Tag: Graduate

Why ideas of graduate success need to catch up with portfolio careers

For many graduates in the creative industries, the question “what do you do?” has never had a simple answer.

A graduate might be holding down part-time work in a gallery, freelancing in digital design, tutoring on the side, stage managing in the summer, and selling their own work online. It’s a patchwork, a blend, a portfolio.

And yet when we measure their success through Graduate Outcomes, the official data collection exercise on graduate employment, they’re told to tick a single box. The reality of hybridity is flattened into the illusion of underemployment.

This is not a trivial issue. Policymakers rely on Graduate Outcomes (and reports based on the collection, like this year’s What do graduates do? out today) to make judgements about which subjects, courses and institutions are “succeeding” in employability terms. Yet in the creative arts, where portfolio working is both the norm and, in many ways, a strength, these categories misrepresent lived reality. The result is a story told back to government, employers and students in which creative graduates appear more precarious, less stable, and less successful than they often are.

Portfolio careers are current and they’re the future

The creative economy has been pointing towards this future for years. In What Do Graduates Do? , the creative arts overview that Elli Whitefoot and I authored, we found repeated evidence of graduates combining multiple sources of income, employment, freelancing, self-employment, often in ways that nurtured both security and creativity. The forthcoming 2025 overview by Burtin and Halfin reinforces the same point: hybridity is a structural feature, not a marginal quirk.

This hybridity is not inherently negative. Portfolio work can provide resilience, satisfaction and autonomy. As Sharland and Slesser argued in 2024, the future workforce needs creative thinkers who can move across boundaries. Portfolio careers develop precisely those capabilities. At the Advance HE Symposium earlier this year, I led a workshop on future-proofing creative graduates through AI, entrepreneurship and digital skills, all of which thrive in a portfolio setting.

Policy writers and senior leaders need to wake up quickly to realise that creative graduates are early adopters of what more of the labour market is beginning to look like. Academic staff, for example, increasingly combine research grants, teaching roles, consultancy and side projects. Tech and green industries are also normalising project-based work, short-term contracts and hybrid roles. In other words, the creative industries are not an outlier; they are a preview.

Why measurement matters

If the data system is misaligned with reality, the consequences are serious. Universities risk being penalised in performance frameworks like TEF or in media rankings if their graduates’ outcomes are deemed “poor.” Students risk being discouraged from pursuing creative courses because outcomes data suggests they are less employable. Policymakers risk designing interventions based on a caricature rather than the real graduate experience.

As Conroy and Firth highlight, employability education must learn from the present, and the present is messy, hybrid, and global. Yet our data systems remain stuck in a single-job paradigm.

The wider sector context is equally pressing. Graduate vacancies have collapsed from around 180,000 in 2023 to just 55,000 this year, according to Reed. Almost seven in ten undergraduates are now working during term-time just to keep going according to the latest student academic experience survey. And international graduates face higher unemployment rates, around 11 per cent, compared with 3 per cent for UK PGT graduates. The labour market picture is not just challenging, it is distorted when portfolio working is coded as failure.

Without intervention, this issue will persist. Not because creative graduates are difficult to track, but because our measurement tools are still based on outdated assumptions. It is therefore encouraging that HESA is taking steps to improve the Graduate Outcomes survey questionnaire through its cognitive testing exercise. I am currently working with HESA and Jisc to explore how we can better capture hybrid and portfolio careers. These efforts will help bridge the gap in understanding, but far more nuanced data is needed if we are to fully represent the complex and evolving realities of creative graduates.

So what should change?

Data collection needs to become more granular, capturing the combination of employment, self-employment, freelancing and further study rather than forcing graduates into a false hierarchy. Recognising hybridity would make Graduate Outcomes a more accurate reflection of real graduate lives.

One complicating factor is that students who do not complete a creative programme, for example, those who transfer courses or graduate from non-creative disciplines but sustain a creative portfolio, are even less likely to record or recognise that work within Graduate Outcomes. Because it isn’t linked to their area of study, they rarely see it as a legitimate graduate destination, and valuable evidence of creative contribution goes uncounted.

We also need to value more than salary. The “graduate premium” may be shrinking in monetary terms, but its non-monetary returns, civic participation, wellbeing, and resilience, are expanding. Research from Firth and Gratrick in BERA Bites identifies clear gaps in how universities support learners to develop and articulate these broader forms of employability.

Evidence must also become richer and longer-term. The work of Prospects Luminate, AGCAS CITG and the Policy and Evidence Centre on skills mismatches shows that snapshot surveys are no longer sufficient. Graduates’ careers unfold over years, not months, and portfolio working often evolves into sustainable, fulfilling trajectories.

Beyond the UK there are instructive examples of how others have rethought the link between learning and employability. None offers a perfect model for capturing the complexity of graduate working lives, but together they point the way. The Netherlands Validation of Prior Learning system recognises skills gained from outside formal education, Canada’s ELMLP platform connects education and earnings data to map real career pathways, and Denmarks register-based labour statistics explicitly track people holding more than one job. If the UK continues to rely on outdated, single-job measures, it risks being left behind.

Beyond the creative industries

This is not an argument limited to art schools or design faculties. The wider labour market is moving in the same direction. Skills-based hiring is on the rise, with employers in AI and green sectors already downplaying traditional degree requirements in favour of demonstrable competencies. Academic precarity is, in effect, a form of portfolio career. The idea of a single linear graduate role is increasingly a historical fiction.

In this context, the creative industries offer higher education a lesson. They have been navigating portfolio realities for decades. Rather than treating this as a problem to be solved, policymakers could treat it as a model to be understood.

The full beauty of graduate success

When we collapse a graduate’s career into a single tick-box, we erase the full beauty of what they are building. We turn resilience into precarity, adaptability into instability, creativity into failure.

If higher education is serious about employability, we need to update our measures to reflect reality. That means capturing hybridity, valuing breadth as well as salary, and designing policy that starts with the lived experiences of graduates rather than the convenience of categories.

Portfolio careers are not the exception. They are the shape of things to come. And higher education, if it is to remain relevant, must learn how to see them clearly.

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November 25, 2025
Can there ever be a definitive graduate premium?

The idea of a graduate premium is a central plank of the way the Westminster government justifies the level of tuition fees, the existence of maintenance loans, and the design of an increasingly punishing repayment system based on earnings.

In essence we tell applicants that they will earn more on average, so they will pay more for the privilege of study.

One policy question that urgently needs attention is whether the graduate premium in an expanding and diverse system is equal to the task of supporting increasingly onerous repayments – and how much (or how little) of this debt needs to be waived because of low graduate salaries in certain industries.

