IVF or other assisted reproduction.
The literature has well documented how the academy has been created by men and is designed to fit their needs and their bodies. Women who have sought professorships or academic leadership positions have, historically, needed to conform to rules written for men’s life cycles. Articles such as Carmen Armenti’s classic “May Babies and Posttenure Babies” speak to women’s attempts to give birth at the end of the academic year and after earning tenure. The tenure clock illustrates this issue well—the usual seven years in which a newly hired assistant professor has time to sufficiently publish and obtain tenure largely coincide with women’s most fertile years. Many forward-thinking institutions such as the University of California system have been addressing this issue by stopping the tenure clock for childbirth and related family formation. It is a step in the right direction that all colleges and universities should consider.
But what happens when the usual challenges of pregnancy and childbirth are compounded by infertility, miscarriage and the sometimes years-long process of IVF?
I met my husband during the pandemic, and we married the next year. Both of us in our 40s and having always wanted a child but neither having met the right partner, we quickly found ourselves going down the IVF route. At the time, I had completed a one-year executive interim role and was on the job hunt and doing part-time remote teaching, and this situation proved fortuitous.
I had no idea how grueling the IVF process would be—multiple rounds of more than a month at a time of hormone pills; nightly self-administered injections for weeks on end; weekly doctor visits, blood draws and ultrasounds—and at the end of each round, a day surgery under anesthesia to retrieve eggs. Several iterations of this, followed by more of a similar process to prepare the body for embryo transfer. The journey is physically and emotionally exhausting, time-consuming, and logistically challenging. It can also be incredibly expensive, with the medications and surgeries costing into the tens of thousands for those whose health insurance does not cover it.
My husband and I had a number of factors helping us on this journey. We had built a supportive network of family and friends. We were fortunate that I was less sick than many women are on these medications. Finally, we were privileged to have insurance (through my husband’s job, which is not in higher ed) that paid for the majority of our treatments. Due to working part-time and remotely, I had the flexibility I needed to take naps, wear comfortable clothes that fit my bloated belly without having to reveal my family-forming status to anyone at work and generally have the privacy I needed during a challenging time.
Other women who work full-time in-person during this process navigate a daunting gauntlet of frequent doctor appointments, exhaustion and sickness at work, while trying to hide a body that can look pregnant before it is. Not to mention that few people fully understand the process, and telling a little can lead you down an uncomfortable path of revealing a lot. Because everything is timed to the menstrual cycle, seemingly innocent questions inevitably lead to awkward conversations. It’s therefore hard to share what you’re going through or ask for support at work at the time you need it most.
And then there are the chemical pregnancies and miscarriages that can happen, and did for us. Grieving for both parents is exacerbated by the isolation and privacy of the whole process. Some companies and higher ed institutions, such as Tufts University in Massachusetts, now offer bereavement leave for miscarriage, something that happens in 10 to 20 percent of pregnancies but is still rarely talked about. All institutions throughout higher ed should offer similar leave.
During this journey, I was also interviewing for full-time jobs, and I was hired into a senior leadership position. My husband and I were taking a break from the exhausting process at that point and the opportunity was once-in-a-lifetime, and so we picked up and moved two states away. My husband’s job had gone remote, giving us the flexibility we needed for my career. We wagered that if I stayed in a part-time role too much longer, it would be increasingly difficult to climb back into a full-time position. The stigma around a résumé gap is alive and well in higher education, with little understanding that this gap often reflects people’s (frequently women’s) time away for family and other care-taking needs, rather than their work experience or abilities. Yet, even when I’ve tried to explain to search committees that I’ve led how discriminatory it can be to overly focus on résumé gaps, faculty and staff often have looked askance at me. This is something else that needs to change.
My husband and I waited almost a year before doing our next embryo transfer. I settled into the job, we settled into our home, we finally had a post-COVID celebration of our marriage. And then I was pregnant! Sadly, I miscarried again toward the end of my first trimester. I powered through at work, serving as a chief academic officer and supervising 200 people while trying to juggle meds, doctor’s appointments, exhaustion and then loss. I read students’ names at a stadium-sized graduation ceremony soon after a miscarriage.
It became clear to me over the following months that the stress and lack of flexibility of a senior role would not lend itself to a last chance at a healthy pregnancy. It was a difficult decision to leave, but also one that I had no doubts about once made. Within weeks we were pregnant again, this time successfully so with a beautiful baby girl who is now a year old. It was not an easy pregnancy, and our daughter likely would not be here had I stayed in my role and not been able to rest as much as I did.
Since her birth, I have launched a higher ed editing and consulting business, resumed teaching part-time, and otherwise adjusted to life as a new mother. For me, leaving higher ed senior leadership was a deliberate choice. I needed more flexibility and control over my own time to be able to care for myself and my child properly. I may or may not return someday to that leadership pathway, and that door may or may not be open to me if I attempt to do so. I’ve learned, however, that to address the question my dissertation asked—Why don’t we have more women in presidencies?—we need to better understand and respond to the many women (and many men and nonbinary folks) who find themselves going through similar family-formation challenges across higher education.
First, we need to offer more flexibility—remote work, flexible hours, the option for extended parental leaves for new parents and foster parents.
Second, we need to consider not only fully paid leave under the Family and Medical Leave Act for childbirth and parental bonding, but also paid medical benefits for IVF as well as similar support for adoption and fostering.
