Other business schools, including University of Michigan’s Ross Business School and Columbia Business School, also enforce set grade distributions.
Photo illustration by Justin Morrison/Inside Higher Ed | Ralf Geithe/iStock/Getty Images
Some faculty members at the Indiana University Kelley School of Business have been instructed to eliminate grade rounding, remove the A-plus grade option and keep average section GPAs between 3.3 and 3.5 for the fall semester.
The grading changes aim to “address grade inflation and promote rigor across our curriculum,” according to an email sent to faculty in the Communication, Professional and Computer Skills (CPS) department from business writing course coordinator Polly Graham, which was obtained by Inside Higher Ed. “During the COVID-19 pandemic, [CPS] grades elevated, and in recent years, grades have remained high. In recent semesters, some instructors have awarded 100% A’s in standard (i.e., non-honors) sections, and others have awarded extraordinary numbers of A+’s and incompletes,” the email said.
The new grading policy was sent to instructors in early August without faculty discussion or approval, according to a faculty member in the CPS department who asked to remain anonymous for fear of retribution. The department, which does not have its own governance or bylaws beyond what governs the business school writ large, is the only one in Kelley that is staffed entirely by lecturers who do not have tenure protections. So far, the new grading policies apply only to courses in the CPS department, the faculty member said.
Instructors of standard, nonhonors courses must make the GPA of each section average between 3.3 and 3.5, and honors course GPA averages must fall within 0.2 points of the “section’s cumulative student GPA,” the email stated. Faculty members should not round up final grades “even if the student’s grade is very close to a higher letter grade,” and each instructor will complete two check-ins with CPS leadership—one before and one after midterms—after which “formative support will be provided to faculty as requested or needed.” It’s unclear what form the support will take, but the faculty member suspects it could be additional assistance from the chair on lesson plans or grading strategies.
It’s not unusual for business schools to enforce a set grade distribution. At the University of Michigan’s Ross Business School, for instance, core class instructors must follow a distribution that allows 40 percent or fewer undergraduates to earn an A-minus or higher, 90 percent or fewer undergraduates to earn a B or higher, and at least 10 percent of undergraduates must earn between a B-minus and an F. Emory University’s Goizueta Business School also enforces a grade distribution, as does Columbia Business School.
The Kelley School will also enforce an attendance policy for CPS classes this fall. Students will be allowed up to three absences without a grade penalty. After the fourth absence, they lose one-third of their final letter grade, and after five absences, they lose a full letter grade. Six absences will result in an automatic “failure due to non-attendance,” the email explained. The school will allow exceptions on a case-by-case basis.
All Kelley students are required to take courses within the CPS department, including a business presentations class, a business writing course and three “Kelley Compass” classes that teach soft business skills such as team building, interviewing and conflict management. Like the lab time that accompanies physical science classes, CPS courses offer skills-based training that encourages mastery, the CPS faculty member told Inside Higher Ed. Faculty are concerned that the new GPA targets put an artificial limit on students’ success.
A spokesperson for the Kelley School did not answer Inside Higher Ed’s questions about the grade recalibration and instead provided the following statement: “At Kelley, faculty design courses to be both rigorous and fair, while supporting student development and career preparation. Our longstanding priority is to ensure that grades reflect the quality of each student’s performance and that grade distribution is fair and consistent, including across multiple sections of the same course.”
The statement language echoes what faculty have been instructed to tell students and parents who ask about the grading changes, according to the CPS faculty member.
Indiana’s Kelley School has become more popular of late, and administrators appear to be tightening admissions standards in response. The school has fielded some 27,000 applications for approximately 2,000 spots in recent years, the faculty member said, though the Kelley spokesperson did not confirm or refute these numbers.
In March, Kelley promoted Patrick E. Hopkins, an accounting professor who has worked at the business school since 1995, to dean. Just over two months later, on June 2, incoming Indiana University prebusiness students were notified that the minimum grade for automatic admission to the Kelley School would be raised from a B to a B-plus, starting with their cohort. Christopher Duff, the father of an incoming Indiana prebusiness student who plans to seek admission to Kelley, said the change was a “bait and switch.”
“To be crystal clear, I have zero issues with the Kelley School of Business changing their admission criteria. I do, however, have a major issue in the timing of this change. We made our decision based on clearly stated information at the time of commitment. We jettisoned all other schools, offers and financial aid to pursue a degree from Indiana-Kelley,” Duff told Inside Higher Ed. “You want to change the criteria? Fine. Do so with the incoming class who will be aware to make an informed decision. We did not get that choice. It was made for us and when we complained—and we all did—we were essentially told to take it or leave it.”
Duff said he met with Kelley’s undergraduate admissions director, Alex Bruce, in June to discuss the change, and in that meeting Bruce told him the school had overadmitted for the incoming class and received commitments from far more students than they anticipated.
“I asked [Bruce] if the admission department was telling the academic departments to grade harder, to weed out even more students than prior years,” Duff said. “He assured me that admissions and academics are separate entities and have no control over each other. I do not believe anything he told me that day.”
The new “Tracking Transfer” report from the National Student Clearinghouse Research Center shows little improvement in transfer rates for first-time college students. But it also sheds light on factors that could contribute to better outcomes.
The latest report, part of a series, examined transfer data for students who entered community college in 2017 and for former community college students enrolled at four-year institutions that academic year.
It found that only 31.6 percent of first-time students who started community college in 2017 transferred within six years. And slightly fewer than half of those who transferred, 49.7 percent, earned a bachelor’s degree, consistent with outcomes for the previous cohort.
But some types of students had better outcomes than others. For example, students who came to community college with some dual-enrollment credits had higher transfer and bachelor’s degree completion rates, 46.9 percent and 60.1 percent, respectively.
Bachelor’s degree completion rates were also highest for transfer students at public four-year institutions compared to other types of institutions. Nearly three-quarters of students who transferred from community colleges to public four-year institutions in the 2017–18 academic year earned a bachelor’s degree within six years. The report also found that most transfer students from community colleges, 75.2 percent, attend public four-year colleges and universities.
Retention rates among these students were also fairly high. Among students who transferred, 82 percent returned to their four-year institutions the following year. The retention rate was even higher for students who earned a certificate or an associate degree before they transferred, 86.8 percent, which was nearly 10 percentage points higher those who didn’t earn a credential before transferring.
Approximately 65 percent of the 1.2 million active-duty service members in the U.S. armed forces have less than an associate degree level of education, according to 2023 data; many of them hold some college credits but no degree. Federal aid programs make enrolling in college and earning a degree more accessible for military-affiliated students, but not every student is aware of academic interventions that can help them complete a credential sooner, including credit for prior learning.
A 2024 research article found that prospective students with military experience were most likely to prioritize academic programming when selecting a college, followed by financial assistance and affordability. CPL is one way colleges and universities seek to expedite student veterans’ ability to enroll in and graduate from college, recognizing the learning already accomplished while in the armed forces.
In the most recent episode of Voices of Student Success, host Ashley Mowreader speaks with three experts from the Kentucky Council on Postsecondary Education—senior fellows Matt Bergman and Dallas Kratzer, and Tracy Teater, associate director of adult learner attainment—to discuss the state’s adult education attainment goals, challenges in CPL rollout and other models of success across the country.
An edited version of the podcast appears below.
Inside Higher Ed: Just to get us started, Matt, can you talk a little about the connection between credit for prior learning and adult learner success? What is that link and why is this an important starting point when it comes to engaging adult learners?
Matthew Bergman, senior fellow at the Kentucky Council on Postsecondary Education and an associate professor at the University of Louisville
Matt Bergman: Credit for prior learning has been around quite a long while, from the early 1930s to when we saw the transition of many military back into higher education. [We were] thinking about, how we could transition individuals that are work-ready but have some college-level and credit-worthy learning that would create more efficient pathways?
Credit for prior learning has been a huge benefit to so many of those folks with that experience. And this is just not experience alone; this is very thoroughly and rigorously assessed learning that we can translate and map directly to curriculum.
The University of Louisville was part of a 72-institution study by the Council on Adult and Experiential Learning, or CAEL, and the CPL Boost came out with some really hard-hitting empirical evidence that not only do people get to graduation faster, but they graduate at a higher rate, and also those that actually engage in this work take more credit hours.
That might seem a bit counterintuitive, but what it boils down to is this idea that you increase retention and persistence by percentage points that create a net-positive revenue for institutions along the way. So the myth of taking away tuition from the university is gone. We’ve got empirical evidence that not only does it benefit students and they save money, but actually the institutions are making more money in the long term because they are creating paths that are efficient, meaningful and impactful for these adult learners, military and beyond.