We should not fall into the trap of equating low salaries with the “worth” of undergraduate study: however poorly we pay them we need the army of graduates that run the public sector, and even the industrial strategy admits that without the (infamously low pay) creative industries we may as well pack up the idea of civilisation and go home.

But we do need to think about whether the system as a whole stacks up in periods like we have been living through – low wage growth overall and high interest rates. And at this point the graduate repayment (annual earnings) threshold isn’t far off the annualised minimum wage.

The minimum

The national minimum wage, since 1999, has set hourly lower limits on pay at various age points.

Compliance is high among employers (though not complete: ONS estimates around 447,000 or 1.5 per cent of all jobs held by those aged 16 or over were paid below the relevant minimum wage). It has raised earnings among the very lowest paid in society.

It has probably been the single most transformative means of addressing poverty in recent times: in most years since the minimum has risen beyond inflation – in real terms the value of the higher rate has increased by 77 per cent since it was introduced.

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Over a period where wages more generally have largely stagnated in real terms this is a remarkable uplift – and it is to the credit of governments of all stripes that this policy of direct and tangible improvements to low pay has continued through multiple economic downturns.

But is it possible that a large increase in the earnings of the lowest decile will have an impact on the way we understand the earnings benefits that a degree could bring?

Certainly if we plot the minimum wage against income percentiles (these are gross figures, at 2016 prices) it is notable how close its value has crept to the tenth percentile of income, suggesting that earnings at the lower end of the spectrum are now bunching at a higher real-terms level.

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The question has to be what, if any, impact this has on the graduate earnings premium and thus repayments.

Low earners and graduates

Currently around 10 per cent of those in employment are paid an hourly wage equivalent to the national minimum wage. If this rate of pay was linked to a full time role (eight hours a day for each of the 253 annual working days in England) it would make for annual earnings of around £24,700.

However, workers on a low hourly wage are more likely to be on part-time hours, while we also know that the likelihood of you holding a full time job increases in line with the highest qualification you hold.

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The jobs involved are more likely to be elementary roles. In the main, jobs like this are primarily held by those with lower level qualifications, or no qualifications at all.

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Conversely, jobs done by graduates are far more likely to be full time, and are more likely to be managerial, professional, and associated professional roles – what the Office for Students calls “graduate jobs” than those with other levels of qualification. Around 60 per cent of graduates are in these roles, compared with around 27 per cent of those with level 3 qualifications (two A levels, so enough to have the option to attend some kind of higher education).

Strikingly, the number (not the proportion) of graduates in “non-graduate” jobs is broadly similar to the number of those qualified to level 3 with “non-graduate” jobs.

LEO and the minimum wage

Instinctually, you’d expect a graduate to be earning comfortably above what is set at a national minimum for reasons of avoiding worker poverty. For this reason, it is fair to assume that gross earnings below the minimum wage relate to part-time work. The canonical failing of LEO is that it doesn’t differentiate between part-time and full-time work, but from the Census (so, 2020–21 issues apply to a certain extent) we know that graduates are less likely to be in part-time work (and more likely to be working at all) than all other groups.

However, there are industry-based differences, and it is reasonable to assume that subject-based differences between earnings are derived from these. To give one obvious example, part-time work is a huge deal in creative and performing arts – so a lower than expected graduate salary in subjects like these would suggest that graduates are participating (at low/no pay) in the industry they have trained for and supporting this with part-time work.

With this caveat in mind, I have plotted LEO earnings against income percentiles for the whole working population and the value of the national minimum wage, all indexed to 2016 prices. The available LEO data extends from 2016 through to 2022, and in the latter year salaries across the economy experienced a real-terms downturn – something which (as we see from the chart above) has been cancelled out over the past few years.

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The two filters allow you to choose a subject area of interest, and to look at graduate gross earnings 1,3,5, and 10 years after graduation for each tax year.

The median gross earnings of graduates is slightly above the median gross earnings of all earners (all ages, all levels of qualification) after ten years – though there is substantial industry-driven variation by subject. After one year (so comparing the gross earnings of 21-22 year olds with national averages) graduate earnings are around the lower quartile – and the intervening years see the difference between the two gradually bridged.

Recall here that graduates are included within the percentile values – we are not looking here at a premium over non-graduates but a premium when compared to all earners. At the end of the day graduates are probably more concerned with the buying power of their own earnings than whether they are doing better than non-graduates.

And, given how close the minimum wage is to the repayment threshold, looking at the premium over the minimum wage (in cash term) is probably a more reasonable thing to do than I would have thought back at the birth of LEO.

We know prior attainment is one indicator of future salary (mostly as an indicator of deprivation more generally) so hear is a visualisation that plots LEO by prior attainment against the annualised minimum wage.

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How earnings are annualised in LEO

The temptation with LEO is to read the figures as salaries, and to be fair the presentation of the data does everything it possibly can to encourage that reading. But inside the sausage machine, things are very different.

The medians and quartiles familiar to us are based on individual graduate tax records for pay as you earn (PAYE, usually used by people in employment) and self assessment (SA, usually used by freelancers and the self-employed).

With PAYE, earnings for a given tax year are divided by the number of days of employment recorded, to give an average daily wage. This is then multiplied by the number of working days in a tax year (which would appear to be different across the UK due to differing numbers of bank holidays: so 253 in England, 252 in Scotland, and 251 in Northern Ireland) to give annualised earnings.

Because SA doesn’t offer dates of employment, LEO just uses the raw earnings. Annualised PAYE and raw SA income are then added together to give the final figure for each graduate, which are then used to produce the median and quartile data that is published.

Another way

I chanced upon some Labour Force Statistics data which neatly cuts across this issue by using gross hourly pay (and as luck would have it, broken down by NUTS3 regions over a number of years) as a measure of earnings. Big thanks to the ONS team for answering my questions on this one, and offering me information on the numbers in each group and an extra year of data.

Now, LFS isn’t half as good as administrative data – it is a large, representative, survey of UK residents which has been dogged by low response rates in recent years – but it was, at the time, official statistics and thus is worth taking reasonably seriously. We do get two big benefits – the first is with hourly earnings we can confirm like with like, rather than needing to compensate for differing patterns of work; while the second is we get some regional data.

A note of caution on that latter one – I’d be looking at the UK wide figures more closely as the NUTS3 regions (roughly equivalent to a top level local authority) may have quite low numbers of workers in each group (see the tooltips).

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What jumps out at me here is a clear and substantial wage premium for being a graduate, both nationally and in pretty much any area of the country. This largely holds against any qualification group of comparators, against average hourly earnings for everyone, and (very much) against the national minimum wage for the year in question. If you include loan repayments (take nine per cent off the hourly gross) there are a handful of areas of the UK where graduates are paid less than those with level three qualifications – and these largely map to other measures of deprivation.