Third, we need to formalize bereavement leave for miscarriage.
Fourth, we need to destigmatize the career gap, so that those who leave would have the opportunity to return.
Fifth, we need to fairly compensate those who assume the work of colleagues who take FMLA for any care-taking reason.
Lastly, we need to change the higher ed culture to one that understands and supports family formation in all its iterations, not just traditional pregnancy with traditional medical leaves.
I recognize my privilege in being able to leave my job—privilege that enabled me to have a child when so many before me without the same economic resources have not been able to. My situation may seem like an outlier to those who are in their 20s or early 30s or who have had relatively easy and healthy pregnancies. But I’m sure that my story rings true for those who have delayed childbearing for their academic careers and then faced the rigors of IVF, or for people of any age who have faced infertility or more difficult pregnancies. For those LGBTQIA+ and other folks who go through the egg/sperm donation process and IVF and surrogacy. For couples and singles who may adopt or foster and face needs for legal meetings and other child-related time off that institutions do not always provide.
Higher ed has taught me so much about antiracism, feminism, LGBTQIA+ rights and other inclusive practices. However, higher ed writ large doesn’t offer the kinds of paid leave and flexibility needed for all employees to succeed at both parenting and work.
Higher ed is losing women with executive leadership potential. The majority of undergraduate and graduate students are women. Yet only 37 percent of full-time faculty are women. Only 33 percent of college presidents are women. Women melt away for a host of reasons. But this former chief academic officer, one step away from a presidency on the career ladder, left the executive pathway because it was the only way I could do so and have a healthy pregnancy and a healthy child.
As long as higher ed makes having a child versus having an academic career a zero-sum choice for many women, it shouldn’t be a surprise that we still have so few women in senior leadership. When the answer becomes “yes, have both” at institutions across the board is when we might start to see the numbers change.
One of the great promises of higher education is that it acts as a social ladder—one that allows students from low-income backgrounds to climb up and reach a higher social and economic status. No one, I think, ever believed it was a guaranteed social leveler, or that children from wealthier families didn’t have an easier time succeeding after college because of their own, and their family’s, social and cultural capital. But most people, in America at least, believed that on the whole it played a positive role in increasing social mobility.
Over the past couple of decades, though, particularly as student debt has increased, people have begun to wonder if this story about social mobility through college is actually true. That’s a hard question to answer definitively. Data sets that track both student origins and outcomes are few and far between, and it’s also difficult to work out what social mobility used to look like in a quantifiable sense.
However, this summer economist Sarah Quincy of Vanderbilt University and Zach Bleemer of Princeton University released a paper called Changes in the College Mobility Pipeline Since 1900. This paper overcame some of those data limitations and took a long, more than century-long, look at the relationship between social mobility and college attendance.
What they found was sobering. Not only is higher education no longer helping poor students catch up with wealthier ones, but in fact the sector’s role as a social elevator actually stopped working almost 80 years ago. This seemed like a perfect story for the podcast, and so we invited Zach Bleemer—who you may remember from an episode on race-conscious admissions about two years ago—to join us to discuss it.
This discussion ranges from the methodological to the expositional. Where does the data come from? What does the data really mean? And are there alternative explanations for the paper’s surprising findings? But enough from me—let’s hear from Zach.
The World of Higher Education Podcast Episode 4.4 | The Widening Gap: Income, College, and Opportunity with Zachary Bleemer
Transcript
Alex Usher (AU): Zach, you wrote, with Sarah Quincy, a paper called Changes in the College Mobility Pipeline Since 1900, which looks a long way back. And you argue that the relative premium received by lower-income Americans from higher education has fallen by half since 1960. Take us through what you found—give us the 90-second elevator pitch.
Zachary Bleemer (ZB): Consider kids who were born in 1900 and were choosing whether or not to go to college in the late 1910s and early 1920s. What we were interested in was that choice, and in particular, following people for the next 20 years after they made it. Some people graduated high school but didn’t go to college, while others graduated high school and chose to go.
We wanted to compare the differences in early 1930s wages between those two groups—both for kids from lower-income backgrounds and kids from upper-income backgrounds. Now, you might be surprised to learn that there were lower-income kids going to college in the U.S. in the early 1920s, but there were. About 5 to 10% of people from the bottom parental income tercile even then were attending college.
What we found, when we linked together historical U.S. census records and followed kids forward, is that whether you were low-income or high-income, if you went to college your wages went up a lot. And the degree to which your wages went up was independent of whether you were low-income or high-income—everyone benefited similarly from going to college.
If you compare that to kids born in the 1980s, who were choosing to go to college in the late 1990s and early 2000s, you see a very different story. Everyone still gains from going to college, but kids from rich backgrounds gain a lot more—more than twice as much as kids from poor backgrounds. And that’s despite the fact they’re making the same choice. They’re going to different universities and studying different things, but when it comes down to the 18-year-old making a decision, those from poor families are just getting less from American higher education now than they did in the past—or compared to kids from rich backgrounds.
AU: I want to make sure I understand this, because it’s a crucial part of your argument. When you talk about relative premiums—premium compared to what, and relative compared to what?