Inside Higher Ed: Why are students with military experience a focus area when it comes to CPL?
Dallas Kratzer, senior fellow at the Kentucky Council on Postsecondary Education
Dallas Kratzer: The American Council on Education has done the evaluation of a lot of military workplace learning, which can include not only the courses they’ve taken in their military careers but also the learning that they’ve had on the job.
In the military, we have a lot of different types of things that we do, and ACE has evaluated many of those. In those evaluations, the great thing is, those types of jobs and skills line up to the civilian sector. About 85 percent of what we do in the military is done in the civilian sector. So, if we can get it right and benchmark off of what ACE has done, it makes it really easy for a higher ed institution to then step across the line to the civilian sector and say, “ACE evaluated it this way. This is how it looks in the civilian sector. We can take that same credit recommendation and make some linkage there.”
As a matter of fact, O*NET has a military jobs crosswalk to civilian jobs. So linking all of that together, and the program that Matt worked on at the University of Louisville, he and I both worked with it, they use it really heavily to make that crosswalk, or that linkage between those two.
Inside Higher Ed: Part of this is from the institution side—making it clear how military experience fulfills civilian responsibilities or those job functionalities. But there’s also making that linkage for the student; if you are somebody with military experience, maybe you haven’t considered the ways that that can translate into the transition outside the civilian world.
Kratzer: You are so on the mark with that comment, because so many folks in the military just see that they’re doing their job. I did 35 years in the Air Force and worked extensively with the Army in the later years, and [military personnel] often think that what they’ve learned on the job or the things that they are doing in their career fields are just that—a job. They don’t see the experiential learning that comes along with that and how that can be translated into college credit.
I’ve had times where I’ve worked with individuals, and I’m like, “So have you gone to college?” Yes, some of them have. “Have you completed a degree?” “No, but I’ve got some college.” And then about a third of them don’t even think about it, and they would say, “No, I don’t have any college [credit] at all.” I’m like, “Actually, you do. There’s this thing called a joint service transcript, and your workplace learning, your military courses have been evaluated, and you have this pot of credits that you need to take to your higher ed institution and say, ‘How does this translate into me completing my degree?’”
Inside Higher Ed: Kentucky has a large plan at the state level to support adults and nontraditional students; how does CPL fit into this vision of student success?
Tracy Teater, associate director of adult learner attainment
Tracy Teater: The Kentucky Council on Postsecondary Education is committed to supporting and improving learner pathways, both to access and then successfully complete postsecondary goals across the age continuum, whether that is a traditional or a post-traditional student. We recognize that supporting our adult learners—whether they be adults with high school equivalency diplomas, adults enrolling for the first time or adults re-enrolling to finish their degree—leads to increased economic mobility for them and their families, increased workforce for Kentucky, of course, and an increased college-going rate for the next generation.
Because our adult learners are often parents, I can’t stress that point enough: By investing in our adult learners and our adult learner returners, we are investing in those generations to come.
Credit for prior learning is a key part of Kentucky’s larger vision for student success. It removes barriers and accelerates pathways for those adults to earn meaningful credentials. That supports Kentucky’s 60 by 30 goal, our North Star, if you will.
To ensure 60 percent of working-age adults hold that postsecondary credential by 2030, it requires that we recognize the learning and experiences that our adults often bring with them from military service, from work, from industry certifications and from their life experiences. This saves tuition dollars for our families and increases return on investment, as Matt shared earlier on, for both the campus and the state. I think also important and sometimes overlooked in this conversation is the fact that it sends a powerful message to the learner that you belong on campus and you’re respected and valued for the college credit–worthy experiences you bring. And so this sense of belonging, I think, impacts persistence towards learning goals. And so CPL for Kentucky is not a stand-alone effort. It’s woven into the broader student success agenda as a way to re-engage adults, and it’s been really exciting to be a part of the work, because Kentucky has a demonstrated commitment to adult learners.
The goals of the Kentucky Student Success Collaborative are we want to set the conditions for a culture of collaboration, and we want to build capacities of our campus partners to innovate and then ultimately accelerate progress.
Kratzer: I’d like to make a comment or tag on to what Tracy just said about one part of that, and that is the tuition dollars and how we can reduce the cost of going to college or returning to college through credit for prior learning. But more importantly, to the military community, the thing that we need to keep in mind is if they have already earned the training and the learning, and we don’t recognize that in higher ed, we’re not being a good steward of the taxpayers’ dollars, because we’re having them go back and take training that they’ve already accomplished. So this is such an important aspect to that military credit recommendation.
Inside Higher Ed: We’ve laid out a lot of the reasons why CPL is so beneficial to the state, to the institution, to the student, to their families, to their future families. But if CPL were easy to do, everyone would be doing it, and they’d be doing it well. So I wonder if we can talk about some of those hurdles when it comes to implementing and executing CPL effectively, and what sort of resources and time it takes to do this work and to do it well.
Bergman: There are a number of barriers, because it is labor-intense. In some ways now, as a result of the American Council on Education, we have military acknowledgment and recommendations for these credits that make it very tangible, almost as though it is transfer credit for most institutions. But the portfolio process that goes beyond that is a bit more labor-intense and faculty-driven. So that is a bit of a barrier.
But what we are seeing as a result of the people on this call here—Dallas, Tracy and so many others that are doing research in this field—we have seen barriers declining. The skepticism of this whole process is starting to wane in a way that is creating pathways for us to reach other institutions in Kentucky, but also nationally. And that’s good. A lot of thanks goes to some of the seminal authors in this work, like Nan Travers and Becky Klein-Collins. These individuals have produced scholarship that has really rooted empirical proof that this is most valuable. It creates efficiency. It helps with tax dollars, and when you boil down all of the pieces and parts, it becomes very process-oriented and very standard in approach.
Now, that has been a long road getting to this moment. So when you talk about barriers, they have been there for so many years that they are starting to diminish, and we are so grateful for that—not only in the Commonwealth of Kentucky, but beyond, because institutions and specifically faculty, which were the biggest barrier in acknowledgment of CPL, are starting to come onboard. Not only because of the demographic cliff, but also because of some of the skepticism that we have in higher education and the shortages that we have in enrollment now. [Faculty] are more likely open to this concept because we are taking this work, we are showing the process, we are showing a portfolio and we are being very transparent about how we calculate and assess learning and translate that to academic credit. In the moment that we do that, we show the robust process. We have new advocates for this work.
When we think about military personnel directly, we plug those individuals into some of those more traditional classes and disciplines, and those faculty are immediately like, “Bring every military learner into my class. They are so mission-driven. They are so committed to this goal of getting to the degree that I want every military learner in my classroom.”
When institutions become military-friendly, that’s when you see the pipeline. Because military folks are insular in their process of communicating about the programs that work well, that are very “military engaged,” to use the phrase from Dallas, but you have to be military engaged and ready for these learners if you’re going to serve them well. And more and more institutions are doing that, showing that commitment.
Kratzer: Just to add to what Matt’s talking about, this whole thing really boils down to awareness. And back in 2015, ACE and a couple of other organizations got together and produced this document called “Credit for Prior Learning: Charting Institutional Practice For Sustainability,” and they identified four major challenges: organizational structure, organizational awareness, student awareness and student engagement. When we see what the challenges are and then address those challenges, it’s really awareness. People just need to become more aware of the population and how what we do in the military can be translated to other sectors and other affinity groups and very easily done.
We’re in a spot right now in higher education. And Tracy alluded to this with the demographic cliff, that we see that adult learners have become a recognized population, and in that adult learner population are different subsets that we can engage with. I think the military one is the best one to start with, because so much of the work has been done and it’s just capitalizing on that. Additionally, the military community is a different set of learners. Military training is about learning, and in the military today, it is very technical thought processes, processing information, very much focused on that academic rigor. So that’s why they make some of the best students today, and anything that we can do to help attract them to our institutions will be incredibly beneficial for all of us.
Inside Higher Ed: We’ve mentioned CAEL and ACE and some other well-known organizations who are supporting this work, but are there other states that you’re learning from or other organizations that you think are doing this work well?
Bergman: One in particular is North Carolina, and through the Belk [Endowment], my buddy Mike Krause is making magic happen down there through InsideTrack and their connection to reconnecting learners that have some college and no degree, but also tying in CPL and then military-connected learners. They are going full force with the type of resources to really re-engage those learners and create a very clear path.
