Crucial Insights: Understanding College Financial Aid Dynamics
(00:02:56) Variety of College Financial Assistance Options
(00:05:18) Scholarships: Balancing Merit and Financial Need
(00:10:00) Student Selection Strategies in College Admissions
(00:21:40) Financial Aid Strategy at Competitive vs. Smaller Schools
(00:26:29) Major-based Financial Aid Allocation in Colleges
Scenario 1: You’re part of a cross-disciplinary group of faculty members working on the new general education requirement. By the end of the semester, your group has to produce a report for your institution’s administration. As you start to generate content, one member’s primary contributions focus on editing for style and mechanics, while the other members are focused on coming to an agreement on the content and recommendations.
Scenario 2: When you’re at the stage of drafting content for a grant, one member of a writing team uses strikethrough to delete a large chunk of text, with no annotation or explanation for the decision. The writing stops as individual participants angrily back channel.
Scenario 3: A team of colleagues decides to draft a vision statement for their unit on campus. They come to the process assuming that everyone has a shared idea about the vision and mission of their department. But when they each contribute a section to the draft, it becomes clear that they are not, in fact, on the same page about how they imagine the future of their unit’s work.
In the best case scenarios, we choose people to write with. People whom we trust, who we know will pull their weight and might even be fun to work with. However, many situations are thrust upon us rather than carefully selected. We have to complete a report, write an important email, articulate a new policy, compose and submit a grant proposal, author a shared memo, etc., with a bunch of folks we would likely not have chosen on our own.
Further, teams of employees tasked with writing are rarely selected because of their ability to write well with others, and many don’t have the language to talk through their preferred composing practices. Across professional writing and within higher education, the inability to work collaboratively on a writing product is the cause of endless strife and inefficiency. How can we learn how to collaborate with people we don’t choose to write with?
Instead of just jumping into the writing task, we argue for a quick conversation about writing before any team authorship even starts. If time is limited, this conversation doesn’t necessarily need to be more than 15 minutes (though devoting 30 minutes might be more effective) depending on the size of the writing team, but it will save you time—and, likely, frustration—in the long run.
Drawing from knowledge in our discipline—writing studies—we offer the following strategies for a guided conversation before starting any joint writing project. The quick convo should serve to surface assumptions about each member’s beliefs about writing, articulate the project’s goal and genre, align expectations, and plan the logistics.
As generative AI tools increasingly become integrated into the writing process, or even supplant parts of it, why should people write at all? Especially, why should we write together when people can be so troublesome?
Because writing is thinking. Certainly, the final writing product matters—a lot—but the reason getting to the product can be so hard is that writing requires critical thinking around project alignment. Asking AI to do the writing skips the hard planning, thinking and drafting work that will make the action/project/product that the writing addresses more successful.
Further, we do more than just complete a product/document when we write (either alone or together)—we surface shared assumptions, we come together through conversation and we build relationships. A final written product that has a real audience and purpose can be a powerful way to build community, and not just in the sense that it might make writers feel good. An engaged community is important, not just for faculty and staff happiness, but for productivity, for effective project completion and for long-term institutional stability.
To get the conversation started, talk to each other: Do real introductions in which participants talk about how they write and what works for them. Talk to yourself: Do a personal gut check, acknowledging any feelings/biases about group members, and commit to being aware of how these personal relationships/feelings might influence how you perceive and accept their contributions. Ideas about authorship, ownership and credit, including emotional investments in one’s own words, are all factors in how people approach writing with others.
Get on the same page about what the writing should do (purpose) and what form it should take (genre). Often the initial purpose of a writing project is that you’ve been assigned to a task—students may find it funny that so much faculty and staff writing at the university is essentially homework! Just like our students, we have to go beyond the bare minimum of meeting a requirement to find out why that writing product matters, what it responds to and what we want it to accomplish. To help the group come to agreement about form and writing conventions, find some effective examples of the type of project you’re trying to write and talk through what you like about each one.
Work to establish a sense of shared authorship—a “we” approach to the work. This is not easy, but it’s important to the success of the product and for the sake of your sanity. Confront style differences and try to come to agreement about not making changes to each other’s writing that don’t necessarily improve the content. There’s always that one person who wants to add “nevertheless” for every transition or write “next” instead of “then”—make peace with not being too picky. Or, agree to let AI come in at the end and talk about the proofreading recommendations from the nonperson writer.
This raises another question: With people increasingly integrating ChatGPT and its ilk into their processes (and Word/Google documents offering AI-assisted authorship tools), how comfortable is each member of the writing team with integrating AI-generated text into a final product?
Where will collaboration occur? In person, online? Synchronously or asynchronously? In a Google doc, on Zoom, in the office, in a coffee shop? Technologies and timing both influence process, and writers might have different ideas about how and when to write (ideas that might vary based on the tools that your team is going to use).
When will collaboration occur? Set deadlines and agree to stick with them. Be transparent about expectations from and for each member.
How will collaboration occur? In smaller groups/pairs, all together, or completely individually? How will issues be discussed and resolved?