ZB: What we always have in mind is the value of college for rich kids, and then asking: how much of that value do poor kids get too? In the early 20th century, and as late as the 1960s, those values were very similar. Lower-income kids were getting somewhere between 80 and 100% of the value of going to college as higher-income kids.
AU: And by “value,” you mean…
ZB: That just means how much your wages go up. So, the wage bump for lower-income kids was very similar to that of higher-income kids. Today, though, it’s more like half—or even a little less than half—of the economic value of college-going that lower-income kids receive compared to higher-income kids.
AU: So in effect, higher education is acting as an engine of greater inequality. That’s what you’re saying?
ZB: I guess it’s worth saying that lower-income kids who go to college are still getting ahead. But it’s not as much of a pipeline as it used to be. Higher education used to accelerate lower-income kids—not to the same level of income as their higher-income peers; they were never going to catch up—but at least they got the same bump, just from a lower starting point.
AU: So the gap widens now. But how do you make a claim like that over 120 years? I mean, I sometimes have a hard time getting data for just one year. How do you track college premiums across a period of 120 years? How sound is the empirical basis for this? You mentioned something about linking data to census records, which obviously go back quite a way. So tell us how you constructed the data for this.
ZB: The first-order answer is that I called up and worked with an economic historian who had much more experience with historical data than I did. Like you said, it’s hard in any period to get high-quality data that links students in high school—especially with information on their parental income—to wage outcomes 10 or 15 years later.
What we did was scan around for any academic or government group over the last 120 years that had conducted a retrospective or longitudinal survey—where you either follow kids for a while, or you find a bunch of 30-year-olds and ask them questions about their childhood. We combined all of these surveys into a comprehensive database.
In the early 20th century, that meant linking kids in the 1920 census, when they were still living with their parents, to the same kids in the 1940 census, when they were in their early thirties and working in the labor market. That link has been well established by economic historians and used in a large series of papers.
By the middle of the 20th century, sociologists were conducting very large-scale longitudinal surveys. The biggest of these was called Project Talent, put together by the American Institutes for Research in 1961. They randomly sampled over 400,000 American high school students, collected a ton of information, and then re-surveyed them between 1971 and 1974 to ask what had happened in their lives.
In more recent years, there’s been a large set of governmental surveys, primarily conducted by the Departments of Labor and Education. Some of these will be familiar to education researchers—like the National Longitudinal Survey of Youth (NLSY). Others are less well known, but there are lots of them. All we did was combine them all together.
AU: I noticed in one of the appendices you’ve got about nine or ten big surveys from across this period. I guess one methodological limitation is that they don’t all follow respondents for the same amount of time, and you’d also be limited to questions where the surveys provided relatively similar answers. You never get your dream data, but those would be the big limitations—you’ve got to look for the similarities, and that restricts you.
ZB: I’d add another restriction. You’re right that, as we filtered down which datasets we could use, the key variables we needed were: parental income when the student was in high school, level of education by age 30, and how much money they made at some point between ages 30 and 35. All of our surveys had those variables.
We also looked for information about what college they attended and what their college major was. Ideally, the surveys also included some kind of high school test—like the SAT or an IQ test—so we could see what kinds of students from what academic backgrounds were going to college.
But there was another key limitation. In most of the data before 1950, it was really difficult to get a direct measure of parental income. Instead, we usually had proxies like parental occupation, industry, or level of education—variables that are highly predictive of income, but not income itself.
So, a lot of the work of the paper was lining up these measures of varying quality from different surveys to make sure the results we report aren’t just noise from mismeasurement, but instead reflect real changes on the ground in American higher education.
AU: So you ran the data and noticed there was a sharp inflection point—or maybe not sharp, but certainly things started to get worse after 1960. When you first saw that, what were your hypotheses? At that point, you’ve got to start looking at whatever variables you can to explain it. What did you think the answer was, and what did you think the confounding variables might be?
ZB: My expectation was that two things would primarily explain the change. My background is in studying undergraduate admissions, so I thought the first explanation would be rising meritocracy in admissions. That might have made it harder for lower-income and lower-testing kids to get access to high-quality education. I also thought changes in affirmative action and in access to selective schools for kids from different backgrounds, along with rising tuition that made it harder for lower-income kids to afford those schools, could have played a big role. That was one possible story.
The second possible story is that it had nothing to do with the causal effect of college at all. Instead, maybe the poor kids who go to college today aren’t as academically strong as they were in the past. Perhaps in the past only the brilliant poor kids went to college, while all the rich kids went regardless of ability. So it could have looked like poor kids were getting a big benefit from college, when in fact those few who made it would have done well anyway.
It turns out neither of these explanations is the primary driver of rising regressivity. On the test score story, it’s always been the case that rich kids who go to college have relatively higher test scores than rich kids who just graduate high school—and that poor kids who go to college have relatively lower scores compared to their peers. That hasn’t changed since 1960.
And on the access story, it’s always been the case that rich kids dominate the schools we now think of as “good”—the fancy private universities and the flagship public universities. But over the last 50 years, poor kids have actually slightly increased their representation at those schools, not the other way around. Rising meritocracy hasn’t pushed poor kids out. If anything, the variety of admissions programs universities have implemented to boost enrollment among racial minority and lower-income students has relatively increased their numbers compared to 1950 or 1960.