You would expect a result like that given what we know about the impact of place on income and the tendency among graduates to move to maximise opportunities and earnings. But even so, national premiums do hold up and appear to be broadly stable or growing since 2018. You can see the impact of the pandemic here – where graduate earnings overall remained stronger during 2020 and 2021.

I should note here again that if you compare graduates with all earners, you are including the graduates themselves on both sides of the equation.

Reasons to be GLMS

Now you are probably ahead of me here, but the government used to do a graduate focused look at labour force survey data – imaginatively enough, called “graduate labour market statistics” (GLMS). I say “used to” because the 2024 iteration (released in summer 2025) is to be the last one ever. There’s an open consultation (follow the link) if you have thoughts on that – but you need to hurry, as responses are requested by the start of next month.

The ostensible reason for discontinuing GLMS is the problems faced by LFS – the falling number of responses leading to issues with sample variability. Since 2024 it has been badged as “official statistics in development” (meaning that testing of quality, volatility, and an ability to meet user needs is underway), while improvements have been made that affect data throughout 2023 and 2024. From 2025 these improvements are fully in effect, and from 2026 a new “transformed labour force survey” (TLFS) will be the means by which ONS generates its whole suite of employment data.

GLMS has clearly had some recent issues (although to be clear, these issues have not had a meaningful impact on the published national level data) but the data above suggests that it does have the potential (with appropriate caveats) to provide a more nuanced look at qualification level and regional data. Certainly, comparing the graduate population with those who hold at least the two A levels or equivalent that could get them into higher education feels like a simple and meaningful comparison we could learn from.

A transformed LEO?

If we are interested in graduate earnings premiums, the most useful thing that could be included in future LEO releases is hourly earnings. This would neatly address the part-time work issue, and focus directly on earning power rather than working patterns (which may vary for a number of reasons).

Of course, earnings are only one part of the benefit of being a graduate – and for some (I’m looking at my creative peers here) the ability to make enough money to live on by doing the thing they love is probably going to be a bigger incentive than the ability to earn more than their neighbour. That’s not to say the salary data isn’t important for them to see, but telling me that I won’t earn much as a musician is not going to stop me from wanting to study music.

That said, it does appear that (over the last few years at least) median graduate earnings have remained stable (or grown slightly) in real terms when compared to a given percentile of income tax payers. This isn’t a fair comparison – in that LEO data includes non-taxpayers and this particular HMRC data does not, but as a benchmarking tool it is interesting. By default I’m showing all but the top 10 percentiles of taxpayer income, alongside LEO by subject, and the minimum wage (all at 2016 prices).

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We know in LEO that a number of factors influence earnings: provider and subject (yes), but also prior disadvantage (of which prior attainment is one visible metric), sex, industry of employment (an economist will earn more in a bank than in a university), and region of employment. And if you control for all of these factors you are not going to get big enough groups to make statistically valid observations.

All of which is a rather maths-heavy way of saying that past performance does not tell us a great deal about the future career prospects and earnings of a single applicant chosen at random. Looking at very broad, national, figures suggests to me that a boost in earning power (which grows throughout your career) is available for three years of study – but I would caveat that by saying if your sole interest in higher study is to increase your earning power then there are other metrics available that could help you maximise this particular benefit.

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November 24, 2025
International Graduate Student Enrollment Drops

Master’s programs at some college saw steep declines in international student enrollment.

Photo illustration by Justin Morrison/Inside Higher Ed | skynesher/E+/Getty Images

Federal actions to limit immigration have affected many international students’ decision to enroll at U.S. colleges and universities this fall, with several institutions reporting dramatic declines in international student enrollment.

New data from the Department of Homeland Security from the Student Exchange and Visitor Information System for October shows an overall 1 percent decline of all international students in the U.S. SEVIS data includes all students on F-1 and M-1 visas, including those enrolled in primary and secondary school, language training, flight school, and other vocational programs.

According to DHS data, bachelor’s degree enrollment among international students is down 1 percent from October 2024 to October 2025; master’s degree enrollment is down 2 percent, as well. Associate degree programs have 7 percent more international students in October 2025 than the year prior, and international doctoral students are up 2 percent.

Campus-level data paints a more dramatic picture; an Inside Higher Ed analysis of self-reported graduate international student enrollment numbers from nine colleges and universities finds an average year-over-year decline of 29 percent.

Some groups, including NAFSA, the association for international educators, have published predictions of how international student enrollment would impact colleges’ enrollment and financial health. NAFSA expected to see a 15 percent decline across the sector and greater drops for master’s degree programs.

“Master’s [programs] have been very hit. And in addition to master’s being hit, programs like computer sciences and STEM in particular have been mostly affected,” NAFSA CEO Fanta Aw said in a Sept. 19 interview with Inside Higher Ed.

At the University of Wisconsin at Madison, for example, master’s degree enrollment dropped 22 percent from fall 2024. Ph.D. program enrollment declined only 1 percent compared to the year prior, according to university data.

While more selective or elite institutions have mostly weathered enrollment declines among undergraduate international students—reporting little or no change to their enrollment numbers this fall—Aw says graduate student enrollment is down everywhere.

The University of Pennsylvania’s Wharton School of Business, for example, reported that international students made up 26 percent of its incoming master’s in business administration class, down five percentage points from the year prior, as reported by Poets and Quants (Poets and Quants is also owned by Times Higher Education, Inside Higher Ed’s parent company). At Duke’s Fuqua School of Business, 47 percent of the incoming class in 2024 hailed from other nations, but that figure dropped to 38 percent this fall.

Because master’s degrees are shorter programs than undergraduate ones, averaging two years, Aw anticipates universities to see even more dramatic declines from 2024 in fall 2026.

“The current environment is still too uncertain for [graduate] students to even consider potentially applying,” Aw said. “You cannot have enrollment if they’re not even applying.”

Of colleges in the data set, Northwest Missouri State University reported the greatest year-over-year decline in graduate student enrollment, falling from 557 international students in fall 2024 to 125 in fall 2025. In April, Northwest Missouri State reported that 43 of its international students had their SEVIS statuses revoked; 38 of them were on optional practical training.

At that time, Northwest Missouri State encouraged students who lost their SEVIS status to depart the U.S. immediately “to avoid accruing unlawful presence,” according to a memo from President Lance Tatum published by Fox 4 Kansas City. The university declined to comment for this piece.

Nationwide, international students make up 22 percent of all full-time graduate students, according to Integrated Postsecondary Education Data System data. International students often pay higher tuition rates compared to their domestic peers, and some colleges rely on international students to boost graduate program enrollment.