Don’t:
I work at Marquette University. As a Roman Catholic, Jesuit university, we’re called to be an academic community that, as Pope John Paul II wrote, “scrutinize[s] reality with the methods proper to each academic discipline.” That’s a tall order, and I remain in the academy, for all its problems, because I find that job description to be the best one on offer, particularly as we have the honor of practicing this scrutinizing along with ever-renewing groups of students.
This bedrock assumption of what a university is continues to give me hope for the liberal educational project despite the ongoing neoliberalization of higher education and some administrators’ and educators’ willingness to either look the other way regarding or uncritically celebrate the generative software (commonly referred to as “generative artificial intelligence”) explosion over the last two years.
In the time since my last essay in Inside Higher Ed, and as Marquette’s director of academic integrity, I’ve had plenty of time to think about this and to observe praxis. In contrast to the earlier essay, which was more philosophical, let’s get more practical here about how access to generative software is impacting higher education and our students and what we might do differently.
At the academic integrity office, we recently had a case in which a student “found an academic article” by prompting ChatGPT to find one for them. The chat bot obeyed, as mechanisms do, and generated a couple pages of text with a title. This was not from any actual example of academic writing but instead was a statistically probable string of text having no basis in the real world of knowledge and experience. The student made a short summary of that text and submitted it. They were, in the end, not found in violation of Marquette’s honor code, since what they submitted was not plagiarized. It was a complex situation to analyze and interpret, done by thoughtful people who care about the integrity of our academic community: The system works.
In some ways, though, such activity is more concerning than plagiarism, for, at least when students plagiarize, they tend to know the ways they are contravening social and professional codes of conduct—the formalizations of our principles of working together honestly. In this case, the student didn’t see the difference between a peer-reviewed essay published by an academic journal and a string of probabilistically generated text in a chat bot’s dialogue box. To not see the difference between these two things—or to not care about that difference—is more disconcerting and concerning to me than straightforward breaches of an honor code, however harmful and sad such breaches are.
I already hear folks saying: “That’s why we need AI literacy!” We do need to educate our students (and our colleagues) on what generative software is and is not. But that’s not enough. Because one also needs to want to understand and, as is central to the Ignatian Pedagogical Paradigm that we draw upon at Marquette, one must understand in context.
Another case this spring term involved a student whom I had spent several months last fall teaching in a writing course that took “critical AI” as its subject matter. Yet this spring term the student still used a chat bot to “find a quote in a YouTube video” for an assignment and then commented briefly on that quote. The problem was that the quote used in the assignment does not appear in the selected video. It was a simulacrum of a quote; it was a string of probabilistically generated text, which is all generative software can produce. It did not accurately reflect reality, and the student did not cite the chat bot they’d copied and pasted from, so they were found in violation of the honor code.
Another student last term in the Critical AI class prompted Microsoft Copilot to give them quotations from an essay, which it mechanically and probabilistically did. They proceeded to base their three-page argument on these quotations, none of which said anything like what the author in question actually said (not even the same topic); their argument was based in irreality. We cannot scrutinize reality together if we cannot see reality. And many of our students (and colleagues) are, at least at times, not seeing reality right now. They’re seeing probabilistic text as “good enough” as, or conflated with, reality.
Let me point more precisely to the problem I’m trying to put my finger on. The student who had a chat bot “find” a quote from a video sent an email to me, which I take to be completely in earnest and much of which I appreciated. They ended the email by letting me know that they still think that “AI” is a really powerful and helpful tool, especially as it “continues to improve.” The cognitive dissonance between the situation and the student’s assertion took me aback.
Again: the problem with the “We just need AI literacy” argument. People tend not to learn what they do not want to learn. If our students (and people generally) do not particularly want to do work, and they have been conditioned by the use of computing and their society’s habits to see computing as an intrinsic good, “AI” must be a powerful and helpful tool. It must be able to do all the things that all the rich and powerful people say it does. It must not need discipline or critical acumen to employ, because it will “supercharge” your productivity or give you “10x efficiency” (whatever that actually means). And if that’s the case, all these educators telling you not to offload your cognition must be behind the curve, or reactionaries. At the moment, we can teach at least some people all about “AI literacy” and it will not matter, because such knowledge refuses to jibe with the mythology concerning digital technology so pervasive in our society right now.
If we still believe in the value of humanistic, liberal education, we cannot be quiet about these larger social systems and problems that shape our pupils, our selves and our institutions. We cannot be quiet about these limits of vision and questioning. Because not only do universities exist for the scrutinizing of reality with the various methods of the disciplines as noted at the outset of this essay, but liberal education also assumes a view of the human person that does not see education as instrumental but as formative.
The long tradition of liberal education, for all its complicity in social stratification down the centuries, assumes that our highest calling is not to make money, to live in comfort, to be entertained. (All three are all right in their place, though we must be aware of how our moneymaking, comfort and entertainment derive from the exploitation of the most vulnerable humans and the other creatures with whom we share the earth, and how they impact our own spiritual health.)
We are called to growth and wisdom, to caring for the common good of the societies in which we live—which at this juncture certainly involves caring for our common home, the Earth, and the other creatures living with us on it. As Antiqua et nova, the note released from the Vatican’s Dicastery for Culture and Education earlier this year (cited commendingly by secular ed-tech critics like Audrey Watters) reiterates, education plays its role in this by contributing “to the person’s holistic formation in its various aspects (intellectual, cultural, spiritual, etc.) … in keeping with the nature and dignity of the human person.”