Oftentimes when trying to reconnect with people, they need to see how this might fit into a compartment of their lives. Because we know, as we serve these learners, they have No. 1, No. 2, No. 3 priorities and then education might come into the conversation [later]. So it’s really important for when we engage these types of learners, when we think about military learners, we have to understand that [education] is not likely priority No. 1.
I use this analogy of “Would you give up some streaming services or social media scrolling to the tune of four to five hours a week for a bachelor’s degree in two years?” And oftentimes people are going to say, “What do you mean? Of course I would.” And I say, “OK, let me break this down and work backwards,” and you look at the number of credits one can earn that they get from CPL, but also what they’ve accumulated thus far, and you start to put the pieces of the puzzle together.
States like North Carolina, Tennessee have done an absolutely wonderful job. California has gone all in on CPL as well, to really try and reconnect learners and show them that the light at the end of the tunnel is quite bright.
We learn from one another—these people are just colleagues in the weeds, really grinding, trying to find ways to really replicate and make it respective to our own institutions and just chop and drop these policies so that we really can scale and impact more and more learners. Now we have battled for years and years and years, and you can hear my passion in this, but we have fought the very traditional mechanisms of institutions, and we are starting to break down so many of those barriers, partially because of the demographic cliff, partially because of some of the skepticism. But as Dallas said, adult learners, military learners are on the forefront. We are at the table for traditional higher ed, and that is a huge change in such a benefit for these learners, because there are new funding models, there are scholarships, grants and then CPL, creating efficiency that we just didn’t have 15 years ago.
Kratz: A couple of organizations that I think are doing some interesting work here … the Council of College and Military Educators. They do an amazing job at bringing the senior leadership of the Department of Education, Department of Labor, Veterans Affairs, all these folks together to talk about education related to the military community.
One that I see as a rising star is NASPA Vets. They have a military-connected students conference every year. I was very excited to see what they’re doing, because it’s helping student affairs administrators to better understand the military population, and part of this is this whole awareness and how we can serve that community.
Of course, Student Veterans of America, it’s a great organization to have on your campus. The work they’re doing in getting the word out to service members is so important … “Hey, come and be in higher education, because we have space for you. This is part of your culture and you can be part of it through this student organization.”
Some states to add on to what Matt was saying about Tennessee and California: Ohio started this thing called Collegiate Purple Star, and I think we need to do that across the country. The reason for that is everybody’s military-friendly right now, but with both Ohio and Indiana’s Collegiate Purple Star, it’s about not only being military-friendly, but military-ready, meaning that you’ve gone the extra mile and you’ve created the pathways to degree completion for service members based on their experiential learning that they’ve had during their military careers.
Inside Higher Ed: How are you all tracking effectiveness and the impact of the work that you’re doing? What does it mean to apply data to CPL for military-affiliated students? What are some of those metrics that you’re tracking?
Teater: I would back up one step to say that data alignment has been a gap that we have learned firsthand about during this pilot. One of the things that we know is that across the broader CPL opportunities, our campus partners are tracking that in different ways, which means that it is a definite gap of how we can track impact as a state without having aligned ways to do that. I wouldn’t call it a challenge; I think I’d call it an opportunity. But it’s something that we definitely want to end this with state recommendations so that we can do a really, really good job of tracking all types of CPL across the state. That’s one gap we’ve seen that I think we will be able to end this with a definite solution to and again, looking at some of our neighboring states and how they’ve been able to address that.
Bergman: It’s important to note that the state work that we’re engaged in, the CPL Council on Postsecondary Education initiative, we are collecting data around metrics directly in growth of CPL, total numbers of credits earned, those programs that are offering them—so additional programs beyond just single adult-friendly programs at institutions—and then actually the number of humans that are connected in the work, so hiring individuals that are responsible for CPL and tracking data through the institutional research office.
We are seeing great growth there, but this is also a direct by-product of what we are seeing in the field, in research and scholarship. I did my dissertation roughly 15 years ago, and it was a really challenging enterprise to find empirical work and scholarship that would really drive my dissertation forward, looking at adult military persistence. What I see today, as I am looking at journals almost daily, is new articles, new empirical pieces and new national work and research that is popping up almost monthly now that is focused on these populations. It is such a boon to our work, because individuals are doing this work, not only for their dissertations, but in their research and scholarship field.
There were not a lot of folks doing this work many years ago, but now we have a new crop of young people jumping in as advocates and allies of military and adult learners, and it truly is making a direct impact, because we have data to lean on and say, “Here is empirical proof of how this directly impacts this individual program or this particular state or this region,” and using that to guide a lot of our push and our nudging that we do, both in Kentucky and beyond, to make institutions think differently about how they formalize policy to really attract these folks and know that they can get them to and through more efficiently.
Kratzer: ACE and CAEL just partnered together to do the national landscape of credit for prior learning, talking about how states are making those recommendations. And I think there’s a lot of work to be done yet to help states, particularly at the legislative position, to understand how to help systems better collect the information. Because from the state, we hear them say, “Yes, you must accept military credit recommendations.” And the schools go, “OK, we accepted, but we don’t apply it well.” We need to be better at counting how we apply it so that we can provide back better information to say, “It does. It is valued in our state. It’s not just brought in as elective credit, but it’s brought in as degree credit that will accelerate degree completion,” and we’re not tracking that as well as I think we could.
Inside Higher Ed: I think you bring up a really valuable point there about the different types of credit. Just because it’s accepted doesn’t necessarily mean it’s helpful to the student in their specific career goal. But I think making sure that all credit is recognized and supported as part of a degree pathway is definitely the next step that we need to see.
Bergman: I would be remiss if I didn’t mention the fact that we have nearly 150 institutions involved in the prior learning assessment network. So for listeners that are checking in on this particular podcast, you can say, “Hey, I’m going to connect with Dallas,” or “I’m going to reach out to Matt and join this prior learning assessment network and hear from these institutions that are doing this work on the ground.” Each month, it costs zero money—we have a featured individual from an institution talking about, whether it be marketing or military credit recommendation or policy implementation or the admissions process in CPL; we are looking at all angles of CPL through the prior learning assessment network from people on the ground.
Inside Higher Ed: That’s amazing. I love especially when we can talk about different institution sizes and types, because what works for one institution might not be easy to do at another.
Bergman: And the best part of that is it’s free. We are not charging individuals. We are just a community of committed professionals that have been working for so many years trying to make an impact, and now we see our crop of individuals growing and growing every single month.
Inside Higher Ed: I want to hear more about what’s next for the state as you all consider adult learners and that lofty goal of 60 percent attainment.
Teater: Matt laid it out beautifully from a national perspective; from a Kentucky perspective, we hope to do the exact same thing.
We are exploring ways to align data collection efforts so we can accurately gauge impact across the state, impact for the institutions and then impact, of course, for the adult learner. We also hope to explore ways to align and standardize credit mobility across our two-year and four-year campuses, so that credit earned at one institution can be recognized at another, so that our two-year graduates can seamlessly transfer to our four-year campuses, and then this will lead to state standards and policies to further support CPL efforts. We’re looking to some of our neighboring states on best practices there.
Then finally, we are, in the fall, launching our Kentucky Adult Attainment Network, from which we will convene a state working group and community of practice to continue to build champions for the work, but also share resources, best practices and be able to offer up policy recommendations that will enact to further address this key part of our adult learner action plan.
Inside Higher Ed: Do you have any advice or insight for others looking to support military-affiliated learners?
Kratzer: I think the big thing that my peers need to know and to understand about the military community is that there’s a significant amount of learning that they gain from their military experience. However, the service member doesn’t always appreciate it the way that we as academics can understand it. They just say, “Hey, I was just doing my job.”
Well, that job has worth and value beyond what you did when you were in the service. There’s so much more we can do. The leadership training that they get—business and industry are just dying for that kind of professional development, so let’s recognize it. Let’s help them to see how they can transition to the civilian sector and bring those great learning skills into the workplace and into higher education.
Bergman: CPL for military and beyond is being done very effectively. If your institution is not doing it perfect or is not even involved, it is being done and there are so many people that are ready to provide open-source information, policy practice, forms, strategies, techniques and nuanced information to your institution directly for free, so that you can engage in this work without having to start from scratch. So to boil it down, you don’t have to start from scratch. So many institutions are doing so well in this work, and if you want to engage, just reach out and we will plug you into the prior learning assessment network or any type of forums at the University of Louisville or share data or information that we use in the state of Kentucky’s CPL initiative. We are ready to share these things because it matters and it’s impactful.