The dramatic changes in enrollment numbers are having budgetary impacts on some colleges.

At Georgetown University, foreign graduate student enrollment dropped 20 percent, which was expected but steeper than anticipated, according to a memo from interim university president Robert M. Groves. In April, Georgetown cut $100 million from its budget due to loss of federal research dollars and international student revenue, and Groves said more cuts may be needed in December.

DePaul University in Chicago saw a 63 percent year-over-year decline in new graduate students from other nations—a sharp drop that administrators, similarly, did not anticipate in this year’s budget.

As more colleges solidify their fall enrollment numbers, the sectorwide decline in foreign students has become more clear.

Inside Higher Ed’s initial data found colleges reported, on average, a 13 percent decrease in international student enrollment. The median year-over-year change was a 9 percent drop.

Small colleges saw significant changes. Bethany Lutheran College in Minnesota, with a total head count of 900 students, reported a 50 percent growth in international students. At the other end, the University of Hartford in Connecticut lost half of its international students, only expecting 50 instead of 100 this fall.

Community colleges are also feeling the loss of international students. Bellevue College in Washington State, a leading destination for international students in the two-year sector, reported a 56 percent year-over-year decline in enrollment.

Southeast Missouri State reported a 63 percent decline in international students, with 494 individuals unable to secure visas, according to a university statement.

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November 6, 2025
How generative AI could re-shape professional services and graduate careers

Join HEPI and the University of Southampton for a webinar on Monday 10 November 2025 from 11am to 12pm to mark the launch of a new collection of essays, AI and the Future of Universities. Sign up now to hear our speakers explore the collection’s key themes and the urgent questions surrounding AI’s impact on higher education.

This blog was kindly authored by Richard Brown, Associate Fellow at the University of London’s School of Advanced Study.

Universities are on the front line of a new technological revolution. Generative AI (genAI) use (mainly large language mode-based chatbots like ChaptGPT and Claude) is almost universal among students. Plagiarism and accuracy are continuing challenges, and universities are considering how learning and assessment can respond positively to the daunting but uneven capabilities of these new technologies.

How genAI is transforming professional services

The world of work that students face after graduation is also being transformed. While it is unclear how much of the current slowdown in graduate recruitment can be attributed to current AI use, or uncertainty about its long-term impacts, it is likely that graduate careers will see great change as the technology develops. Surveys by McKinsey indicate that adoption of AI spread fastest between 2023/24 in media, communications, business, legal and professional services – the sectors with the highest proportions of graduates in their workforce (around 80 per cent in London and 60 per cent in the rest of the UK).

‘Human-centric’, a new report from the University of London looks at how AI is being adopted by professional service firms, and at what this might mean for the future shape and delivery of higher education.

The report identifies how AI is being adopted both through grassroots initiatives and corporate action. In some firms, genAI is still the preserve of ‘secret cyborgs’ – individual workers using chatbots under the radar. In others, task forces of younger workers have been deployed to find new uses for the tech to tackle chronic workflow problems or develop new services. Lawyers and accountants are codifying expertise into proprietary knowledge bases. These are private chatbots that minimise the risks of falsehood that still plague open systems, and offer potential to extend cheap professional-grade advice to many more people.

Graduate careers re-thought

What does this mean for graduate employment and skills? Many of the routine tasks frequently allocated to graduates can be automated through AI. This could be a doubled-edged sword. On the one hand, genAI may open up more varied and engaging ways for graduates to develop their skills, including the applied client-facing and problem-solving capabilities that underpin professional practice.

On the other hand, employers may question whether they need to employ as many graduates. Some of our interviewees talked of the potential for the ‘triangle’ structure of mass graduate recruitment being replaced by a ‘diamond-shaped’ refocus on mid-career hires. The obvious problem with this approach – of where mid-career hires will come from if there is no graduate recruitment – means that graduate recruitment is unlikely to dry up in the short term, but graduate careers may look very different as the knowledge economy is transformed.

The agile university in an age of career turbulence

This will have an impact on universities as well as employers. AI literacy, and the ability to use AI responsibly and authentically, are likely to become baseline expectations – suggesting that this should be core to university teaching and learning. Intriguingly, this is less about traditional computing skills and more about setting AI in context: research shows that software engineers were less in demand in early 2025 than AI ethicists and compliance specialists.

Broader ‘soft’ skills (what a previous University of London / Demos report called GRASP skills – general, relational, analytic, social and personal) will remain in demand, particularly as critical judgement, empathy and the ability to work as a team remain human-centric specialities. Employers also said that, while deep domain knowledge was still needed to assess and interrogate AI outputs, they were also looking for employees with a broader understanding of issues such as cybersecurity, climate regulation and ESG (Environmental, Social, and Governance), who could work across diverse disciplines and perspectives to create new knowledge and applications.

The shape of higher education may also need to change. Given the speed of advances in AI, it is likely that most propositions about which skills will be needed in the future may quickly become outdated (including this one). This will call for a more responsive and agile system, which can experiment with new course content and innovative teaching methods, while sustaining the rigour that underpins the value of their degrees and other qualifications.

As the Lifelong Learning Entitlement is implemented, the relationship between students and universities may also need to become more long-term, rather than an intense three-year affair. Exposure to the world of work will be important too, but this needs to be open to all, not just to those with contacts and social capital.

Longer term – beyond workplace skills?

In the longer term, all bets are off, or at least pretty risky. Public concerns (over everything from privacy, to corporate control, to disinformation, to environmental impact) and regulatory pressures may slow the adoption of AI. Or AI may so radically transform our world that workplace skills are no longer such a central concern. Previous predictions of technology unlocking a more leisured world have not been realised, but maybe this time it will be different. If so, universities will not just be preparing students for the workplace, but also helping students to prepare for, shape and flourish in a radically transformed world.

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November 5, 2025
What might lower response rates mean for Graduate Outcomes data?

The key goal of any administered national survey is for it to be representative.

That is, the objective is to gather data from a section of the population of interest in a country (a sample), which then enables the production of statistics that accurately reflect the picture among that population. If this is not the case, the statistic from the sample is said to be inaccurate or biased.

A consistent pattern that has emerged both nationally and internationally in recent decades has been the declining levels of participation in surveys. In the UK, this trend has become particularly evident since the Covid-19 pandemic, leading to concerns regarding the accuracy of statistics reported from a sample.

A survey

Much of the focus in the media has been on the falling response rates to the Labour Force Survey and the consequences of this on the ability to publish key economic statistics (hence their temporary suspension). Furthermore, as the recent Office for Statistics Regulation report on the UK statistical system has illustrated, many of our national surveys are experiencing similar issues in relation to response rates.