These objectives of education are not being served by students using generative software to satisfy their instructors’ prompts. And no amount of “literacy” is going to ameliorate the situation on its own. People have to want to change, or to see through the neoliberal, machine-obsessed myth, for literacy to matter.
I do believe that the students I’ve referred to are generally striving for the good as they know how. On a practical level, I am confident they’ll go on to lead modestly successful lives as our society defines that term with regard to material well-being. I assume their motivation is not to cause harm or dupe their instructors; they’re taking part in “hustle” culture, “doing school” and possibly overwhelmed by all their commitments. Even if all this is indeed the case, liberal education calls us to more, and it’s the role of instructors and administrators to invite our students into that larger vision again and again.
If we refuse to give up on humanistic, liberal education, then what do we do? The answer is becoming clearer by the day, with plenty of folks all over the internet weighing in, though it is one many of us do not really want to hear. Because at least one major part of the answer is that we need to make an education genuinely oriented toward our students. A human-scale education, not an industrial-scale education (let’s recall over and over that computers are industrial technology). The grand irony of the generative software moment for education in neoliberal, late-capitalist society is that it is revealing so many of the limits we’ve been putting on education in the first place.
If we can’t “AI literacy” our educational problems away, we have to change our pedagogy. We have to change the ways we interact with our students inside the classroom and out: to cultivate personal relationships with them whenever possible, to model the intellectual life as something that is indeed lived out with the whole person in a many-partied dialogue stretching over millennia, decidedly not as the mere ability to move information around. This is not a time for dismay or defeat but an incitement to do the experimenting, questioning, joyful intellectual work many of us have likely wanted to do all along but have not had a reason to go off script for.
This probably means getting creative. Part of getting creative in our day probably means de-computing (as Dan McQuillan at the University of London labels it). To de-compute is to ask ourselves—given our ambient maximalist computing habits of the last couple decades—what is of value in this situation? What is important here? And then: Does a computer add value to this that it is not detracting from in some other way? Computers may help educators collect assignments neatly and read them clearly, but if that convenience is outweighed by constantly having to wonder if a student has simply copied and pasted or patch-written text with generative software, is the value of the convenience worth the problems?
Likewise, getting creative in our day probably means looking at the forms of our assessments. If the highly structured student essay makes it easier for instructors to assess because of its regularity and predictability, yet that very regularity and predictability make it a form that chat bots can produce fairly readily, well: 1) the value for assessing may not be worth the problems of teeing up chat bot–ifiable assignments and 2) maybe that wasn’t the best form for inviting genuinely insightful and exciting intellectual engagement with our disciplines’ materials in the first place.
I’ve experimented with research journals rather than papers, with oral exams as structured conversations, with essays that focus intently on one detail of a text and do not need introductions and conclusions and that privilege the student’s own voice, and other in-person, handmade, leaving-the-classroom kinds of assessments over the last academic year. Not everything succeeded the way I wanted, but it was a lively, interactive year. A convivial year. A year in which mostly I did not have to worry about whether students were automating their educations.
We have a chance as educators to rethink everything in light of what we want for our societies and for our students; let’s not miss it because it’s hard to redesign assignments and courses. (And it is hard.) Let’s experiment, for our own sakes and for our students’ sakes. Let’s experiment for the sakes of our institutions that, though they are often scoffed at in our popular discourse, I hope we believe in as vibrant communities in which we have the immense privilege of scrutinizing reality together.
In recent years, financial pressures have mounted across the entirety of the UK higher education (HE) sector, and have left many institutions in an exceptionally vulnerable position. In England alone, 43% of institutions are expected to face a financial deficit for 2024-25, prompting the House of Commons Education Select Committee to announce an inquiry into university finances and insolvency plans. Wide-ranging cost-cutting measures and redundancies are taking place across the sector, and the first institution (to our knowledge) has recently received emergency (bailout) funding from its regulator.
With the recent release of the full HESA Finance data for 2023-24, we now have an updated picture of the scale of the financial challenges facing higher education providers (HEPs). London Economics analysed HEPs’ financial data between 2018-19 and 2023-24 to better understand the current financial circumstances of the sector.
While other recent analyses focused on England only or covered other types of financial variables, here, we include providers across all of the UK and focus on three core financial indicators.
Our analysis focuses on four broad clusters of HEPs, following the approach originally developed by Boliver (2015), which categorises a total of 126 providers according to differences in their research activity, teaching quality, economic resources, and other characteristics. Cluster 1 includes just two institutions: the University of Oxford and the University of Cambridge. Cluster 2 is composed mainly of other Russell Group universities and the majority of other pre-1992 institutions (totalling 39 institutions). Cluster 3 includes the remaining pre-1992 universities and most post-1992 institutions (67 institutions), and Cluster 4 consists of around a quarter of post-1992 universities (totalling 18 institutions). The latest HESA Finance data were, unfortunately, not available for 8 of these clustered institutions, meaning that our analysis covers 118 institutions in total.