AU: You were just making the case that this isn’t about compositional change in where poor students went. I heard you say there are more lower-income students at Harvard, Yale, and MIT than there were 50 or 60 years ago—and I have no doubt that’s true. But as a percentage of all poor students, surely that’s not true. The vast wave of lower-income students, often from minority backgrounds, are ending up in community colleges or non-flagship publics. Surely that has to be part of the story.
ZB: Yes. It turns out there are three primary trends that explain this rising collegiate regressivity, and you just hit on two of them.
The first is exactly your point: lower-income students primarily go to satellite public universities, basically all the non–R1 publics. Higher-income students, if they attend a public university, tend to go to the flagship, research-oriented universities.
I’ll skip talking about Harvard, Yale, and Princeton—almost no one goes to those schools, and they’re irrelevant to the overall landscape.
AU: Because they’re such a small piece of the pie, right?
ZB: Exactly. Fewer than 1% of students attend an Ivy Plus school. They don’t matter when we’re talking about American higher education as a whole. The flagships, though, matter a lot. About a third of all four-year college students go to a research-oriented flagship public university.
What’s happened since 1960 isn’t that poor kids lost access to those schools—it’s that they never really had access in the first place. Meanwhile, those schools have gotten much better over time. If you look at simple measures of university quality—student-to-faculty ratios, instructional expenditures per student, graduation rates—or even our own wage “value-added” measures (the degree to which each university boosts students’ wages), the gap between flagship and non-flagship publics has widened dramatically since the 1960s.
The flagships have pulled away. They’ve gotten more money—both from higher tuition and from huge federal subsidies, in part for research—and they’ve used that money to provide much more value to the students who attend. And those students tend to be higher income.
The second trend is what you mentioned: increasing diversion to community colleges. Interestingly, before 1980, community colleges were already well established in the U.S. and enrolled only slightly more lower-income than higher-income students. They actually enrolled a lot of high-income students, and the gap was small. Since the 1980s, though, that gap has grown substantially. There’s been a huge diversion of lower-income students toward community colleges—and those schools just provide lower-value education to the students who enroll.
AU: At some level this is a sorting story, right? You see that in discussions about American economic geography—that people sort themselves into certain areas. Is that what you’re saying is happening here too?
ZB: It’s not about sorting inside the four-year sector. It’s about sorting between the two- and four-year sectors. And on top of that, we think there’s fundamentally a story about American state governments choosing to invest much more heavily in their flagship publics—turning them into gem schools, amazing schools—while leaving the other universities in their states behind. Those flagships enroll far more higher-income than lower-income students.
AU: When I was reading this paper, one thing that struck me was how hard it is to read about American higher education without also reading something about race. The last time you were on, we were talking about SCOTUS and the Fair Harvard decision. But as far as I can tell, this paper doesn’t talk about race. I assume that goes back to our earlier discussion about data limitations—that race just wasn’t captured at some point. What’s the story there?
ZB: No—we observe race throughout this entire period. In fact, you could basically rewrite our study and ask: how has the relative value of college for white kids compared to Black kids changed over the last hundred years? I suspect you’d see very similar patterns.
The datasets we’re working with observe both parental income and race, but they aren’t large enough to separately analyze, for example, just white students and then compare lower- and higher-income groups over time. There’s a sense in which you could tell our story in terms of race, or you could tell it in terms of class—and both would be right. At a first-order level, both are happening. And within racial groups, the evidence we’ve been able to collect suggests that class gaps have substantially widened over time.
Similarly, we show some evidence that even within the lower-income group there are substantial gaps between white and Black students. So in part, I saw this as an interesting complement to the work I’d already done on race. It points out that while race is part of the story, you can also reframe the entire conversation in terms of America’s higher education system leaving lower-income students behind—irrespective of race.
AU: Right, because it strikes me that 1960 is only six years after Brown v. Board of Education. By the early to mid-1960s, you’d start to see a bigger push of Black students entering higher education, becoming a larger share of the lower-income sector. And a few years later, the same thing with Latino students.
Suddenly lower-income students are not only starting from further behind, but also increasingly made up of groups who, irrespective of education, face discrimination in the labor market. Wouldn’t that pull things down? Wouldn’t that be part of the explanation?
ZB: Keep in mind that when we measure wage premiums, we’re always comparing people who went to college with people who only finished high school. So there are Black students on both sides of that comparison, across both lower- and higher-income groups.
That said, I think your point is well taken. We don’t do any work in the paper specifically looking at changes in the racial composition of students by parental income over this period. One thing we do show is that the test scores of lower-income students who go to college aren’t falling over time. But you’re probably right: while racial discrimination affects both college-goers and non-college-goers, it’s entirely plausible that part of what we’re picking up here is the changing racial dynamics in college-going.
AU: What’s the range of policy solutions we can imagine here, other than, you know, taking money away from rich publics and giving it to community colleges? That’s the obvious one to me, but maybe there are others.
ZB: And not just community colleges—satellite publics as well. I’ve spent the last five years of my life thinking about how to get more disadvantaged students into highly selective universities, and what happens when they get there. The main takeaway from that research is that it’s really hard to get lower-income students into highly selective universities. It’s also expensive, because of the financial aid required.
But once they get into those schools, they tend not only to benefit in terms of long-run wage outcomes, they actually derive disproportionate value. Highly selective schools are more valuable for lower-income kids than for the higher-income kids who typically enroll there.