Teater: The awareness is critical, and that’s awareness across states, across institutions and within institutions. One of the things that we have seen is sometimes just a gap in awareness on what’s possible, what’s available and then how best to pull the technical levers to make those things happen for students. So I would say every single conversation that we come out of, we learn something new, and hopefully others learn something new as well. And I just think that that awareness can’t be underestimated.
I will never forget the student who—upon being given 15 minutes at the end of class to get rolling on the writing assignment I’d just given—whipped out their phone and starting furiously typing away.
At first, I thought this was an act of defiance, a deliberate wasting of time I’d been generous enough to provide following a carefully constructed discussion activity that was meant to give students sufficient kindling to get the flames of the first draft flickering to life.
I said something about maybe texting people later and the student said that they were working on their draft, that they, in fact, first wrote everything on their phone. Not wanting to make a fuss in the moment, I shut up about it, but a week or so later in an individual conference I asked the student about their method, and they showed me the reams and reams of text in their phone’s Notes app.
The phone itself was a fright, the screen cracked, a particularly dense web of fractures at the bottom, but when I asked the student to show me how they used the app for writing, it became clear that they could type at a speed comparable or better to the average student on a computer keyboard.
I’d been teaching the writing process for my entire career, talking students through the steps and sequence to producing a satisfactory piece of work—prewriting, drafting, revision, editing, proofreading—with more detailed dives into each of those stages, but until that incident I didn’t fully appreciate that I shouldn’t be teaching the writing process per se, I should be giving students the kinds of challenges that allowed them to develop their own writing processes.
As I considered this distinction, I realized how truly idiosyncratic my own process is and how different it can be depending on the occasion and situation. An outside observer looking at how I put together a column or book or proposal would see all manner of inefficiency and declare my method … madness.
But the key thing about my method is that it’s mine, and I think I have sufficient proof that it works. It may continue to evolve over time, which I suppose we could equate with improvement, but it’s really just different.
My student’s strategy was rooted in resource constraints, both time and money. Typing on the phone had started as a way to get stuff done during brief in-between times when working as a bicycle delivery person for one of the downtown-Charleston sandwich shops. They’d capture a draft on the phone on the fly and then transfer it to a computer for further development. The phone text had notes like “put thing from that thing here” as place markers for sources or evidence.
I realized that this method required the student to fundamentally work from a place of their own thoughts and ideas, something that was actually at odds with some of their first-year writing classmates who had been conditioned to defer to their readings, seeing their job as students to prove that they’d read and (generally) understood the content, rather than building on that content with ideas of their own, as I’d been asking them to do.
At the time of the conference, the student didn’t even have a computer, having had theirs stolen and not having sufficient funds at the time to immediately replace it. The student had been using the terminals in the library computer lab for the nonphone work.
This conference also revealed the reason for the rather up-and-down nature of this student’s work that semester. This was a clearly curious and driven person who had a number of extra challenges at simply completing the work of college. The assignment we were working on at the time, an alternate history analysis where students had to take a past event, change some aspect of it and imagine a different future, was probably the most challenging experience of the semester, but according to my archives at least, it proved to be this student’s best work.
Writing the initial draft untethered from any sources or even being able to easily move between information online and the text on the screen required the student to think creatively and analytically in ways that unlocked interesting insights into their choice of subject. Because of fate and circumstance, and without me really planning it, this student was getting a high-level experience in how to harness their own mind.
I started thinking more deeply about the intersection between the affordances of the tools and the writing process. One of the biggest shifts in my method over the years was when I acquired an external monitor that allowed me to see two full pages of text simultaneously on screen. This was something I’d longed for for years but resisted because I’m cheap. I now have a hard time working without it.
This incident happened as I was also experimenting with approaches to alternative grading, so it became a natural fit to start asking students to reflect more purposefully on the literal mechanics of their writing process so they could identify missing needs that they might be able to fulfill.
At the time I hadn’t yet come up with my framework of the writer’s practice, but now I can see how integral asking students to be this mindful about their own process can be to the development of a practice.
It’s also a good route for introducing mindfulness into the choices they may make when it comes to using generative AI tools. If they understand their labor and its meaning, they will have the capacity to assess how using the tool may enhance or—what I think is more likely—distort their process. It is also a reminder to us to design challenges that encourage the kind of labor we want students to be doing.
Before we retreat to old technology that dodges these challenges, like blue books, I think we could do a lot of good by really leaning in to helping students see writing as an experience that will differ based on their unique intelligences, and that if they pay attention, if what they are doing matters, they can come to know themselves a bit better.
This summer, I did a gig as an international cat courier. As a favor, I agreed to fly from my home in Spokane, Wash., to D.C., meet my sister-in-law and travel with her to her new government post—taking responsibility for one of her two cats—on a plane to Algiers.
Having never visited a Muslim country, I was game, though people who’d traveled widely warned me that Algiers is unusually conservative and restrictive. I got warnings not to ask about religion or politics. A friend who works for the U.N. gave me a talking-to about what to wear, which boiled down to: no exposed skin.
My sister-in-law would start work the day after we arrived, so I’d be on my own in a country where my options were limited. You cannot use credit cards, only cash, and can’t change money. I’d tough it out and then five days later head to Italy for a vacation.
The only thing I could do in Algiers was walk around, make friends with many street cats, and talk to strangers. In French brushed up from college with some recent Duolingo practice, I spoke with shopkeepers, chatted with security guards outside embassies and met people hanging out on the streets. I didn’t always bother conjugating verbs and probably misgendered every noun.
What I found were people who love their homeland and were eager to show me around. Even in a country that fought for independence in the 1960s, endured a bloody civil war in the ’90s and now exists under a repressive government, pride endured. But I also noticed what wasn’t there: easy travel, open political discourse, casual criticism of authority. Their pride lived alongside careful silence.
In my layover on the way home, I struck up a conversation with a Delta employee from Algeria. I told him how generous and openhearted I’d found everyone I’d met. His face lit up. “It’s good now. It’s better.” But when he spoke of the government and the civil war—even in the Minneapolis airport—his voice dropped to a whisper.
He now lived in the U.S., scanning bags as they rode around the carousel, having earned a Ph.D. in economics in his home country and taught for 30 years at a university in Poland. He would be going “home” to Algeria in September.
People, I’m just gonna go there and say it: I love America.
Given my politics, profession and (hippie Vietnam War–protesting) parentage (father: regional public faculty; mother: community college and Ivy lecturer), I’m a little surprised to find myself feeling a surge of patriotism, especially these days, I know I’m expected to be cynically critical of everything our (legitimate) government does. Many of my friends and colleagues dismiss folks who vote differently from us and wave a virtue flag at “those people” who drape their homes in red, white and blue.
And yet, many who share my convictions about diversity, equity and inclusion have often been intolerant of others. We’ve gotten shouty, telling others they’re wrong, uneducated and a bucket of creeps. Maybe some of them are. Maybe some of us are, too. But we sure have stopped talking to each other. We’re not even getting the same news or finding the same facts. Some of my friends say they’ve become numb to what’s coming out our nation’s capitol. Not me. Every day I am shocked by where we are now, and where I fear we might be heading (another bloody civil war).
In academe, we have the luxury to spout off. We spouted and in 2016 learned a big lesson: Not everyone was buying what we were selling. Which is how we got to the current political, cultural and societal shit show.
And yet, I still love America. I love the values expressed in the documents that established us, written in such beautiful language I often assign them to creative writing students. The autobiography of our funniest founder—the first best-selling book—still carries so much wit and wisdom I’m filled with awe and envy when I reread it. The America Lincoln described in speeches with the brevity and power of a prose poem can bring me to my knees. And I love that over the past two centuries, our best leaders hoped by their criticism to form a more perfect union, to correct the many things we’ve gotten wrong.
Just before I boarded a long and uncomfortable flight, a friend sent me a link to Ronald Reagan’s last speech. In it, he quoted from a letter he’d received: “You can go to live in France, but you cannot become a Frenchman … But anyone, from any corner of the Earth, can come to live in America and become an American.” His point: “If we ever closed the door to new Americans, our leadership in the world would soon be lost.”
If you’d told me decades ago I’d write in praise of Ronald freaking Reagan, I’d have said that’s as likely as 2001: A Space Odyssey’s HAL becoming reality. But, well, here we are.