We focus on three key financial indicators (KFIs):
The key findings from the analysis are as follows:
The analysis shows that the financial position of UK higher education institutions is worsening, with all three indicators analysed (i.e. NCIF, NCA, and liquidity days) showing a decline in providers’ financial stability. Major challenges to the sector’s finances are set to continue, especially as the UK government is looking to further curb net migration through potential additional restrictions on international student visas. Therefore, the financial pressures on UK HE providers are expected to remain significant.
Our more detailed analysis, including a number of charts and additional findings on each indicator by university ‘cluster’, can be found on our website.
As the sector begins to prepare for REF 2029, with a greater emphasis on people, culture and environment and the breadth of forms of research and inclusive production, one critical issue demands renewed attention: the composition of the REF panels themselves. While much of the focus rightly centres on shaping fairer metrics and redefining engagement and impact, we should not overlook who is sitting at the table making the judgments.
If the Research Excellence Framework is to command the trust of the full spectrum of UK higher education institutions, then its panels must reflect the diversity of that spectrum. That means ensuring meaningful representation from a wide range of universities, including Russell Group institutions, pre- and post-92s, specialist colleges, teaching-led universities, and those with strong regional or civic missions.
Without diverse panel representation, there is a real risk that excellence will be defined too narrowly, inadvertently privileging certain types of research and institutional profiles over others.
Research excellence looks different in different contexts. A university with a strong regional engagement strategy might produce research that is deeply embedded in local communities, with impacts that are tangible but not easily measured by traditional academic metrics, but with clear international excellence. A specialist arts institution may demonstrate world-leading innovation through creative practice that doesn’t align neatly with standard research output categories.
The RAND report looking at the impact of research through the lens of the REF 2021 impact cases rightly recognised the importance of “hyperlocality” – and we need to ensure that research and impact is equally recognised in the forthcoming REF exercise.
UK higher education institutions are incredibly diverse, with different institutions having distinct missions, research priorities, and challenges. REF panels that lack representation from the full spectrum of institutions risks bias toward certain types of research outputs or methodologies, particularly those dominant in elite institutions.
Dominance of one type of institution on the panels could lead to an underappreciation of applied, practice-based, or interdisciplinary research, which is often produced by newer or specialist institutions.
Fair assessment depends not only on the criteria applied but also on the perspectives and experiences of those applying them. Including assessors from a wide range of institutional backgrounds helps surface blind spots and reduce unconscious bias. It also allows the panels to better understand and account for contextual factors, such as variations in institutional resources, missions, and community roles, when evaluating submissions.
Diverse panels also enhance the credibility of the process. The REF is not just a technical exercise; it shapes funding, reputations, and careers. A panel that visibly includes internationally recognised experts from across the breadth of the sector helps ensure that all institutions – and their staff – feel seen, heard, and fairly treated, and that a rigorous assessment of UK’s research prowess is made across the diversity of research outputs whatever their form.
Academic prestige and structural advantages (such as funding, legacy reputations, or networks) can skew assessment outcomes if not checked. Diversity helps counter bias that may favour research norms associated with more research established institutions. Panel diversity encourages broader thinking about what constitutes excellence, helping to recognize high-quality work regardless of institutional setting.
Plus there is the question of innovation. Fresh thinking often comes from the edges. A wider variety of voices on REF panels can challenge groupthink and encourage more inclusive and creative understandings of impact, quality, and engagement.
This isn’t about ticking boxes. True diversity means valuing the insights and expertise of panel members from all corners of the sector and ensuring they have the opportunity to shape outcomes, not just observe them. It also means recognising that institutional diversity intersects with other forms of diversity, including protected characteristics, professions and career stage, which must also be addressed.
The REF is one of the most powerful instruments shaping UK research culture. Who gets to define excellence in the international context has a profound impact on what research is done, how it is valued, and who is supported to succeed. REF panels should reflect the diversity of UK HEIs to ensure fairness, credibility, and a comprehensive understanding of research excellence across all contexts.
If REF 2029 is to live up to the sector’s ambitions for equity, inclusion, and innovation, then we must start with its panels. Without diverse panels, the REF risks perpetuating inequality and undervaluing the full range of scholarly contributions made across the sector, even as it evaluates universities on their own people, culture, and environment. The composition of those panels will be a litmus test for how seriously we take those commitments.
North Carolina’s Democratic governor has vetoed two bills the Republican-led General Assembly passed targeting what lawmakers dubbed “diversity, equity and inclusion”; “discriminatory practices”; and “divisive concepts” in public higher education.
Senate Bill 558 would have banned institutions from having offices “promoting discriminatory practices or divisive concepts” or focused on DEI. The bill defined “discriminatory practices” as “treating an individual differently [based on their protected federal law classification] solely to advantage or disadvantage that individual as compared to other individuals or groups.”
SB 558’s list of restricted divisive concepts mirrored the lists that Republicans have inserted into laws in other states, including the idea that “a meritocracy is inherently racist or sexist” or that “the rule of law does not exist.” The legislation would have prohibited colleges and universities from endorsing these concepts.
The bill would have also banned institutions from establishing processes “for reporting or investigating offensive or unwanted speech that is protected by the First Amendment, including satire or speech labeled as microaggression.”