Relative to other collections, the Graduate Outcomes survey continues to achieve a high response rate. Among the UK-domiciled population, the response rate was 47 per cent for the 2022-23 cohort (once partial responses are excluded). However, this is six percentage points lower than what we saw in 2018-19.

We recognise the importance to our users of being able to produce statistics at sub-group level and thus the need for high response rates. For example, the data may be used to support equality of opportunity monitoring, regulatory work and understand course outcomes to inform student choice.

So, HESA has been exploring ways in which we can improve response rates, such as through strategies to boost online engagement and offering guidance on how the sector can support us in meeting this aim by, for example, outlining best practice in relation to maintaining contact details for graduates.

We also need, on behalf of everyone who uses Graduate Outcomes data, to think about the potential impact of an ongoing pattern of declining response rates on the accuracy of key survey statistics.

Setting the context

To understand why we might see inaccurate estimates in Graduate Outcomes, it’s helpful to take a broader view of survey collection processes.

It will often be the case that a small proportion of the population will be selected to take part in a survey. For instance, in the Labour Force Survey, the inclusion of residents north of the Caledonian Canal in the sample to be surveyed is based on a telephone directory. This means, of course, that those not in the directory will not form part of the sample. If these individuals have very different labour market outcomes to those that do sit in the directory, their exclusion could mean that estimates from the sample do not accurately reflect the wider population. They would therefore be inaccurate or biased. However, this cause of bias cannot arise in Graduate Outcomes, which is sent to nearly all those who qualify in a particular year.

Where the Labour Force Survey and Graduate Outcomes are similar is that submitting answers to the questionnaire is optional. So, if the activities in the labour market of those who do choose to take part are distinct from those who do not respond, there is again a risk of the final survey estimates not accurately representing the situation within the wider population.

Simply increasing response rates will not necessarily reduce the extent of inaccuracy or bias that emerges. For instance, a survey could achieve a response rate of 80 per cent, but if it does not capture any unemployed individuals (even when it is well known that there are unemployed people in the population), the labour market statistics will be less representative than a sample based on a 40 per cent response rate that captures those in and out of work. Indeed, the academic literature also highlights that there is no clear association between response rates and bias.

It was the potential for bias to arise from non-response that prompted us to commission the Institute for Social and Economic Research back in 2021 to examine whether weighting needed to be applied. Their approach to this was as follows. Firstly, it was recognised that for any given cohort, it is possible that the final sample composition could have been different had the survey been run again (holding all else fixed). The sole cause of this would be a change in the group of graduates who choose not to respond. As Graduate Outcomes invites almost all qualifiers to participate, this variation cannot be due to the sample randomly chosen to be surveyed being different from the outset if the process were to be repeated – as might be the case in other survey collections.

The consequence of this is that we need to be aware that a repetition of the collection process for any given cohort could lead to different statistics being generated. Prior to weighting, the researchers therefore created intervals – including at provider level – for the key survey estimate (the proportion in highly skilled employment and/or further study) which were highly likely to contain the true (but unknown) value among the wider population. They then evaluated whether weighted estimates sat within these intervals and concluded that if they did, there was zero bias. Indeed, this was what they found in the majority of cases, leading to them stating that there was no evidence of substantial non-response bias in Graduate Outcomes.

What would be the impact of lower response rates on statistics from Graduate Outcomes?

We are not the only agency running a survey that has examined this question. Other organisations administering surveys have also explored this matter too. For instance, the Scottish Crime and Justice Survey (SCJS) has historically had a target response rate of 68 per cent (in Graduate Outcomes, our target has been to reach a response rate of 60 per cent for UK-domiciled individuals). In SCJS, this goal was never achieved, leading to a piece of research being conducted to explore what would happen if lower response rates were accepted.

SCJS relies on face-to-face interviews, with a certain fraction of the non-responding sample being reissued to different interviewers in the latter stages of the collection process to boost response rates. For their analysis, they looked at how estimates would change had they not reissued the survey (which tended to increase response rates by around 8-9 percentage points). They found that choosing not to reissue the survey would not make any material difference to key survey statistics.

Graduate Outcomes data is collected across four waves from December to November, with each collection period covering approximately 90 days. During this time, individuals have the option to respond either online or by telephone. Using the 2022-23 collection, we generated samples that would lead to response rates of 45 per cent, 40 per cent and 35 per cent among the UK-domiciled population by assuming the survey period was shorter than 90 days. Similar to the methodology for SCJS therefore, we looked at what would have happened to our estimates had we altered the later stages of the collection process.

From this point, our methodology was similar to that deployed by the Institute for Social and Economic Research. For the full sample we achieved (i.e. based on response rate of 47 per cent), we began by generating intervals at provider level for the proportion in highly skilled employment and/or further study. We then examined whether the statistic observed at a response rate of 45 per cent, 40 per cent and 35 per cent sat within this interval. If it did, our conclusion was there was no material difference in the estimates.

Among the 271 providers in our dataset, we found that, at a 45 per cent response rate, only one provider had an estimate that fell outside the intervals created based on the full sample. This figure rose to 10 (encompassing 4 per cent of providers) at a 40 per cent response rate and 25 (representing 9 per cent of providers) at a 35 per cent response rate, though there was no particular pattern to the types of providers that emerged (aside from them generally being large establishments).

What does this mean for Graduate Outcomes users?

Those who work with Graduate Outcomes data need to understand the potential impact of a continuing trend of lower response rates. While users can be assured that the survey team at HESA are still working hard to achieve high response rates, the key-take away message from our study is that a lower response rate to the Graduate Outcomes survey is unlikely to lead to a material change in the estimates for the proportion in highly skilled employment and/or further study among the bulk of providers.

The full insight and associated charts can be viewed on the HESA website:
What impact might lower response rates have had on the latest Graduate Outcomes statistics?

Read HESA’s latest research releases. If you would like to be kept updated on future publications, please sign-up to our mailing list.

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November 4, 2025
How rare are colleges that enroll and graduate high shares of Pell Grant students?

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When it comes to colleges where Pell Grant recipients are at least 55% likely to graduate, there are not a whole lot throughout the U.S. In fact, nearly half of states — many of them Southern with some of the highest poverty rates in the country — don’t have any at all.

That’s what Becca Spindel Bassett, higher education professor at the University of Arkansas, discovered in a recent analysis in which she sought to identify and map institutions of higher education that she describes as “equity engines.”

These are colleges where at least 34% of the students receive Pell Grants and at least 55% of those Pell Grant recipients earn a bachelor’s degree within six years.

Out of the 1,584 public and private nonprofit four-year institutions that Bassett studied nationwide, she found only 91 — or less than 6% — that qualified for her “equity engine” distinction.