What I’ve learned from this project, though, is that the closing of higher education’s mobility pipeline isn’t fundamentally about access. It’s about investments—by state governments, by students, by donors, by all the people and organizations that fund higher education. Over time, that funding has become increasingly centralized in schools that enroll a lot of wealthy students.
So, the point you brought up—redirecting funds—is important. In California they call it “rebenching”: siphoning money away from high-funded schools and pushing it toward low-funded schools. There’s very little academic research on what happens when you do that, but our study suggests that this century-long trend of unequal investment has disadvantaged low-income students. Potentially moving in the other direction could make a real difference for them.
AU: Zach, thanks so much for being with us today.
ZB: My pleasure.
AU: It just remains for me to thank our excellent producers, Tiffany MacLennan and Sam Pufek, and you, our listeners and readers, for joining us. If you have any questions or comments about today’s podcast, or suggestions for future editions, don’t hesitate to get in touch at [email protected].
Join us next week when our guest will be Dmitry Dubrovsky, a research scholar and lecturer at Charles University in Prague. He’ll be talking to us about the slow-motion collapse of Russian higher education under Vladimir Putin. Bye for now.
*This podcast transcript was generated using an AI transcription service with limited editing. Please forgive any errors made through this service.Please note, the views and opinions expressed in each episode are those of the individual contributors, and do not necessarily reflect those of the podcast host and team, or our sponsors.
IRVING, Texas — Crowded around a workshop table, four girls at de Zavala Middle School puzzled over a Lego machine they had built. As they flashed a purple card in front of a light sensor, nothing happened.
The teacher at the Dallas-area school had emphasized that in the building process, there are no such thing as mistakes. Only iterations. So the girls dug back into the box of blocks and pulled out an orange card. They held it over the sensor and the machine kicked into motion.
“Oh! Oh, it reacts differently to different colors,” said sixth grader Sofia Cruz.
In de Zavala’s first year as a choice school focused on science, technology, engineering and math, the school recruited a sixth grade class that’s half girls. School leaders are hoping the girls will stick with STEM fields. In de Zavala’s higher grades — whose students joined before it was a STEM school — some elective STEM classes have just one girl enrolled.
Efforts to close the gap between boys and girls in STEM classes are picking up after losing steam nationwide during the chaos of the Covid pandemic. Schools have extensive work ahead to make up for the ground girls lost, in both interest and performance.
In the years leading up to the pandemic, the gender gap nearly closed. But within a few years, girls lost all the ground they had gained in math test scores over the previous decade, according to an Associated Press analysis. While boys’ scores also suffered during Covid, they have recovered faster than girls, widening the gender gap.
As learning went online, special programs to engage girls lapsed — and schools were slow to restart them. Zoom school also emphasized rote learning, a technique based on repetition that some experts believe may favor boys, instead of teaching students to solve problems in different ways, which may benefit girls.
Old practices and biases likely reemerged during the pandemic, said Michelle Stie, a vice president at the National Math and Science Initiative.
“Let’s just call it what it is,” Stie said. “When society is disrupted, you fall back into bad patterns.”
In most school districts in the 2008-09 school year, boys had higher average math scores on standardized tests than girls, according to AP’s analysis, which looked at scores across 15 years in over 5,000 school districts. It was based on average test scores for third through eighth graders in 33 states, compiled by the Educational Opportunity Project at Stanford University.
A decade later, girls had not only caught up, they were ahead: Slightly more than half of districts had higher math averages for girls.
Within a few years of the pandemic, the parity disappeared. In 2023-24, boys on average outscored girls in math in nearly 9 out of 10 districts.
A separate study by NWEA, an education research company, found gaps between boys and girls in science and math on national assessments went from being practically non-existent in 2019 to favoring boys around 2022.
Studies have indicated girls reported higher levels of anxiety and depression during the pandemic, plus more caretaking burdens than boys, but the dip in academic performance did not appear outside STEM. Girls outperformed boys in reading in nearly every district nationwide before the pandemic and continued to do so afterward.
“It wasn’t something like Covid happened and girls just fell apart,” said Megan Kuhfeld, one of the authors of the NWEA study.
In the years leading up to the pandemic, teaching practices shifted to deemphasize speed, competition and rote memorization. Through new curriculum standards, schools moved toward research-backed methods that emphasized how to think flexibly to solve problems and how to tackle numeric problems conceptually.
Educators also promoted participation in STEM subjects and programs that boosted girls’ confidence, including extracurriculars that emphasized hands-on learning and connected abstract concepts to real-life applications.
When STEM courses had large male enrollment, Superintendent Kenny Rodrequez noticed girls losing interest as boys dominated classroom discussions at his schools in Grandview C-4 District outside Kansas City. Girls were significantly more engaged after the district moved some of its introductory hands-on STEM curriculum to the lower grade levels and balanced classes by gender, he said.
When schools closed for the pandemic, the district had to focus on making remote learning work. When in-person classes resumed, some of the teachers had left, and new ones had to be trained in the curriculum, Rodrequez said.
“Whenever there’s crisis, we go back to what we knew,” Rodrequez said.
Despite shifts in societal perceptions, a bias against girls persists in science and math subjects, according to teachers, administrators and advocates. It becomes a message girls can internalize about their own abilities, they say, even at a very young age.