In his veto message Thursday, Gov. Josh Stein wrote, “Diversity is our strength. We should not whitewash history, police dorm room conversations, or ban books. Rather than fearing differing viewpoints and cracking down on free speech, we should ensure our students learn from diverse perspectives and form their own opinions.”
Stein also vetoed House Bill 171, which would have broadly banned DEI from state government. It defined DEI in multiple ways, including the promotion of “differential treatment of or providing special benefits to individuals on the basis of race, sex, color, ethnicity, nationality, country of origin, or sexual orientation.”
“House Bill 171 is riddled with vague definitions yet imposes extreme penalties for unknowable violations,” Stein wrote in his HB 171 veto message. NC Newsline reported that lawmakers might still override the vetoes.
I am deeply worried about my vacuuming skills. I’ve always enjoyed vacuuming, especially with the vacuum cleaner I use. It has a clear dustbin, and there’s something cathartic about running it over the carpet in the upstairs hallway and seeing all the dust and debris it collects. I’m worried, however, because I keep outsourcing my downstairs vacuuming to the robot vacuum cleaner my wife and I bought a while back. With three kids and three dogs in the house, our family room sees a lot of foot traffic, and I save a lot of time by letting the robot clean up. What am I losing by relying on my robot vacuum to keep my house clean?
Not much, of course, and I’m not actually worried about losing my vacuuming skills. Vacuuming the family room isn’t a task that means much to me, and I’m happy to let the robot handle it. Doing so frees up my time for other tasks, preferably bird-watching out the kitchen window, but more often doing the dishes, a chore for which I don’t have a robot to help me. It’s entirely reasonable for me to offload a task I don’t care much about to the machines when the machines are right there waiting to do the work for me.
That was my response to a new high-profile study from a MIT Media Lab team led by Nataliya Kosmyna. Their preprint, “Your Brain on ChatGPT: Accumulation of Cognitive Debt When Using an AI Assistant for Essay Writing Task,” details their experiment. The team enlisted 54 adult participants to write short essays using SAT prompts over multiple sessions. A third of the participants were given access to ChatGPT to help with their essay writing, a third had access to any website they could reach through a Google search engine but were prohibited from using ChatGPT or other large language models and a third had no outside aids (the “brain-only” group). The researchers not only scored the quality of the participants’ essays, but they also used electroencephalography to record participants’ brain activity during these writing tasks.
The MIT team found that “brain connectivity systematically scaled down with the amount of external support.” While the brain-only group “exhibited the strongest, widest‑ranging [neural] networks,” AI assistance in the experiment “elicited the weakest overall coupling.” Moreover, the ChatGPT users were increasingly less engaged in the writing process over the multiple sessions, often just copying and pasting from the AI chat bot by the end of the experiment. They also had a harder time quoting anything from the essay they had just submitted compared to the brain-only group.
This study has inspired some dramatic headlines: “ChatGPT May Be Eroding Critical Thinking Skills” and “Study: Using AI Could Cost You Brainpower” and “Your Reliance on ChatGPT Might Be Really Bad for Your Brain.” Savvy news readers will key into the qualifiers in those headlines (“may,” “could,” “might”) instead of the scarier words, and the authors of the study have made an effort to prevent journalists and commentators from overplaying their results. From the study’s FAQ: “Is it safe to say that LLMs are, in essence, making us ‘dumber’? No!” As is usually the case in the AI-and-learning discourse, we need to slow our roll and look beyond the hyperbole to see what this new study does and doesn’t actually say.
I should state now for the record that I am not a neuroscientist. I can’t weigh in with any authority on the EEG analysis in this study, although others with expertise in this area have done so and have expressed concerns about the authors’ interpretation of EEG data. I do, however, know a thing or two about teaching and learning in higher education, having spent my career at university centers for teaching and learning helping faculty and other instructors across the disciplines explore and adopt evidence-based teaching practices. And it’s the teaching-and-learning context in the MIT study that caught my eye.
Consider the task that participants in this study, all students or staff at Boston-area universities, were given. They were presented with three SAT essay prompts and asked to select one. They were then given 20 minutes to write an essay in response to their chosen prompt, while wearing an EEG helmet of some kind. Each subject participated in a session like this three times over the course of a few months. Should we be surprised that the participants who had access to ChatGPT increasingly outsourced their writing to the AI chat bot? And that, in doing so, they were less and less engaged in the writing process?
I think the takeaway from this study is that if you give adults an entirely inauthentic task and access to ChatGPT, they’ll let the robot do the work and save their energy for something else. It’s a reasonable and perhaps cognitively efficient thing to do. Just like I let my robot vacuum cleaner tidy up my family room while I do the dishes or look for an eastern wood pewee in my backyard.
Sure, writing an SAT essay is a cognitively complex task, and it is perhaps an important skill for a certain cohort of high school students. But what this study shows is what generative AI has been showing higher ed since ChatGPT launched in 2022: When we ask students to do things that are neither interesting nor relevant to their personal or professional lives, they look for shortcuts.