And they’re all clustered in 26 states, resulting in what Bassett calls a “spatial injustice” for low-income students who live in one of the states without any equity engines or in areas with limited access to such institutions.

The almost eight dozen existing equity engines represent a diverse range of institutional types, including regional public universities, small Christian colleges and historically Black institutions.

As for whether states can invest more in colleges that are close to being equity engines — a key recommendation of Bassett’s study — it all depends.

“It’s worth noting that over half of Equity Engines are private colleges and universities, so their relationship to the state and dependency on state funding varies,” Bassett said in an email to Higher Ed Dive.

But improving Pell graduation rates isn’t only a question of funding models, she said.

Leaders at aspiring equity engines can learn best practices and approaches from these colleges and should be prepared to enact “organizational learning and change,” Bassett said. However, much is unknown about what enables colleges to become equity engines, including whether it depends on their programs and services or their policy and funding environments.

While Bassett’s study doesn’t answer those questions, a forthcoming book will describe how two of the colleges she identified as equity engines were able to achieve their results, she said.

Michael Itzkowitz, founder and president of the HEA Group, a higher ed-focused research firm and consultancy, said in an email that identifying colleges with strong graduation rates is a “good first step” because students who earn a degree “typically earn more than those who do not.”

However, Itzkowitz, who under former President Barack Obama served as the director of The College Scorecard — an online federal tool with various data on higher education institutions — added that it’s also critical to consider whether graduates are actually better off economically since “not all institutions and degrees are created equal.”

“Students who earn a credential at one institution may experience wildly different outcomes if they earned the same degree elsewhere,” he said.

David Hawkins, chief education and policy officer at the National Association for College Admission Counseling, said in an email that colleges would do well to emulate the equity engines Bassett identified, such as the University of Illinois Chicago. Bassett’s study calls the university a “major driver” of bachelor’s degree completion among Pell Grant recipients in the state, noting those students have a 58% six-year graduation rate.

Among other things, Hawkins said, such institutions deploy a wide range of services — such as evening or online courses for working students, and transportation to campus — that have been proven to help low-income students cross the finish line.

“From my perspective, the United States will only remain competitive if we can invest in a postsecondary infrastructure that serves all students who seek opportunity through higher education,” Hawkins said.

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October 22, 2025
Framework for GenAI in Graduate Career Development (opinion)

In Plato’s Phaedrus, King Thamus feared writing would make people forgetful and create the appearance of wisdom without true understanding. His concern was not merely about a new tool, but about a technology that would fundamentally transform how humans think, remember and communicate. Today, we face similar anxieties about generative AI. Like writing before it, generative AI is not just a tool but a transformative technology reshaping how we think, write and work.

This transformation is particularly consequential in graduate education, where students develop professional competencies while managing competing demands, research deadlines, teaching responsibilities, caregiving obligations and often financial pressures. Generative AI’s appeal is clear; it promises to accelerate tasks that compete for limited time and cognitive resources. Graduate students report using ChatGPT and similar tools for professional development tasks, such as drafting cover letters, preparing for interviews and exploring career options, often without institutional guidance on effective and ethical use.

Most AI policies focus on coursework and academic integrity; professional development contexts remain largely unaddressed. Faculty and career advisers need practical strategies for guiding students to use generative AI critically and effectively. This article proposes a four-stage framework—explore, build, connect, refine—for guiding students’ generative AI use in professional development.

Professional Development in the AI Era

Over the past decade, graduate education has invested significantly in career readiness through dedicated offices, individual development plans and co-curricular programming—for example, the Council of Graduate Schools’ PhD Career Pathways initiative involved 75 U.S. doctoral institutions building data-informed professional development, and the Graduate Career Consortium, representing graduate-focused career staff, grew from roughly 220 members in 2014 to 500-plus members across about 220 institutions by 2022.

These investments reflect recognition that Ph.D. and master’s students pursue diverse career paths, with fewer than half of STEM Ph.D.s entering tenure-track positions immediately after graduation; the figure for humanities and social sciences also remains below 50 percent over all.

We now face a different challenge: integrating a technology that touches every part of the knowledge economy. Generative AI adoption among graduate students has been swift and largely unsupervised: At Ohio State University, 48 percent of graduate students reported using ChatGPT in spring 2024. At the University of Maryland, 77 percent of students report using generative AI, and 35 percent use it routinely for academic work, with graduate students more likely than undergraduates to be routine users; among routine student users, 38 percent said they did so without instructor guidance.

Some subskills, like mechanical formatting, will matter less in this landscape; higher-order capacities—framing problems, tailoring messages to audiences, exercising ethical discernment—will matter more. For example, in a 2025 National Association of Colleges and Employers survey, employers rank communication and critical thinking among the most important competencies for new hires, and in a 2024 LinkedIn report, communication was the most in-demand skill.

Without structured guidance, students face conflicting messages: Some faculty ban AI use entirely, while others assume so-called digital natives will figure it out independently. This leaves students navigating an ethical and practical minefield with high stakes for their careers. A framework offers consistency and clear principles across advising contexts.

We propose a four-stage framework that mirrors how professionals actually learn: explore, build, connect, refine. This approach adapts design thinking principles, the iterative cycle of prototyping and testing, to AI-augmented professional development. Students rapidly generate options with AI support, test them in low-stakes environments and refine based on feedback. While we use writing and communication examples throughout for clarity, this framework applies broadly to professional development.

Explore: Map Possibilities and Surface Gaps

Exploring begins by mapping career paths, fellowship opportunities and professional norms, then identifying gaps in skills or expectations. A graduate student can ask a generative AI chatbot to infer competencies from their lab work or course projects, then compare those skills to current job postings in their target sector to identify skills they need to develop. They can generate a matrix of fellowship opportunities in their field, including eligibility requirements, deadlines and required materials, and then validate every detail on official websites. They can ask AI to describe communication norms in target sectors, comparing the tone and structure of academic versus industry cover letters—not to memorize a script, but to understand audience expectations they will need to meet.

Students should not, however, rely on AI-generated job descriptions or program requirements without verification, as the technology may conflate roles, misrepresent qualifications or cite outdated information and sources.

Build: Learn Through Iterative Practice

Building turns insight into artifacts and habits. With generative AI as a sounding board, students can experiment with different résumé architectures for the same goal, testing chronological versus skills-based formats or tailoring a CV for academic versus industry positions. They can generate detailed outlines for an individual development plan, breaking down abstract goals into concrete, time-bound actions. They can devise practice tasks that address specific growth areas, such as mock interview questions for teaching-intensive positions or practice pitches tailored to different funding audiences. The point is not to paste in AI text; it is to lower the barriers of uncertainty and blank-page intimidation, making it easier to start building while keeping authorship and evidence squarely in the student’s hands.