In his third grade classroom in Washington, D.C., teacher Raphael Bonhomme starts the year with an exercise where students break down what makes up their identity. Rarely do the girls describe themselves as good at math. Already, some say they are “not a math person.”
“I’m like, you’re 8 years old,” he said. “What are you talking about, ‘I’m not a math person?’”
Girls also may have been more sensitive to changes in instructional methods spurred by the pandemic, said Janine Remillard, a math education professor at the University of Pennsylvania. Research has found girls tend to prefer learning things that are connected to real-life examples, while boys generally do better in a competitive environment.
“What teachers told me during Covid is the first thing to go were all of these sense-making processes,” she said.
At de Zavala Middle School in Irving, the STEM program is part of a push that aims to build curiosity, resilience and problem-solving across subjects.
Coming out of the pandemic, Irving schools had to make a renewed investment in training for teachers, said Erin O’Connor, a STEM and innovation specialist there.
The district last year also piloted a new science curriculum from Lego Education. The lesson involving the machine at de Zavala, for example, had students learn about kinetic energy. Fifth graders learned about genetics by building dinosaurs and their offspring with Lego blocks, identifying shared traits.
“It is just rebuilding the culture of, we want to build critical thinkers and problem solvers,” O’Connor said.
Teacher Tenisha Willis recently led second graders at Irving’s Townley Elementary School through building a machine that would push blocks into a container. She knelt next to three girls who were struggling.
They tried to add a plank to the wheeled body of the machine, but the blocks didn’t move enough. One girl grew frustrated, but Willis was patient. She asked what else they could try, whether they could flip some parts around. The girls ran the machine again. This time, it worked.
“Sometimes we can’t give up,” Willis said. “Sometimes we already have a solution. We just have to adjust it a little bit.”
Lurye reported from Philadelphia. Todd Feathers contributed reporting from New York.
The Associated Press’ education coverage receives financial support from multiple private foundations. AP is solely responsible for all content. Find AP’s standards for working with philanthropies, a list of supporters and funded coverage areas at AP.org.
The Hechinger Report provides in-depth, fact-based, unbiased reporting on education that is free to all readers. But that doesn’t mean it’s free to produce. Our work keeps educators and the public informed about pressing issues at schools and on campuses throughout the country. We tell the whole story, even when the details are inconvenient. Help us keep doing that.
Not surprisingly, jobs in AI are the fastest growing of any in the country, with a 59 percent increase in job postings between January 2024 and November 2024. Yet we continue to struggle with growing a workforce that is proficient in STEM.
To fill the AI talent pipeline, we need to interest kids in STEM early, particularly in math, which is critical to AI. But that’s proven difficult. One reason is that math is a stumbling block. Whether because of math anxiety, attitudes they’ve absorbed from the community, inadequate curricular materials, or traditional teaching methods, U.S. students either avoid or are not proficient in math.
A recent Gallup report on Math Matters reveals that the U.S. public greatly values math but also experiences significant gaps in learning and confidence, finding that:
95 percent of U.S. adults say that math is very or somewhat important in their work life
43 percent of U.S. adults wish they had learned more math skills in middle or high school.
24 percent of U.S. adults say that math makes them feel confused
Yet this need not be the case. Creative instruction in math can change the equation, and it is available now. The following three examples from respected researchers in STEM education demonstrate this fact.
The first is a recently published book by Susan Jo Russell and Deborah Schifter, Interweaving Equitable Participation and Deep Mathematics. The book provides practical tools and a fresh vision to help educators create math classrooms where all students can thrive. It tackles a critical challenge: How do teachers ensure that all students engage deeply with rigorous mathematics? The authors pose and successfully answer key questions: What does a mathematical community look like in an elementary classroom? How do teachers engage young mathematicians in deep and challenging mathematical content? How do we ensure that every student contributes their voice to this community?
Through classroom videos, teacher reflections, and clear instructional frameworks, Russell and Schifter bring readers inside real elementary classrooms where all students’ ideas and voices matter. They provide vivid examples, insightful commentary, and ready-to-use resources for teachers, coaches, and school leaders working to make math a subject where every student sees themselves as capable and connected.
Next is a set of projects devoted to early algebra. Significantly, research shows that how well students perform in Algebra 2 is a leading indicator of whether they’ll get into college, graduate from college, or become a top income earner. But introducing algebra in middle school, as is the common practice, is too late, according to researchers Maria Blanton and Angela Gardiner of TERC, a STEM education research nonprofit. Instead, learning algebra must begin in K-5, they believe.
Students would be introduced to algebraic concepts rather than algebra itself, becoming familiar with ways of thinking using pattern and structure. For example, when students understand that whenever they add two odd numbers together, they get an even number, they’re recognizing important mathematical relationships that are critical to algebra.
Blanton and Gardiner, along with colleagues at Tufts University, University of Wisconsin Madison, University of Texas at Austin, Merrimack College, and City College of New York, have already demonstrated the success of an early algebra approach through Project LEAP, the first early algebra curriculum of its kind for grades K–5, funded in part by the National Science Foundation.
If students haven’t been introduced to algebra early on, the ramp-up from arithmetic to algebra can be uniquely difficult. TERC researcher Jennifer Knudsen told me that elementary to middle school is an important time for students’ mathematical growth.