John Warner, an Inside Higher Ed contributor and author of More Than Words: How to Think About Writing in the Age of AI (Basic Books), wrote about this notion in his very first post about ChatGPT in December 2022. He noted concerns that ChatGPT would lead to the end of high school English, and then asked, “What does it say about what we ask students to do in school that we assume they will do whatever they can to avoid it?”
What’s surprising to me about the new MIT study is that we are more than two years into the ChatGPT era and we’re still trying to assess the impact of generative AI on learning by studying how people respond to boring essay assignments. Why not explore how students use AI during more authentic learning tasks? Like law students drafting contracts and client memos or composition students designing multimodal projects or communications students attempting impossible persuasive tasks? We know that more authentic assignments motivate deeper engagement and learning, so why not turn students loose on those assignments and then see what impact AI use might have?
There’s another, more subtle issue with the discourse around generative AI in learning that we can see in this study. In the “Limitations and Future Work” section of the preprint, the authors write, “We did not divide our essay writing task into subtasks like idea generation, writing, and so on.” Writing an essay is a more complicated cognitive process than vacuuming my family room, but critiques of the use of AI in writing are often focused on outsourcing the entire writing process to a chat bot. That seems to be what the participants did in this study, and it is perhaps a natural use of AI when given an uninteresting task.
However, when a task is interesting and relevant, we’re not likely to hand it off entirely to ChatGPT. Savvy AI users might get a little AI help with parts of the task, like generating examples or imagining different audiences or tightening our prose. AI can’t do all the things that a trained human editor can, but, as writing instructor (and human editor) Heidi Nobles has argued, AI can be a useful substitute when a human editor isn’t readily available. It’s a stretch to say that my robot vacuum cleaner and I collaborate to keep the house tidy, but it’s reasonable to think that someone invested in a complex activity like writing might use generative AI as what Ethan Mollick calls a “co-intelligence.”
If we’re going to better understand generative AI’s impact on learning, something that will be critical for higher education to do to keep its teaching mission relevant, we have to look at the best uses of AI and the best kinds of learning activities. That research is happening, thankfully, but we shouldn’t expect simple answers. After all, learning is more complicated than vacuuming.
A career is much like a work of art: We select an area to study—a medium, of sorts—in which to pursue an interest or a desire. We start by obtaining foundational knowledge before creating something that contributes to the greater society. Some may benefit from what is produced; others may not. Some will appreciate the output; others will not gain much, if anything, from what is constructed. At the center of the result is the artist themselves. Others along the way lend their own expertise, time and insights toward the outcome. However, it is the unique skills, perspectives, knowledge, choices and behaviors of the artist that determine what is created.
We are all artists in the making. We have a profession in which we have chosen to engage. As graduate students or postdoctoral scholars, we gain the foundations needed for our chosen discipline. During our time training in higher education, we focus on acquiring technical skills and techniques to contribute to sustaining and expanding our fields of study. We set upon the path to becoming experts through trial and error, discovery and disappointments, gains and losses.
Like with a work of art, we may start from a place of uncertainty: What can appear to be confounding fragments of a greater idea can coalesce in ways that surprise and satisfy us. We pull together parts and pieces to make something whole or even construct something unique. Yet while we are engaged in this creative and intellectual process, we must also work within defined boundaries. Expectations and ethical standards guide our professional conduct. Understanding these nuances is essential to forming a professional identity.
Each profession carries its own expectations for behavior, decision-making and accountability. Cambridge defines “professionalism” as “the qualities connected with trained and skilled people.” We can have strong technical skills and deep knowledge in our particular disciplines; however, these alone do not guarantee our level of professionalism when we are actually in the workforce interfacing with supervisors, colleagues, team members and clients.
While having the foundational skills and understanding may guarantee some success within a career, it is actually the capacity for acquiring and applying what I’ve termed “human-centered competencies” that ensures a greater degree of career fulfillment. Human-centered competencies consist of behaviors that involve a deeper sense of self-awareness. Recognizing and managing our behaviors, and understanding how they may impact those interacting with us, helps us relate to others in ways that forge effective communication, efficacious decision-making, constructive conflict resolution and fruitful work endeavors.
With this in mind, let’s explore the ART of professionalism through some simple reflective exercises. Think about the questions presented here as intended to encourage an honest reflection on the art we are creating within our own spheres of influence.
Our attitude is an outward reflection of what we are thinking and how we are feeling. Our attitude toward an assignment, toward a co-worker, toward ourselves or toward life itself is exemplified through our behaviors. Are we respectful and kind to others? Do we smile at who we see in the mirror or constantly chastise ourselves for what we have done (or not done)? Do we tend to jump to negative conclusions regarding those with whom we interact? Do we shake hands, look people in the eye and smile? Or are we downcast, avoidant and possibly even surly? How do we appear? Are we dressed for the part—one in which we want to be respected and taken seriously—or do we look like we would rather be on the couch bingeing on Netflix and eating potato chips?
Our attitude says a lot about ourselves, and sometimes we do not even have to open our mouths to reveal it. Our internal dialogue can have an impact on our external behaviors, so we need to be aware of our attitude. We can improve it, if needed. We can start by examining how we carry ourselves, as our posture and physical appearance convey nonverbal messages. How we show up is also important to consider. Are we prepared for meetings? Do we speak up with confidence? Do we actively listen to others and appreciate their contributions?