Connect: Communicate and Network With Purpose

Connecting focuses on communicating with real people. Here, generative AI can lower the stakes for high-pressure interactions. By asking a chatbot to act the part of various audience members, students can rehearse multiple versions of a tailored 60-second elevator pitch, such as for a recruiter at a career fair, a cross-disciplinary faculty member at a poster session or a community partner exploring collaboration. Generative AI can also simulate informational interviews if students prompt the system to ask follow-up questions or even refine user inputs.

In addition, students can leverage generative AI to draft initial outreach notes to potential mentors that the students then personalize and fact-check. They can explore networking strategies for conferences or professional association events, identifying whom to approach and what questions to ask based on publicly available information about attendees’ work.

Even just five years ago, completing this nonexhaustive list of networking tasks might have seemed an impossibility for graduate students with already crammed agendas. Generative AI, however, affords graduate students the opportunity to become adept networkers without sacrificing much time from research and scholarship. Crucially, generative AI creates a low-risk space to practice, while it is the student who ultimately supplies credibility and authentic voice. Generative AI cannot build genuine relationships, but it can help students prepare for the human interactions where relationships form.

Refine: Test, Adapt and Verify

Refining is where judgment becomes visible. Before submitting a fellowship essay, for example, a student can ask the generative AI chatbot to simulate likely reviewer critiques based on published evaluation criteria, then use that feedback to align revisions to scoring rubrics. They can A/B test two AI-generated narrative approaches from the build stage with trusted readers, advisers or peers to determine which is more compelling. Before a campus talk, they can ask the chatbot to identify jargon, unclear transitions or slides with excessive text, then revise for audience accessibility.

In each case, verification and ownership are nonnegotiable: Students must check references, deadlines and factual claims against primary sources and ensure the final product reflects their authentic voice rather than generic AI prose. A student who submits an AI-refined essay without verification may cite outdated program requirements, misrepresent their own experience or include plausible-sounding but fabricated details, undermining credibility with reviewers and jeopardizing their application.

Cultivate Expert Caution, Not Technical Proficiency

The goal is not to train students as prompt engineers but to help them exercise expert caution. This means teaching students to ask: Does this AI-generated text reflect my actual experience? Can I defend every claim in an interview? Does this output sound like me, or like generic professional-speak? Does this align with my values and the impression I want to create? If someone asked, “Tell me more about that,” could I elaborate with specific details?

Students should view AI as a thought partner for the early stages of professional development work: the brainstorming, the first-draft scaffolding, the low-stakes rehearsal. It cannot replace human judgment, authentic relationships or deep expertise. A generative AI tool can help a student draft three versions of an elevator pitch, but only a trusted adviser can tell them which version sounds most genuine. It can list networking strategies, but only actual humans can become meaningful professional connections.

Conclusion

Each graduate student brings unique aptitudes, challenges and starting points. First-generation students navigating unfamiliar professional cultures may use generative AI to explore networking norms and decode unstated expectations. International students can practice U.S. interview conventions and professional correspondence styles. Part-time students with limited campus access can get preliminary feedback before precious advising appointments. Students managing disabilities or mental health challenges can use generative AI to reduce the cognitive load of initial drafting, preserving energy for higher-order revision and relationship-building.

Used critically and transparently, generative AI can help students at all starting points explore, build, connect and refine their professional paths, alongside faculty advisers and career development professionals—never replacing them, but providing just-in-time feedback and broader access to coaching-style support.

The question is no longer whether generative AI belongs in professional development. The real question is whether we will guide students to use it thoughtfully or leave them to navigate it alone. The explore-build-connect-refine framework offers one path forward: a structured approach that develops both professional competency and critical judgment. We choose guidance.

Ioannis Vasileios Chremos is program manager for professional development at the University of Michigan Medical School Office of Graduate and Postdoctoral Studies.

William A. Repetto is a postdoctoral researcher in the Department of English and the research office at the University of Delaware.

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October 20, 2025
Podcast: Wales, Franchising, Graduate Jobs

This week on the podcast we look at Wales’ emerging higher education settlement, as Universities Wales publishes its manifesto for the May 2026 Senedd elections amid polling that points to a potential Plaid-led administration.

Plus we discuss new Office for Students’ data on subcontracted (franchised) provision showing weaker continuation, completion and progression outcomes relative to sector averages, and assess the Institute of Student Employers’ latest survey, with graduate hiring down overall but highly variable by sector amid persistently high applications per vacancy.

With Debbie McVitty, Editor at Wonkhe, Sarah Cowan, Head of Policy (Higher Education and Research) at the British Academy, Sarah Stevens, Director of Strategy at the Russell Group and presented by Jim Dickinson, Associate Editor at Wonkhe.

<a href="https://rss.com/podcasts/the-wonkhe-show">The Wonkhe Show | RSS.com</a>

Universities Wales election manifesto

Outcomes data for subcontracted provision

Graduate jobs and recruitment reality

You can subscribe to the podcast on Apple Podcasts, YouTube Music, Spotify, Acast, Amazon Music, Deezer, RadioPublic, Podchaser, Castbox, Player FM, Stitcher, TuneIn, Luminary or via your favourite app with the RSS feed.

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October 16, 2025
Graduate jobs and recruitment reality

Despite frequent headlines warning of large declines in graduate jobs, the Institute of Student Employers (ISE) Student Recruitment Survey 2025 shows a less severe and more nuanced reality of the entry-level recruitment market.

Our survey captures recruitment trends from 155 ISE employer members who received over 1.8m job applications for over 31,000 early careers roles. For these employers, graduate hiring has fallen by eight per cent this year, marking the weakest year for graduate hiring since the 12 per cent decline during the pandemic in 2020.

Although the ISE represents larger employers who recruit graduates onto formal training programmes, broader labour market data also shows reduced hiring which may impact students who take jobs that may not be part of a formal training programme. For example, data from the Recruitment and Employment Confederation shows a 13 per cent drop in all job adverts from July 2024 to July 2025.

However, this trend varies from sector to sector and employer to employer. ISE’s survey found that while 42 per cent of employers reduced graduate hiring levels, 25 per cent of employers maintained hiring levels – and 33 per cent reported an increase.

Looking ahead to 2025–26, we expect graduate recruitment to remain challenging as employers forecast an overall seven per cent reduction in graduate hiring, driven by sharp declines for a small number of large employers.

Rebalancing early talent programmes

Graduate programmes aren’t the only route into the UK’s top employers and investment in apprenticeships has been growing since the levy was introduced. ISE found employers are rebalancing early careers programmes with more focus on apprenticeships to meet skills demands.