We can’t stop critiquing our country—that’s the essence of democracy and the real value of higher ed. But instead of just spouting off about what’s wrong with America, we need to model how to engage constructively with imperfect institutions. We need to teach our students how to critique while also participating, how to demand better while acknowledging what’s worth preserving.
Seeing a country like Algeria, that has closed itself down politically, isolated from the other North African nations and in many ways the rest of the world, even after throwing off colonial rule, felt like a cautionary example. In higher education, when we shut ourselves off to uncomfortable truths or dismiss those who disagree with us, we risk becoming like that whispered conversation in the airport—fearful, constrained, diminished.
Which is why, after five days of wandering Algiers with bad French and heat-slick layers of covered skin, I boarded my flight to Rome to stuff myself with pasta alla carbonara, gelato and vigorous discussions about what’s wrong with today’s world with an odd mix of relief and resolve. You don’t have to think your country’s perfect to love it, but you do have to notice when the door’s still open and fight to keep it that way. In democracy, as in academe, the moment we stop letting in new voices, new challenges, new possibilities, we begin to die from the inside out.
Rachel Toor is a contributing editor at Inside Higher Ed and the co-founder of The Sandbox, a weekly newsletter that allows presidents and chancellors to write anonymously. She is also a professor of creative writing and the author of books on weirdly diverse subjects. Reach her here with questions, comments and complaints compliments.
As the global education landscape evolves, understanding what motivates international students has never been more critical. NCUK’s annual student survey series, Transforming Student Futures, provides essential insights into the aspirations of approximately 1,000 international students from 88 countries participating in NCUK’s in-country pathway programmes worldwide.
The latest findings reveal clear patterns in student priorities that demand attention from educators, policymakers, and universities.
Maintaining quality and reputation is key
Quality of education stands as the decisive factor for international students, with 69.9% of respondents selecting it as their primary motivation for pursuing overseas qualifications, up from 58% in 2024. Career-focused motivations follow closely, with over half of students (56.4%) motivated by career development opportunities, including increased employability and monetary benefits.
This emphasis on educational excellence is particularly pronounced among students from Nigeria, Pakistan, Myanmar, and Peru, where quality ranks as the top motivation. In Kenya, quality shares the top position with career development, while in Ghana, it ties with gaining new knowledge as the primary driver.
Interestingly, students from China present a unique pattern, with gaining new knowledge emerging as their main motivation rather than quality alone, suggesting different educational priorities for NCUK students across source markets.
The rise of TNE and changing learning preferences
Traditional learning models continue to dominate student preferences, with 66% favouring fully on-campus learning experiences. However, the survey indicates growing consideration for online provision as an increasingly viable alternative, reflecting evolving attitudes toward flexible education delivery.
The year-on-year increases in demand for full online learning (up from 10% to 22%), full on-campus learning at a local institution in the students home country (up from 16% to 32%) and full on-campus learning but half taught at a branch campus in the student’s home country and half taught at a main campus overseas (up from 14% to 20%) all signal a move toward flexibility.
This shift aligns with the recent growth of TNE, and NCUK’s in-country model and diverse qualification offerings cater to this demand, enabling students to access global education without relocating immediately.
Is it worth us considering whether, as a sector, we sometimes place too much emphasis on policy change?
The high confidence level in NCUK pathways – with 94% of students believing these programs will enhance their career prospects (a 5% year-on-year increase) – demonstrates strong programme satisfaction and perceived value among participants.
Policy changes: The US coming up Trumps but overall, NCUK students unaffected by policy changes
In 2025, 52% of respondents expressed concern about UK visa restrictions, up from 38% in 2024, reflecting recent tightening of post-study work policies. Conversely, the USA saw a 12% rise in positive sentiment (to 29%) due to perceived stability in immigration rules, while Australia’s appeal dipped 8% (to 22%) amid cost-of-living concerns.
These shifts highlight a nuanced landscape: students from Ghana and Pakistan are more deterred by UK policy changes, while Nigerian students remain optimistic about the USA. However, the overall message here is that NCUK students’ decision making does not seem significantly influenced by policy changes, with 80% of respondents choosing the UK as their preferred destination, despite the above findings.
Is it worth us considering whether, as a sector, we sometimes place too much emphasis on policy change?
Implications for the future
The emphasis on quality demands continued investment in academic excellence and institutional reputation to meet rising student expectations, particularly in competitive source markets like Nigeria. And further, expanding TNE and hybrid learning options will cater to students seeking quality education with flexibility, reducing reliance on traditional study-abroad models.
NCUK’s in-country pathway programmes demonstrate strong alignment with student needs and aspirations, offering the academic preparation, university access to high-ranking institutions, and career development support that international students prioritise. As the education sector continues to evolve, maintaining focus on quality, flexibility, and comprehensive student support will remain essential for meeting the diverse and changing needs of international students.
About the author: Andy Howells is the Chief Marketing Officer for NCUK, a leading global pathway provider. He has worked in higher education for over 15 years in senior marketing and student recruitment roles at Royal Holloway, University of London, the University of Southampton and most recently, Universities UK International (UUKi).
Andy has won several awards, including ‘Best Issues and Crisis Campaign’ at the PR Week Global awards in 2022 for UUKi’s We Are Together campaign, and ‘Marketing Campaign of the Year’ at the PIEoneer Awards in 2023 for UUKi’s Twin for Hope campaign. In 2023, Andy led the relaunch of the UK higher education sectors, #WeAreInternational campaign.
Andy is a father of two young children and his claim to fame is delivering his second child himself, in his car, in a supermarket car park during the first weeks of Covid lockdowns!
From learning gain to educational gain, the concept is appealing for its emphasis on understanding how students grow and develop over time, and the extent to which higher education institutions can make robust claims about their roles and contributions.
However, the Office for Students (OfS) left the definition and measurement of educational gain to individual providers to decide for themselves, which left the sector with a multitude of definitions. In the absence of a clear, shared definition of and approach to educational gain, the sector has tended to default to what is most easily measured.
Yet, an over reliance on student outcome metrics (such as the National Student Survey, continuation/completion or Graduate Outcome data) reduces the indicators of student development into just numbers. More concerningly, this approach meant student groups with small numbers may be lumped together or even excluded in various statistical analyses. When we focus on lived experience as headline statistics, the nuances are swept away.
For individual institutions, a context-specific relevant approach makes sense, reflecting their own goals, priorities and practical considerations. But as a sector, including for the OfS, such freedom makes national comparison difficult if not impossible, and we revert to readily accessible and available outcome data.
Yet, educational gain must not only capture cognitive progress, but also the broader and holistic developments such as confidence and belonging.
The sector would benefit from a shared but flexible frame of reference for educational gain, which advocates for a diverse approach to evidence student growth over time.
A multi-dimensional approach to educational gain
Informed by the foundations of learning gain, this new paper proposes a multi-dimensional model of educational gain through three interrelated domains: cognitive and metacognitive, personal and affective, and social and cultural. Drawing on educational, psychological and sociological perspectives, these domains recognise the different aspects of student development, which also foregrounds the importance of longitudinal data from both qualitative and quantitative methods.
A multi-dimensional approach appreciates the student experience across the agency-structure spectrum. It provides an overarching frame of reference that enables institutions to tailor the specific approach as appropriate for their contexts. There will be differences across the sector in how institutions apply these in practice, but if the three domains (cognitive and metacognitive, personal and affective, and social and cultural) are broadly shared and operated as a thematic proxy across the sector, then we are at least in a position to explore how different institutions have collectively explored those dimensions.
For example, for cognitive and metacognitive development, it is conceivable that TASO’s Access and Success Questionnaire (ASQ) is adopted nationally to provide sector-wide comparable data with use value within and across institutions. In parallel, it is also conceivable to run a longitudinal qualitative study that unpacks how students articulate, reflect on and discuss their cognitive and metacognitive development.
Similarly, quantitative and qualitative methods can explore the extent to which students grow in confidence, resilience and self-efficacy, or whether they expand their social capital, sense of belonging or broader development as global citizens.
A multi-dimensional approach offers a unified lens for understanding educational gain that recognises sector benchmarks as well as local narratives. Without such a multi-dimensional view, the sector risks defaulting to established metrics that do not capture the full breadth of gains students achieve during their higher education.