Our attitude reflects our frame of mind, and we illustrate who we are through our attitude. We also should keep in mind that each of us represents more than ourselves; we reflect the values and credibility of our professional communities.
Within the work environment we all have duties, projects or assignments that we manage. Responsibility involves taking ownership of our decisions, our actions and our outcomes. Work involves interdependence; it is rare that we can achieve a goal all on our own. Even artists need people who help them develop their skills, manufacture their tools, market their work and provide venues to exhibit their talent. Within the workplace, we will need others and others will need us.
Responsibility, therefore, is a crucial competency to have as a professional. Exhibiting responsibility involves both dependability and accountability. Being dependable is a choice, and this can involve time management, setting boundaries and fulfilling obligations; we show up on time and we follow through with what we say we are going to do. Accountability means that we acknowledge when things have not worked out as planned, we recognize our contributions to successes and we face the consequences of our decisions and actions, whether positive or negative. Instead of evaluating situations as win or lose, we can choose to look at outcomes as win or learn. Whether we experience a victory or suffer a defeat, we can always learn from the process. In essence, responsibility is about us doing our part so that we contribute, in a mindful way, to the success and well-being of our colleagues and co-workers.
Trust is by far the most important component of professionalism. Trust looks different in a professional atmosphere than it does in personal life. Trust involves being genuine with others. We want to be able to count on others and to believe that they are being honest with us. The same expectations for honesty should hold when it comes to our own behavior.
Trust involves being reliable, striving to meet expectations, fulfilling obligations, avoiding gossip and feeling secure in the knowledge that harm will not be done or betrayal will not occur. As professionals, it is imperative that we are trustworthy, as this is a fundamental component of human interactions. Being competent at trust involves building goodwill, being cooperative, displaying integrity, adhering to our values, engaging in sincere interactions and forming strong alliances. Without trust, bonds are broken, relationships are destroyed and organizations fail. We need to examine our words and our actions to evaluate how trustworthy we may seem to others. Being empathetic, reliable and ethical will serve us well as we pursue our passion and contribute our talents to the well-being of those with whom we work, as well as those who benefit from what our teams and organizations produce.
As professionals, we are not just building careers; we are creating something much more enduring: a body of work, a reputation, a legacy. The skills we acquire in our chosen disciplines are only part of the equation. Equally important are the attitudes we embody, the responsibilities we accept and the trust we build. It takes time, reflection and endurance to create a great work of art; the same is true for our careers. The process may be unpredictable, but the core elements—our values, our character and our professionalism—will determine how our work is received and remembered.
So ask yourself: What kind of professional artist do you want to be? What are you creating through your everyday choices? How will your ART— attitude, responsibility and trust—shape your path forward?
The promise of higher education as a pathway to opportunity has never been more important, or more precarious.
While overall university participation has reached record levels, this headline figure masks a troubling reality: where you’re born in England increasingly determines whether you’ll ever set foot on a university campus. And even once students do get their foot in the door, they might not have the support system in place – financially as well as academically – to succeed and thrive.
It is in this context that the UPP Foundation has today published the concluding paper in its widening participation inquiry. Mission Critical: six recommendations for the widening participation agenda is our attempt to fill in the gaps that the government left in its opportunity mission around widening participation, and to provide targets and mechanisms by which it can achieve success in this area.
For years, the biggest single aim of widening participation work has been “getting in” – ensuring that young people from disadvantaged backgrounds are supported to attend university, most often by undertaking a bachelor’s degree as a residential student. The aim of growing participation has come under political scrutiny in recent years and is no longer an accepted mission across the political spectrum.
But as our inquiry’s earlier papers highlight, there remains significant gaps in participation. Although more young people are going to university than ever before, there are stark disparities in the rates at which young people from different parts of the country attend university. If we believe, as I do, that talent is not simply concentrated in London and the South East, then by implication if opportunity is spread out more evenly, participation in higher education needs to grow.
That’s why our first recommendation is a “triple lock” widening participation target. This includes a gap of no more than ten percentage points between the highest and lowest regional HE participation rates; plus a 50 per cent floor for progression to HE at 18-19 across all regions; and a target for 70 per cent of the whole English population to have studied at level 4 or above by the age of 25, as advocated by Universities UK. Meeting these targets will ensure that “getting in” really is for everyone.
But this is not enough in isolation. The people we spoke to in Doncaster and Nottingham made it clear that “getting on” and “getting out” are equally important parts of the widening participation struggle – with the cost of learning a major barrier to full participation in university life.
With that in mind, we’re calling for the restoration of maintenance loans to 2021 real-terms levels by the end of the decade, as well as additional maintenance grants for those eligible for free school meals in the last six years.
We also want universities that are currently spending millions of pounds on bursaries and hardship funds to put that money towards outreach in the most challenging cold spots, as well as ensuring that the wider student experiences that undergrads cherish are available to all. That’s why it makes sense for a proportion of the proceeds from the proposed international student fee levy, if introduced, to be ring fenced to support an expanded access and participation plan regime, prioritising disadvantaged students from cold spot backgrounds.