While graduate hiring declined this year, school and college leaver hiring increased by eight per cent. Graduates still outnumber apprentices and therefore the overall entry-level job market is down five per cent.

This increase reflects the role of large levy-paying employers with greater resources to develop and manage apprenticeship schemes, bucking the wider market trend. Government data reports only a 0.6 per cent rise in apprenticeship starts among 19- to 24-year-olds over the past year.

The ratio of graduates to school or college leaver hiring (which is mostly apprenticeships) among ISE members who recruit students onto both pathways is 1.8 graduates for every school/college leaver hire, down from 2.3 last year. This trend looks set to continue into 2025–26 with the ratio is forecast to decline further to 1.6:1.

Despite this rebalancing, graduate hires still outnumber school and college leaver hires, and although the jobs market remains challenging, graduates remain a core element of early talent strategies.

AI impact

AI is undoubtedly reshaping the early careers recruitment sector. However, no one is telling us that AI is replacing entry level jobs (yet).

As students increasingly use AI to craft job applications, they also submit a greater number of applications, driving up competition for each role. The application to vacancy ratio remains at a historic high of 140 applications per vacancy.

The authenticity of applications from “AI-enabled candidates” has also emerged as a key employer concern. In fact, an arms race appears to be underway: only 15 per cent of employers said they never suspected or identified candidates cheating in assessments, and 79 per cent of employers are redesigning or reviewing their recruitment processes in response to AI developments.

Currently around half of employers allow candidates to use AI tools during the recruitment process, primarily for drafting covering letters and CVs and completing online application questions. Only a small proportion of employers (10 per cent) have banned the use of AI or introduced technical measures to prevent its use.

Our data also shows that 45 per cent of employers had not provided applicants with any guidance on when it was or was not appropriate to use AI. This guidance may support students in navigating their transition into a graduate role and help employers manage their application volumes.

But while students are embracing AI in their job search, the use of AI by recruiters is currently limited, but likely to grow. While over half of employers use automated systems to fully manage some aspects of testing, AI use is very rare. Employers are most likely to use AI in gamified assessments, but even here the adoption rate is only 15 per cent. Looking ahead to the next five years, more than half of employers expect to use AI in their recruitment processes, and 70 per cent anticipate increasing their use of automation.

Getting ahead

The graduate job market is challenging, reflecting the broader economic climate – but it is not without opportunity.

Students looking to get ahead should remain cautious about their prospects in their chosen career, but the graduate job market is always competitive. A job search should be treated just like a job. Applications should be authentic, considered and tailored, with a focus on quality not quantity. And work experience remains key, with employers reporting former interns better equipped with the skills that they need.

For universities, these findings highlight the importance of preparing students for a more complex and competitive graduate market through close collaboration with employers.

As employers rebalance early talent programmes and adapt to the rise of AI, institutions have a key role to play in equipping students with practical experience, adaptability, and digital literacy.

Strengthening partnerships with employers, embedding employability across the curriculum, and helping students navigate responsible AI use will be critical to ensuring graduates continue to thrive in a shifting recruitment market.

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October 15, 2025
The Motivations and Concerns of Prospective Graduate Students

Graduate student enrollment is increasingly critical to the overall enrollment health for universities. As demographic changes make it harder to grow traditional undergraduate enrollment, institutions will need graduate student population growth to fill in those gaps.

The good news is that the graduate student market is growing. According to National Student Clearinghouse data, graduate enrollment reached an all-time high of 3.2 million in fall 2024, with a 3.3% increase over the year before.

However, in order to compete for these students, you need to understand their motivations, influences, and concerns when it comes to their selection of a higher educational institution. To dig into these issues, RNL surveyed 1,400 prospective and enrolled graduate students on a wide range of issues that relate to their decision to pursue graduate study. Here are some of the key findings that enrollment managers need to know.

What is their primary motivation to study?

It’s no surprise that today’s students are career-oriented, but it’s clear that advancing their current career is the top driver, with 74% of our participants listing that as their primary motivation to study.

What does this mean for us as practitioners in higher education? It’s critical to not only highlight career-related information, but also to make sure that information and outcomes are very easy to find. In another finding from our report, 90% of respondents indicated that it’s important for program pages to provide specific and easy-to-access information on careers related to their field.

What influences graduate students to consider graduate study?

As you can see here, these decisions are largely self-motivated even if the reasons to pursue grad study are career-oriented. I find it interesting that these are not more employer-driven, especially when it comes to continuing degrees. However, it still shows that the majority of graduate students are self-motivated, intrinsic learners who see graduate study as a way to improve their lives.

What are the most important program features to prospective graduate students?

For our survey respondents, format flexibility was the feature that was cited as most important, followed closely by available specializations. This is interesting, as the respondents cited modality, course format, and specializations, and then flexible scheduling. This could be a reflection of the growing number of Gen Z students (those under 29) who make up 56% of the graduate student population according to the fall 2023 IPEDS snapshot. This change in student age demographic emphasizes the importance of offering and designing those programs for multiple delivery types and really meeting those students where they are.

What are the main concerns of graduate students?

I don’t think anyone will be shocked that cost is a concern for 60% of graduate students. But half of our respondents also cited balancing responsibilities as a primary concern. This is again, not shocking considering the vast majority of our participants said they worked full-time. While fewer than 20% cited ROI uncertainty, that still represents 1 in 5 of our survey takers. The bottom line is that institutions need to directly address these pain points when they conduct outreach with students. Mitigating some of those concerns right away can help students feel more comfortable in the process and be more likely to enroll in, and ultimately complete their programs.

What will inhibit a graduate student from applying to a program?

Finally, we asked our survey respondents which common requirements would potentially dissuade them from applying to a program.

As you can see, 1 in 3 students cited letters of recommendation and essays/personal statements. This is not to say that institutions should remove these requirements, but be mindful if your program really needs them in the evaluation process. Similarly, for items such as transcripts, look for ways to make it easier for transcripts to be submitted or gathered to remove the burden from students—and a potential barrier from applying to your program.

Read the full report for even more insights

These findings represent a fraction of what you will find in the 2025 Graduate Student Recruitment Report. It’s packed with findings on the channels graduate students use to search for schools, how they use search engines for research, which digital ads they click on, and much more.

You can also watch our webinar Keys to Engaging and Enrolling Graduate Students to hear my colleague Lori Cannistra and I discuss the findings and how you can use them to guide your strategies. And if you want to discuss graduate marketing and recruitment strategies, reach out to set up a consultation.

Talk with our graduate and online enrollment experts

Ask for a free consultation with us. We’ll help you assess your market and develop the optimal strategies for your prospective graduate students and online learners.

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September 25, 2025