What institutions can do
Short, funded pilot projects – supported by modest capacity-building grants – would give staff the space to test these methods before it is rolled out more widely. Contextually relevant reflective tasks could be strengthened and encouraged across programmes to encourage students to engage more critically with their own development. Crucially, it is important to ensure that any evidence gathered is conceptually robust and grounded in relevant theories of student progress and gains, for example: cognitive and metacognitive development, personal and affective growth, and social and cultural development. National-level benchmarks can be used effectively alongside the richness of context-specific data and evidence collected over time at the institutional level – reconciling national comparability with institutional distinctiveness.
What next?
If educational gain – and variations of it – is part of any next assessments, then the OfS should really be more explicit about what it expects from institutions. The ‘test’ from TEF 2023 to give providers the freedom to set their own criteria may be well-intended, but it served limited value for the sector, and presumably for the regulators themselves. A broad, flexible guiding principle or framework might provide the necessary coherence, preferably one that invites theoretical and methodological foundations in addition to the practical and pragmatic.
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.
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The rapid integration of generative artificial intelligence (GenAI) into higher education has reignited a familiar moral panic around academic dishonesty. While much of the immediate institutional response has centered on detection and enforcement, this reaction echoes a familiar pattern—one that treats academic dishonesty as an individual moral failure rather than a symptom of broader systemic issues (Bertram Gallant, 2008). Students, faced with overloaded schedules, mounting debt, and an unforgiving emphasis on grades, often make pragmatic decisions about where to invest their time and effort gravitating toward efficiency and certainty over exploration and risk. In this environment, the ethical clarity around “cheating” becomes murky—not because students don’t understand right from wrong, but because the structures around them reward efficiency and penalize risk. AI tools such as ChatGPT or Grammarly simply fit into this equation as time-saving devices—much like calculators, Google, or even essay mills before them. What has shifted is not the motivation, but the accessibility. A student’s use of AI is a reflection of the transactional nature of how learning is often experienced in contemporary institutions.
The proliferation of AI tools has not so much introduced a new problem as it has exposed the fragility of existing educational structures. Before we focus on what AI changes, we must confront what it reveals: that higher education has long incentivized a culture of performance over learning. Research has shown that when students perceive academic work as irrelevant, overly procedural, or divorced from their goals and identities, they disengage—regardless of whether AI is available (Ambrose et al., 2010). In this light, the question is not “How do we catch them?” but rather, “What kind of learning environments have we built that make outsourcing feel logical or even necessary?” This reframing shifts the pedagogical conversation from one of compliance and control to one of capacity-building and curiosity. If we can use this moment to ask how to foster a culture of learning that values growth over grades, process over product, and understanding over output, then AI may serve not as a threat to education but as a catalyst for its reinvention.
Why Students Turn to AI
When students turn to generative AI to complete assignments, they are not necessarily demonstrating a lack of understanding or unethical behavior—they are often responding rationally to the conditions of their academic lives. In surveys and interviews, students routinely cite overwhelming workloads, unclear expectations, time scarcity, and mental health struggles as reasons they seek shortcuts or support tools (McCabe, Treviño, & Butterfield, 2001; Pascoe, Hetrick, & Parker, 2020). AI fits seamlessly into a landscape where efficiency is often more highly rewarded than curiosity. Faced with tasks that feel repetitive, decontextualized, or performative, many students learn that their survival depends on doing just enough, not necessarily doing it deeply.
When a student uses AI to write a discussion post or summarize an article, the act is not just a form of evasion—it’s often a judgment about the value of the task itself. If the assignment doesn’t ask them to do something personally meaningful, intellectually stimulating, or clearly useful for their future goals, it becomes an obligation to manage rather than a learning opportunity to embrace. This disconnect is further amplified by the unspoken norms that teach students to prioritize grades, speed, and performance over exploration, failure, and growth (Margolis, 2001). In such a climate, using AI can feel less like cheating and more like optimization. Students aren’t necessarily trying to game the system—they’re playing the game as they perceive it was designed.
The challenge, then, is not to eliminate AI from the learning process, but to design learning experiences that make authentic engagement the easier, more meaningful choice. Rather than dismissing AI use as evidence of student laziness or misconduct, educators might ask: What does the pattern of AI usage tell us about how students experience our courses? What if the real issue isn’t student behavior but assignment design and instructional culture? By attending to these questions, we shift the focus from enforcement to empathy—from trying to control student actions to trying to understand their motivations and constraints.
Process Over Product
If generative AI makes it easier for students to produce work without deeply engaging in the learning process, it invites faculty to reconsider what their assessments are really measuring. Too often, traditional assessments prioritize polished outputs—essays, quizzes, presentations—over the messy, iterative, and uncertain processes that lead to genuine understanding. These conventional formats may reward conformity and correctness, but they do little to cultivate the flexibility, reflection, and critical thinking required in a world shaped by automation and ambiguity.
In contrast, AI offers a lens through which educators can reassess their learning goals: Are students being asked to think or to comply? To demonstrate mastery or to demonstrate growth? Increasingly, the competencies that matter most in both life and work—such as collaboration, problem framing, ethical reasoning, and the ability to adapt—are not easily captured through static, time-bound tasks (National Academies of Sciences, Engineering, and Medicine, 2018). Nor are they easily outsourced to AI. These are precisely the skills that thrive when assessment shifts from evaluating finished products to examining students’ thinking and decision-making processes along the way.
Emerging research on assessment design suggests that when students are asked to reflect on how they used AI, evaluate its limitations, compare its outputs to their own, or build on AI-generated responses with original insights, they begin to see these tools not as shortcuts but as thought partners (Mollick & Mollick, 2023; Popenici & Kerr, 2017). This approach not only fosters academic integrity, but also helps students develop the judgment, discernment, and contextual understanding that AI cannot replicate. Moreover, process-oriented assessments are more inclusive. They allow students with diverse learning styles, linguistic backgrounds, and levels of prior knowledge to demonstrate learning in multiple ways. They also encourage metacognition—students thinking about their own thinking—which is known to enhance long-term retention and transfer of learning (Ambrose et al., 2010). Rethinking assessment does not mean lowering standards; it means realigning them with the kinds of learning we claim to value. When assessments emphasize authentic engagement over mere output, they become more resilient to automation—and more relevant to the lives students are preparing to lead.
Disciplinary Innovation
While discussions about AI in education often remain abstract or policy-focused, meaningful change is already taking root within disciplines as faculty reimagine how to integrate AI into the logic of their fields. Rather than banning its use outright, many instructors are embedding AI into course design in ways that mirror authentic disciplinary practices. The goal is not just to accommodate new tools, but to cultivate students’ judgment in using them—aligning learning with the actual thinking, creating, and problem-solving expected in professional contexts.
Creative Disciplines
In design, media production, and the arts, instructors are moving beyond static portfolio reviews and embracing reflective, process-based documentation. Students are asked to keep iterative journals that detail how their ideas evolved, what role AI tools played in generating or refining concepts, and how creative decisions were made at each stage. This encourages fluency with new tools while reinforcing core disciplinary values: originality, intentionality, and critique. As Sullivan (2010) and McArthur & White (2021) argue, creative practice is inherently dialogic, and integrating AI into that dialogue allows students to develop their own voices in conversation with machine-generated suggestions.
Simulating Complexity
In fields such as public administration, business, and marketing, instructors are shifting from case-study write-ups to scenario-based simulations and role-playing exercises. These assessments ask students to evaluate data, propose interventions, justify decisions, and communicate across stakeholder perspectives. AI-generated content may be introduced as one data source among many, prompting students to critique its assumptions, identify potential biases, or integrate it with qualitative insights. This simulates real-world complexity and ethical ambiguity—core aspects of professional judgment that cannot be easily automated (Farrell, 2023; Wiggins, 2016).
Modeling Scientific Reasoning
In the sciences, instructors are piloting assignments that blend AI-assisted analysis with human critique. For example, students might be given an AI-generated interpretation of experimental data and asked to assess its validity, identify methodological flaws, or propose alternative explanations. These tasks promote skills fundamental to scientific reasoning: skepticism, data literacy, and peer review. As Wieman (2017) and Holmes, Wieman, & Bonn (2015) have shown, such approaches encourage deeper engagement with the epistemological foundations of science—what counts as evidence, how claims are justified, and why precision matters.
Building AI Fluency Across the Curriculum
To meet the demands of a world increasingly shaped by automation, higher education must move beyond treating AI as an external threat and begin cultivating AI fluency as a foundational component of 21st-century learning. AI fluency goes beyond knowing how to use tools like ChatGPT or Midjourney—it encompasses the ability to understand how AI systems work, critically evaluate their outputs, use them ethically, and reflect on their broader social and disciplinary implications (Long & Magerko, 2020; Luckin et al., 2016).