Finally, widening participation needs to address the short-term mindset that grips young people both before and during their time at university.
Young people are more mindful of their finances than ever before, with many opting out of university in favour of a job in places where graduate careers are scarce and those who do choose to attend keeping one eye on their present and future earnings even before they’ve graduated.
If we are to revitalise the widening participation agenda, we have to bring employability to the fore, both by reconfiguring the Office for Students’ B3 metric on positive student outcomes and by bringing employers into the design and outputs of university study. There are already fantastic examples of this working in practice across the sector, such as at London South Bank’s energy advice centre and Bristol University’s career- and community-oriented dental school. It’s time for the sector to pick up these ideas and run with them.
The young person in Doncaster with the same grades and aspirations as their counterpart in Surrey faces not just different odds of getting to university, but different expectations about what’s possible. When we fail to address these disparities, we’re not just perpetuating inequality, we’re actively weakening the economic foundations that the whole country depends on.
What our new report offers is a chance to refocus the widening participation agenda around a series of ambitious but achievable targets. Getting in, getting on and getting out are all crucial parts of the higher education cycle, especially for those who otherwise wouldn’t attend. If the government want to take their widening participation priorities seriously, all three aspects need to take their place in the sun.
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Parking permits. Desk space. Access cards.
Ordered to bring back roughly 1,300 laid-off workers, the U.S. Department of Education instead has spent weeks ostensibly working on the logistics. Meanwhile, the Trump administration wants the U.S. Supreme Court to decide they don’t have to restore those jobs after all.
The legal argument over the job status of Education Department workers is testing the extent to which President Donald Trump and Education Secretary Linda McMahon can reshape the federal bureaucracy without congressional approval.
The employees, meanwhile, remain in limbo, getting paid for jobs they aren’t allowed to perform.
An analysis done by the union representing Education Department employees estimates the government is spending about $7 million a month for workers not to work. That figure does not include supervisors who are not part of the American Federation of Government Employee Local 252.
“It is terribly inefficient,” said Brittany Coleman, chief steward for AFGE Local 252 and an attorney in the Office for Civil Rights. “The American people are not getting what they need because we can’t do our jobs.”
McMahon announced the layoffs in March, a week after she was confirmed by the Senate, and described them as a first step toward dismantling the Education Department. A few days later, Trump signed an executive order directing McMahon to do everything in her legal authority to shut down the department.
The Somerville and Easthampton school districts in Massachusetts, along with the American Federation of Teachers, other education groups, and 21 Democratic attorneys general sued McMahon over the cuts. They argued the layoffs were so extensive that the Education Department would not be able to perform its duties under the law.
The layoffs hit the Office for Civil Rights, Federal Student Aid, and the Institute of Education Sciences particularly hard. These agencies are responsible for federally mandated work within the Education Department. By law, only Congress can get rid of the Education Department.
U.S. District Court Judge Myong Joun agreed, issuing a sweeping preliminary injunction in May that ordered the Education Department to bring laid off employees back to work and blocked any further effort to dismantle or substantively restructure the department.
The Trump administration sought a stay of that order, and the case is on the emergency docket of the Supreme Court, where a decision could come any day.
In the administration’s request to the Supreme Court, Solicitor General John Sauer argued that the harms the various plaintiffs had described were largely hypothetical, that they had not shown the department wasn’t fulfilling its duties, and that they didn’t have standing to sue because layoffs primarily affect department employees, not states, school districts, and education organizations.
Sauer further argued that the injunction violates the separation of powers, putting the judicial branch in charge of employment decisions that are the purview of the executive branch.
“The injunction rests on the untenable assumption that every terminated employee is necessary to perform the Department of Education’s statutory functions,” Sauer wrote in a court filing. “That injunction effectively appoints the district court to a Cabinet role and bars the Executive Branch from terminating anyone.”
The Supreme Court, with a conservative 6-3 majority, has been friendlier to the administration’s arguments than lower court judges. Already the court has allowed cuts to teacher training grants to go through while a lawsuit works its way through the courts. And it has halted the reinstatement of fired probationary workers.
The Education Department did not immediately respond to a request for comment.
Last week, Joun issued a separate order telling the Education Department that it must reinstate employees in the Office for Civil Rights. The Victims Rights Law Center and other groups had described thousands of cases left in limbo, with children suffering severe bullying or unable to safely return to school.
Meanwhile, the Education Department continues to file weekly updates with Joun about the complexities of reinstating the laid-off employees. In these court filings, Chief of Staff Rachel Oglesby said an “ad hoc committee of senior leadership” is meeting weekly to figure out where employees might park and where they should report to work.
Since the layoffs, the department has closed regional offices, consolidated offices in three Washington, D.C. buildings into one, reduced its contracts for parking space, and discontinued an interoffice shuttle.
In the most recent filing, Oglesby said the department is working on a “reintegration plan.”
Coleman said she finds these updates “laughable.”
“If you are really willing to do what the court is telling you to do, then your working group would have figured out a way to get us our laptops,” she said.
This story was originally published by Chalkbeat. Chalkbeat is a nonprofit news site covering educational change in public schools. Sign up for their newsletters at ckbe.at/newsletters.
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