After a short break for the July 4th holiday, we’re back and ready to dive into the latest higher ed news in this week’s issue! Although we typically steer clear of politics, we can’t ignore the impacts of the One Big Beautiful Bill Act on higher education. From there, we look at the impact faculty subjectivity has on transfer students before diving into a recent study from the MIT Media Lab about AI and college student thinking.  

After reading today’s issue, share in the comments your thoughts about AI’s impact on thinking! 

Federal Policy Changes for Higher Education 

From What Republicans’ Sprawling Policy Bill Means for Higher Ed | The Chronicle of Higher Education 

The One Big Beautiful Bill Act introduces sweeping changes to higher education policy alongside broader policy reforms. 

Our Thoughts 

I know we try not to talk about politics in our weekly issue, but this week, it’s hard to avoid. We cannot ignore the impacts that this massive legislation will have on higher education. As always, whether you agree or disagree with the current administration, it’s important to be informed about how its ideological and fiscal priorities are poised to reshape how colleges, especially private institutions, operate and support their students. 

While I could have selected almost any higher ed publication for an article, I felt this one from The Chronicle offered a detailed, nuanced breakdown of the major federal policy shift with more neutral commentary. While the Senate version of the bill is less extreme than its House predecessor, the cumulative impact of changes to endowment taxation, graduate lending programs, and accountability measures still adds up to a significant “net negative” for the sector, as one expert in the article put it. 

Of all the changes in the bill, I think the most important one to consider is how lawmakers frame accountability for institutions. The final bill embraces a return to “gainful employment”-style thinking with an emphasis on an earnings-based metric for loan eligibility broadly applied to a range of academic programs. This policy may seem more palatable than earlier punitive risk-sharing proposals, but it still raises complex questions about how we define educational value—for example, whether programs in the arts, social work, or education can be fairly judged by post-graduation salary alone, without considering their broader societal contributions. Institutions will need to be ready not just to report outcomes but to rethink how they communicate program value while designing curriculum to meet evolving workforce needs. 

As this bill demonstrates, the period of political immunity for higher education seems to be at an end. It’s vital that we stay engaged with these policy changes, anticipate their ripple effects, and advocate for student-centered solutions that preserve access, affordability, and institutional mission. This legislation may not be the final word, but it’s a powerful indicator of the road ahead. 

Transfer Troubles 

From How Faculty Stall the Transfer Process | Inside Higher Ed  

A new report from the policy nonprofit MDRC found that transfer processes are not standardized or transparent, both of which often impact transfer student success.   

Our Thoughts  

As a former registrar, I found myself nodding along (perhaps a bit too enthusiastically) with the findings of this study. For so many, the transfer process is an opaque maze in which transfer courses go in and only sometimes come out on the other side as something resembling credit needed to complete a degree. Without clear guidance, transfer students struggle to map a path forward, and often, people think that transferring credit is a purely bureaucratic process when in fact it is a fundamentally academic one.  

This report provides an important window into the hidden but significant role faculty play in the credit transfer process and highlights the systemic barriers that transfer students face as they move from community colleges to four-year institutions. As transfer students becomes a key strategy in addressing enrollment goals, understanding the nuances of this process is vital. Faculty discretion, while valuable, is shown to contribute to inconsistency in credit evaluation, often resulting in students taking more time and spending more money to complete their degrees—a reality that undermines both student success and institutional effectiveness. 

Whether transfer students are truly less prepared for upper-level course work or that is simply a reflection of faculty perceptions about the academic rigor of two-year institutions is a question ripe for further research. We need to better understand whether these perceptions are grounded in evidence or shaped by implicit bias because the consequences for students are significant. This analysis is particularly relevant in the current landscape, where institutions are under pressure to improve retention and completion, especially for historically underserved students. Transfer students disproportionately come from underrepresented backgrounds and lower-income communities, and the credit loss they experience perpetuates inequity in degree attainment.  

Given the public sentiment about higher education, can we honestly continue to subjectively evaluate transfer credit without robust data on student success and transfer outcomes? 

ChatGPT’s Impact on Thinking 

From Your Brain on ChatGPT | MIT Media Lab 

A new study from the MIT Media Lab provides an early look at the impacts of using ChatGPT on an individual’s thinking process.  

Our Thoughts 

Don’t hate me for including an academic journal article, but this study has been in the news frequently this week, and so I thought it best to feature it instead of the assorted news articles referencing it. While I encourage you to read the study in full, in essence, it found that students who used ChatGPT to write an essay (in a controlled condition) remembered less about their essay than a group of students who wrote an essay without assistance from ChatGPT.  

While foundational research such as this is important, you’re probably also thinking “well, duh! Whenever you don’t complete a task yourself, you remember less about it.” And for me, at least, this is where it gets interesting because one of the philosophical debates about generative AI is: What is it doing to and for student learning? If students can outsource much of their college work to AI, what transformative experiences are they leaving our institutions with?   

Late last month, I read a piece in The New Yorker titled What Happens After A.I. Destroys College Writing?, which offered a more deeply reported view into how students are actually engaging with AI tools in their academic lives. Together, the study and the article paint a fuller picture of how AI is reshaping student learning, both in terms of cognitive impact and lived experience. Where the journal article offers slight empirical evidence that students remember less when they outsource writing to AI, The New Yorker piece brings this dynamic to life through student perspectives. The students featured in the article are using AI not just to complete assignments, but also to manage their time, navigate stress, and generally outsource much of the busy work in their lives. One student in the article even used ChatGPT to respond to the author’s emails requesting a time to meet.  

While most of the students profiled understood that asking AI to complete an entire assignment for them was cheating, their broader use of AI also underscores shifting ideas about AI, what we value, and the lived realities of overwhelmed college students who are balancing the simultaneous demands of school, work, and life. If students feel that entire components of their education are irrelevant, redundant, or inefficient, what does that say about the design of those experiences? Have we drifted too far from inquiry and critical thinking in our push for skills-based and outcomes-driven learning?  

Both Ethan Mollick and Derek Bruff argue that more research is needed to fully understand the implications of student use of AI on critical thinking and whether or not we should be worried. After all, learning is a complex process that we still don’t fully understand. But Mollick and Bruff both offer a useful reminder in their respective articles–if students are turning to AI because they feel their coursework is inefficient or disconnected from what they need, then maybe the issue isn’t just the tool but the task. Rather than rushing to ban AI or double down on surveillance, we should be asking ourselves what this moment is revealing about how students experience learning. Are we creating environments that invite curiosity and deep thinking, or ones that feel like a series of hoops to jump through?