Integrating AI fluency into the curriculum means designing assignments that treat AI not as a banned shortcut or unexamined assistant, but as a subject of inquiry. Students should be taught to interrogate the design of AI tools, including how they are trained, what kinds of data they rely on, and how they reflect or reinforce existing social biases. Courses across disciplines can include assignments that ask students to compare AI-generated outputs with human-created work, evaluate their quality and assumptions, or explore how different prompts lead to different results. These tasks cultivate what Holmes, Bialik, & Fadel (2019) describe as “cognitive partnership”—the ability to think with, through, and about technology, rather than simply accepting its outputs.
Building AI fluency involves normalizing reflective practice. Students should be encouraged to articulate how and why they used AI tools in the completion of an assignment: What choices did they make? What did the AI get right—or wrong? How did it shape their thinking? This kind of metacognitive work not only deepens learning, but also demystifies AI as a black box and empowers students to use it responsibly (Mollick & Mollick, 2023).
Crucially, AI fluency should not be siloed within STEM or computer science. Every field—from history to healthcare, literature to law—will be transformed by AI in some way, and students in every discipline deserve the opportunity to engage critically with those transformations. Developing AI fluency as a form of literacy positions students to become not just competent users of technology, but thoughtful citizens capable of shaping its role in society.
The task before educators is not merely to regulate AI use, but to reimagine what it means to be educated in an age of intelligent machines. By embedding AI fluency into course outcomes, assessment practices, and institutional values, higher education can help students navigate this new landscape with agency, insight, and integrity.
The text was generated in collaboration with Gemini (2.5 Flash), Google’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication.
References
Ambrose, Susan A., Michael W. Bridges, Michele DiPietro, Marsha C. Lovett, and Marie K. Norman. How Learning Works: Seven Research-Based Principles for Smart Teaching. San Francisco: Jossey-Bass, 2010.
Bertram Gallant, Tricia. Academic Integrity in the Twenty-First Century: A Teaching and Learning Imperative. San Francisco: Jossey-Bass, 2008.
Farrell, Paul. “Integrating AI into Public Administration Education: Risks and Rewards.” Journal of Public Affairs Education 29, no. 1 (2023): 45–58.
Holmes, Wayne, Maya Bialik, and Charles Fadel. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston: Center for Curriculum Redesign, 2019.
Holmes, N. G., Carl E. Wieman, and Doug A. Bonn. “Teaching Critical Thinking.” Proceedings of the National Academy of Sciences 112, no. 36 (2015): 11199–204.
Long, David, and Brian Magerko. “What Is AI Literacy? Competencies and Design Considerations.” In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. New York: ACM, 2020.
Luckin, Rose, Wayne Holmes, Mark Griffiths, and Laurie B. Forcier. Intelligence Unleashed: An Argument for AI in Education. London: Pearson, 2016.
Margolis, Eric. The Hidden Curriculum in Higher Education. New York: Routledge, 2001.
McArthur, Janine A., and Betty White. “Redesigning Creativity: AI, Agency, and Reflective Practice in the Visual Arts.” International Journal of Art & Design Education 40, no. 2 (2021): 437–51.
McCabe, Donald L., Linda Klebe Treviño, and Kenneth D. Butterfield. “Cheating in Academic Institutions: A Decade of Research.” Ethics & Behavior 11, no. 3 (2001): 219–32.
Mollick, Ethan, and Lilach Mollick. “Assigning AI: Seven Approaches for Students with Prompts.” Working paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4573321 (accessed July 8, 2025).
National Academies of Sciences, Engineering, and Medicine. How People Learn II: Learners, Contexts, and Cultures. Washington, D.C.: The National Academies Press, 2018.
Pascoe, Michaela C., Rosanna M. Hetrick, and Alexandra G. Parker. “The Impact of Stress on Students in Secondary School and Higher Education.” International Journal of Adolescence and Youth 25, no. 1 (2020): 104–12.
Popenici, Stefan C., and Sharon Kerr. “Exploring the Impact of Artificial Intelligence on Teaching and Learning in Higher Education.” Research and Practice in Technology Enhanced Learning 12, no. 1 (2017): 1–13.
Sullivan, Graeme. Art Practice as Research: Inquiry in the Visual Arts. Thousand Oaks, CA: SAGE Publications, 2010.
Wieman, Carl E. “The Similarities between Research in Education and Research in the Hard Sciences.” Educational Researcher 46, no. 6 (2017): 319–29.
Wiggins, Grant. “A True Test: Toward More Authentic and Equitable Assessment.” Educational Leadership 70, no. 6 (2016): 28–33.
The rapid integration of generative artificial intelligence (GenAI) into higher education has reignited a familiar moral panic around academic dishonesty. While much of the immediate institutional response has centered on detection and enforcement, this reaction echoes a familiar pattern—one that treats academic dishonesty as an individual moral failure rather than a symptom of broader systemic issues (Bertram Gallant, 2008). Students, faced with overloaded schedules, mounting debt, and an unforgiving emphasis on grades, often make pragmatic decisions about where to invest their time and effort gravitating toward efficiency and certainty over exploration and risk. In this environment, the ethical clarity around “cheating” becomes murky—not because students don’t understand right from wrong, but because the structures around them reward efficiency and penalize risk. AI tools such as ChatGPT or Grammarly simply fit into this equation as time-saving devices—much like calculators, Google, or even essay mills before them. What has shifted is not the motivation, but the accessibility. A student’s use of AI is a reflection of the transactional nature of how learning is often experienced in contemporary institutions.
The proliferation of AI tools has not so much introduced a new problem as it has exposed the fragility of existing educational structures. Before we focus on what AI changes, we must confront what it reveals: that higher education has long incentivized a culture of performance over learning. Research has shown that when students perceive academic work as irrelevant, overly procedural, or divorced from their goals and identities, they disengage—regardless of whether AI is available (Ambrose et al., 2010). In this light, the question is not “How do we catch them?” but rather, “What kind of learning environments have we built that make outsourcing feel logical or even necessary?” This reframing shifts the pedagogical conversation from one of compliance and control to one of capacity-building and curiosity. If we can use this moment to ask how to foster a culture of learning that values growth over grades, process over product, and understanding over output, then AI may serve not as a threat to education but as a catalyst for its reinvention.
Why Students Turn to AI
When students turn to generative AI to complete assignments, they are not necessarily demonstrating a lack of understanding or unethical behavior—they are often responding rationally to the conditions of their academic lives. In surveys and interviews, students routinely cite overwhelming workloads, unclear expectations, time scarcity, and mental health struggles as reasons they seek shortcuts or support tools (McCabe, Treviño, & Butterfield, 2001; Pascoe, Hetrick, & Parker, 2020). AI fits seamlessly into a landscape where efficiency is often more highly rewarded than curiosity. Faced with tasks that feel repetitive, decontextualized, or performative, many students learn that their survival depends on doing just enough, not necessarily doing it deeply.
When a student uses AI to write a discussion post or summarize an article, the act is not just a form of evasion—it’s often a judgment about the value of the task itself. If the assignment doesn’t ask them to do something personally meaningful, intellectually stimulating, or clearly useful for their future goals, it becomes an obligation to manage rather than a learning opportunity to embrace. This disconnect is further amplified by the unspoken norms that teach students to prioritize grades, speed, and performance over exploration, failure, and growth (Margolis, 2001). In such a climate, using AI can feel less like cheating and more like optimization. Students aren’t necessarily trying to game the system—they’re playing the game as they perceive it was designed.
The challenge, then, is not to eliminate AI from the learning process, but to design learning experiences that make authentic engagement the easier, more meaningful choice. Rather than dismissing AI use as evidence of student laziness or misconduct, educators might ask: What does the pattern of AI usage tell us about how students experience our courses? What if the real issue isn’t student behavior but assignment design and instructional culture? By attending to these questions, we shift the focus from enforcement to empathy—from trying to control student actions to trying to understand their motivations and constraints.
Process Over Product
If generative AI makes it easier for students to produce work without deeply engaging in the learning process, it invites faculty to reconsider what their assessments are really measuring. Too often, traditional assessments prioritize polished outputs—essays, quizzes, presentations—over the messy, iterative, and uncertain processes that lead to genuine understanding. These conventional formats may reward conformity and correctness, but they do little to cultivate the flexibility, reflection, and critical thinking required in a world shaped by automation and ambiguity.