Knudsen’s project, MPACT, the third example of creative math teaching, engages middle school students in 3D making with everything from quick-dry clay and cardboard to digital tools for 3D modeling and printing. The project gets students involved in designing objects, helping them develop understanding of important mathematical topics in addition to spatial reasoning and computational thinking skills closely related to math. Students learn concepts and solve problems with real objects they can hold in their hands, not just with words and diagrams on paper.
So far, the evidence is encouraging: A two-year study shows that 4th–5th graders demonstrated significant learning gains on an assessment of math, computational thinking, and spatial reasoning. These creative design-and-making units are free and ready to download.
Math is critical for success in STEM and AI, yet too many kids either avoid or do not succeed in it. Well-researched interventions in grade school and middle school can go a long way toward teaching essential math skills. Curricula for creating a math community for deep learning, as well as projects for Early Algebra and MPACT, have shown success and are readily available for school systems to use.
We owe it to our students to take creative approaches to math so they can prepare for future AI and STEM professions. We owe it to ourselves to help develop a skilled STEM and AI workforce, which the nation needs to stay competitive.
Dr. Nadine Bonda, TERC
Dr. Nadine Bonda has worked in education for over 40 years, holding positions of Superintendent, Assistant Superintendent, Principal, Mathematics Department Chair, Mathematics Teacher, and Head of a school for students with dyslexia and language processing problems. Most recently she was an Assistant Professor at American International College. Dr. Bonda holds a PhD in Curriculum and Instruction from the University of British Columbia, a C.A.G.S. in Leadership from Boston University, an MEd in Mathematics from Boston University, and a BA in Mathematics from Regis College. She is chairman of the board of directors at TERC.
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By Pete Moss, Business Development Director at Ellucian.
‘Pouvez-vous s’il vous plaît me dire où se trouve la gare?’ – this is the extent that a colleague of mine can remember from his Introductory French module that he completed as part of a computing degree in the late 90s. That institution’s attempt at the time to embed flexibility and cross-curriculum choice to help students develop skills out of their discipline to help with employability. ‘It was easier to pass than the programming courses’ was the authentic feedback that my colleague gave in retrospect, but they did at least have the choice to expand their learning experience and gain some broader foundational skills. That institution, however, has long abandoned much of that flexibility, largely due to the apparent complexity of administration.
That is not to say that there are not fantastic examples of employability related skills initiatives across the sector, but the recent policy landscape (not least the Skills England Sector evidence on the growth and skills offer) and ever-present national growth agenda are now firmly putting the spotlight on the role of HE in this area. The if element of HE holding that key role in the skills agenda is widely held, but now the thorny problem of how must be addressed. Technology advancements, specifically AI, will play a contributory factor in how institutions can remove barriers that caused institutions to reduce flexibility in the past, but what of the wider considerations?
To explore this topic further I asked Ben Rodgers, an experienced academic registrar and AHEP consultant, for his views on the topic:
In today’s fast-moving global economy, the value of a university education is increasingly measured not just by academic achievement, but by the employability of graduates. Employers are no longer looking solely for degrees, they’re looking for skills: digital fluency, critical thinking, communication, and technical know-how that align with the needs of their industries. Meanwhile, universities are under pressure to demonstrate that their programmes deliver real-world value. The challenge is clear: how do we bridge the gap between what is taught and what is needed?
This is where technology can make a transformative difference. At the forefront of this change is a new wave of AI-powered innovation designed to bridge the gap between academic programmes and real-world skill demands. These emerging technologies can analyse curricula, extracting the skills embedded within them and mapping those against labour market data to identify areas of alignment and gaps.
Crucially, they work in both directions; institutions can see what skills a course develops, while students or employers can start with a desired competency like coding or digital marketing and trace back to the programmes that build those capabilities.
It is the kind of innovation that higher education has long needed. For too long, the link between the classroom and the workplace has been inconsistent or poorly articulated. Universities may know they are delivering valuable learning, but haven’t always had the means to evidence that value in terms that resonate with employers and prospective students. These technologies bring much-needed clarity, offering structured and data-informed ways to demonstrate how academic learning contributes directly to employment readiness.
A Game-Changer for the Lifelong Learning Entitlement (LLE)
This kind of technology becomes even more important as the UK rolls out the Lifelong Learning Entitlement (LLE). The LLE is set to reshape the educational landscape by allowing individuals to access student finance for short courses, modular learning and skills-based development over the course of their lives. This shift away from traditional three-year degrees opens new possibilities, but also new challenges.
How will learners know which modules to pick? How will they know what skills they need for the job they want or even the job they haven’t yet imagined? With the support of emerging AI-driven tools, learners can begin to reverse-engineer their career goals. Want to become a Data Scientist? These systems can help identify which combinations of modules across a university lead to that destination. Interested in project management? The technology can pinpoint where those skills are taught, and which courses offer them. It’s like having a careers advisor, curriculum guide, and labour market analyst all in one—offering personalised insights that connect educational choices with professional ambitions.
This sort of capability is vital if LLE is to be more than just a funding mechanism. It needs to be supported by intelligent infrastructure that empowers learners to make informed choices. Otherwise, there’s a risk that modular study becomes a confusing patchwork of disconnected learning.