In contrast, AI offers a lens through which educators can reassess their learning goals: Are students being asked to think or to comply? To demonstrate mastery or to demonstrate growth? Increasingly, the competencies that matter most in both life and work—such as collaboration, problem framing, ethical reasoning, and the ability to adapt—are not easily captured through static, time-bound tasks (National Academies of Sciences, Engineering, and Medicine, 2018). Nor are they easily outsourced to AI. These are precisely the skills that thrive when assessment shifts from evaluating finished products to examining students’ thinking and decision-making processes along the way.
Emerging research on assessment design suggests that when students are asked to reflect on how they used AI, evaluate its limitations, compare its outputs to their own, or build on AI-generated responses with original insights, they begin to see these tools not as shortcuts but as thought partners (Mollick & Mollick, 2023; Popenici & Kerr, 2017). This approach not only fosters academic integrity, but also helps students develop the judgment, discernment, and contextual understanding that AI cannot replicate. Moreover, process-oriented assessments are more inclusive. They allow students with diverse learning styles, linguistic backgrounds, and levels of prior knowledge to demonstrate learning in multiple ways. They also encourage metacognition—students thinking about their own thinking—which is known to enhance long-term retention and transfer of learning (Ambrose et al., 2010). Rethinking assessment does not mean lowering standards; it means realigning them with the kinds of learning we claim to value. When assessments emphasize authentic engagement over mere output, they become more resilient to automation—and more relevant to the lives students are preparing to lead.
Disciplinary Innovation
While discussions about AI in education often remain abstract or policy-focused, meaningful change is already taking root within disciplines as faculty reimagine how to integrate AI into the logic of their fields. Rather than banning its use outright, many instructors are embedding AI into course design in ways that mirror authentic disciplinary practices. The goal is not just to accommodate new tools, but to cultivate students’ judgment in using them—aligning learning with the actual thinking, creating, and problem-solving expected in professional contexts.
Creative Disciplines
In design, media production, and the arts, instructors are moving beyond static portfolio reviews and embracing reflective, process-based documentation. Students are asked to keep iterative journals that detail how their ideas evolved, what role AI tools played in generating or refining concepts, and how creative decisions were made at each stage. This encourages fluency with new tools while reinforcing core disciplinary values: originality, intentionality, and critique. As Sullivan (2010) and McArthur & White (2021) argue, creative practice is inherently dialogic, and integrating AI into that dialogue allows students to develop their own voices in conversation with machine-generated suggestions.
Simulating Complexity
In fields such as public administration, business, and marketing, instructors are shifting from case-study write-ups to scenario-based simulations and role-playing exercises. These assessments ask students to evaluate data, propose interventions, justify decisions, and communicate across stakeholder perspectives. AI-generated content may be introduced as one data source among many, prompting students to critique its assumptions, identify potential biases, or integrate it with qualitative insights. This simulates real-world complexity and ethical ambiguity—core aspects of professional judgment that cannot be easily automated (Farrell, 2023; Wiggins, 2016).
Modeling Scientific Reasoning
In the sciences, instructors are piloting assignments that blend AI-assisted analysis with human critique. For example, students might be given an AI-generated interpretation of experimental data and asked to assess its validity, identify methodological flaws, or propose alternative explanations. These tasks promote skills fundamental to scientific reasoning: skepticism, data literacy, and peer review. As Wieman (2017) and Holmes, Wieman, & Bonn (2015) have shown, such approaches encourage deeper engagement with the epistemological foundations of science—what counts as evidence, how claims are justified, and why precision matters.
Building AI Fluency Across the Curriculum
To meet the demands of a world increasingly shaped by automation, higher education must move beyond treating AI as an external threat and begin cultivating AI fluency as a foundational component of 21st-century learning. AI fluency goes beyond knowing how to use tools like ChatGPT or Midjourney—it encompasses the ability to understand how AI systems work, critically evaluate their outputs, use them ethically, and reflect on their broader social and disciplinary implications (Long & Magerko, 2020; Luckin et al., 2016).
Integrating AI fluency into the curriculum means designing assignments that treat AI not as a banned shortcut or unexamined assistant, but as a subject of inquiry. Students should be taught to interrogate the design of AI tools, including how they are trained, what kinds of data they rely on, and how they reflect or reinforce existing social biases. Courses across disciplines can include assignments that ask students to compare AI-generated outputs with human-created work, evaluate their quality and assumptions, or explore how different prompts lead to different results. These tasks cultivate what Holmes, Bialik, & Fadel (2019) describe as “cognitive partnership”—the ability to think with, through, and about technology, rather than simply accepting its outputs.
Building AI fluency involves normalizing reflective practice. Students should be encouraged to articulate how and why they used AI tools in the completion of an assignment: What choices did they make? What did the AI get right—or wrong? How did it shape their thinking? This kind of metacognitive work not only deepens learning, but also demystifies AI as a black box and empowers students to use it responsibly (Mollick & Mollick, 2023).
Crucially, AI fluency should not be siloed within STEM or computer science. Every field—from history to healthcare, literature to law—will be transformed by AI in some way, and students in every discipline deserve the opportunity to engage critically with those transformations. Developing AI fluency as a form of literacy positions students to become not just competent users of technology, but thoughtful citizens capable of shaping its role in society.
The task before educators is not merely to regulate AI use, but to reimagine what it means to be educated in an age of intelligent machines. By embedding AI fluency into course outcomes, assessment practices, and institutional values, higher education can help students navigate this new landscape with agency, insight, and integrity.
The text was generated in collaboration with Gemini (2.5 Flash), Google’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication.
References
Ambrose, Susan A., Michael W. Bridges, Michele DiPietro, Marsha C. Lovett, and Marie K. Norman. How Learning Works: Seven Research-Based Principles for Smart Teaching. San Francisco: Jossey-Bass, 2010.
Bertram Gallant, Tricia. Academic Integrity in the Twenty-First Century: A Teaching and Learning Imperative. San Francisco: Jossey-Bass, 2008.
Farrell, Paul. “Integrating AI into Public Administration Education: Risks and Rewards.” Journal of Public Affairs Education 29, no. 1 (2023): 45–58.
Holmes, Wayne, Maya Bialik, and Charles Fadel. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston: Center for Curriculum Redesign, 2019.
Holmes, N. G., Carl E. Wieman, and Doug A. Bonn. “Teaching Critical Thinking.” Proceedings of the National Academy of Sciences 112, no. 36 (2015): 11199–204.
Long, David, and Brian Magerko. “What Is AI Literacy? Competencies and Design Considerations.” In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. New York: ACM, 2020.
Luckin, Rose, Wayne Holmes, Mark Griffiths, and Laurie B. Forcier. Intelligence Unleashed: An Argument for AI in Education. London: Pearson, 2016.
Margolis, Eric. The Hidden Curriculum in Higher Education. New York: Routledge, 2001.
McArthur, Janine A., and Betty White. “Redesigning Creativity: AI, Agency, and Reflective Practice in the Visual Arts.” International Journal of Art & Design Education 40, no. 2 (2021): 437–51.
McCabe, Donald L., Linda Klebe Treviño, and Kenneth D. Butterfield. “Cheating in Academic Institutions: A Decade of Research.” Ethics & Behavior 11, no. 3 (2001): 219–32.
Mollick, Ethan, and Lilach Mollick. “Assigning AI: Seven Approaches for Students with Prompts.” Working paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4573321 (accessed July 8, 2025).
National Academies of Sciences, Engineering, and Medicine. How People Learn II: Learners, Contexts, and Cultures. Washington, D.C.: The National Academies Press, 2018.
Pascoe, Michaela C., Rosanna M. Hetrick, and Alexandra G. Parker. “The Impact of Stress on Students in Secondary School and Higher Education.” International Journal of Adolescence and Youth 25, no. 1 (2020): 104–12.
Popenici, Stefan C., and Sharon Kerr. “Exploring the Impact of Artificial Intelligence on Teaching and Learning in Higher Education.” Research and Practice in Technology Enhanced Learning 12, no. 1 (2017): 1–13.
Sullivan, Graeme. Art Practice as Research: Inquiry in the Visual Arts. Thousand Oaks, CA: SAGE Publications, 2010.
Wieman, Carl E. “The Similarities between Research in Education and Research in the Hard Sciences.” Educational Researcher 46, no. 6 (2017): 319–29.
Wiggins, Grant. “A True Test: Toward More Authentic and Equitable Assessment.” Educational Leadership 70, no. 6 (2016): 28–33.