Towards a Shared, Inter-University Skills Ecosystem
Now imagine if we took this even further. What if a skills platform were adopted not just by individual institutions but as a shared framework across regions or even nationally? In this model, students in Glasgow, Cardiff, Birmingham, or Belfast could see the skills they need for local job markets and be directed to the institutions offering them. This would create a more agile, responsive, and learner-centred education system. Universities wouldn’t just be competing with each other; they’d be collaborating to build a broader skills ecosystem.
The scale of opportunity here is significant and growing fast. Consider this: if every individual in the workforce has access to around £1,800 in personal development funding each year, the cumulative potential across a university’s learner base is vast. Multiply that by hundreds or thousands of learners, and you’re looking at a transformative funding stream that’s currently underutilised.
This is not just an opportunity for students, it’s a strategic imperative for institutions. By enabling individuals to build relevant, targeted skills, universities position themselves as essential engines of workforce development, driving economic resilience at local, regional, and national levels. It’s a win-win: empowered learners, future-ready graduates, and sustainable new revenue for the sector.
Of course, this requires a shift in thinking from institutional autonomy to inter-institutional alignment. But the benefits are compelling: more efficient use of public funding, stronger regional economies, and better outcomes for students.
Making Programme Design More Purposeful
Beyond helping students choose what to study, this technology also has the power to influence what universities choose to offer. If data consistently shows that a particular programme has little connection to current or emerging job markets, it is worth investigating. It does not mean the course should be cut. There may be academic or social reasons to preserve it, but it does mean the institution is equipped with the intelligence needed to make informed decisions.
It also invites a more purposeful approach to curriculum design. Are we including this module because it is pedagogically valuable, or because it’s always been there? Are we assessing this way because it builds a skill, or because it is the easiest to administer? When you can map outcomes to employment skills, these questions become easier to answer.
Moreover, it provides a compelling framework for conversations with students, parents, and policy-makers about the value of university education. It shows that we are listening to what the world needs and responding with academic rigour and strategic intent.
Global Potential, Local Application
The skills gap is not just a UK issue; it’s a global one. The World Economic Forum reports that nearly half of all workers (66 per cent) will need reskilling by 2030. Universities worldwide are grappling with how to stay relevant in an era of automation, AI and constant disruption. Emerging AI tools offer the potential for a globally shared skills taxonomy that could, with appropriate localisation, apply anywhere.
Conclusion
As universities continue to evolve, their role as engines of economic and social mobility becomes more important than ever. To fulfil that role, we must ensure that what we teach aligns with what the world needs. That does not mean turning every degree into job training, but it does mean being thoughtful, strategic, and transparent about the skills our programmes provide.
Emerging technologies offer an exciting glimpse into a more connected, skills-aware future. They empower students to take greater control of their learning, help universities refine and align their programmes and ensure that the promise of Higher Education translates into meaningful, real-world opportunities.
After all, education is a journey. It’s time the map caught up.
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Dive Brief:
The University of Southern California plans to use layoffs and other budget austerity measures to tackle a $200 million operating deficit and gird against a massive blow to federal funding, Interim President Beong-Soo Kim said in a community message on Monday.
On top of USC’s growing budget shortfall, which ballooned from $158 million in fiscal year 2024, officials are now grappling with federal headwinds affecting the outlook for research support, student financial aid and international enrollment, Kim said.
Lower federal research funding could cost the highly selective private university $300 million — or more — each year, Kim said. “To deal decisively with our financial challenges, we need to transform our operating model, and that will require layoffs,” he said.
Dive Insight:
Kim pointed out that USC isn’t alone in making painful budget decisions — but said that didn’t make the news any easier to hear. Indeed, many other well-known research universities have also been tightening their budgets and signaling layoffs amid the Trump administration’s widespread federal grant terminations.
That includes Stanford University, a fellow California college, and Brown University, in Rhode Island, which have both signaled potential staff reductions as they contend with federal funding shifts. Boston University, another private nonprofit, recently cut 120 employees and eliminated an equal number of vacant positions to deal with those challenges.
Kim did not disclose how many employees the university plans to lay off, and a USC spokesperson did not provide more details in response to questions Tuesday. But Kim said in his message to faculty and staff that USC has also taken other measures to shore up its budget.
The university will forego merit raises for the 2026 fiscal year, has ended certain services from third parties, and tightened discretionary and travel spending. It’s also planning to sell unused properties, streamline operations and adjust pay for the most highly compensated employees.
Kim, however, said it wasn’t feasible to bank on increased tuition revenue, drawing down more on the university’s endowment or taking out additional debt.
“Each of these ‘solutions’ would simply shift our problem onto the backs of future generations of Trojans,” Kim said, referring to the university’s mascot and student body nickname.
He also noted that the university could not likely count on federal funding returning to prior norms. “While we will continue to advocate for the vital importance of research and our academic mission, we cannot rely on the hope that federal support will revert to historical levels,” he said.
Kim’s message comes just two weeks into his tenure as the college’s interim leader, making it one of his first acts.
USC depends heavily on federal research funding. In fiscal 2024, the university received $569 million for federally funded research, according to a recent FAQ posted to its website. Overall, the university brought in nearly $7.5 billion in operating revenue that year and had $7.6 billion in operating expenses.
