Higher Education Inquirer : Search results for AI

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Monday, January 27, 2025

Nvidia, Microsoft Stock Fall in Wake of New AI Model From DeepSeek (Bloomberg Podcasts)

Tuesday, January 21, 2025

Student Booted from PhD Program Over AI Use (Derek Newton/The Cheat Sheet)

336: Student Says He Was Expelled Over False Accusation of AI Use

Issue 336

This one is going to take a hot minute to dissect. Minnesota Public Radio (MPR) has the story.

The plot contours are easy. A PhD student at the University of Minnesota was accused of using AI on a required pre-dissertation exam and removed from the program. He denies that allegation and has sued the school — and one of his professors — for due process violations and defamation respectively.

Starting the case.

The coverage reports that:

all four faculty graders of his exam expressed “significant concerns” that it was not written in his voice. They noted answers that seemed irrelevant or involved subjects not covered in coursework. Two instructors then generated their own responses in ChatGPT to compare against his and submitted those as evidence against Yang. At the resulting disciplinary hearing, Yang says those professors also shared results from AI detection software. 

Personally, when I see that four members of the faculty unanimously agreed on the authenticity of his work, I am out. I trust teachers.

I know what a serious thing it is to accuse someone of cheating; I know teachers do not take such things lightly. When four go on the record to say so, I’m convinced. Barring some personal grievance or prejudice, which could happen, hard for me to believe that all four subject-matter experts were just wrong here. Also, if there was bias or petty politics at play, it probably would have shown up before the student’s third year, not just before starting his dissertation.

Moreover, at least as far as the coverage is concerned, the student does not allege bias or program politics. His complaint is based on due process and inaccuracy of the underlying accusation.

Let me also say quickly that asking ChatGPT for answers you plan to compare to suspicious work may be interesting, but it’s far from convincing — in my opinion. ChatGPT makes stuff up. I’m not saying that answer comparison is a waste, I just would not build a case on it. Here, the university didn’t. It may have added to the case, but it was not the case. Adding also that the similarities between the faculty-created answers and the student’s — both are included in the article — are more compelling than I expected.

Then you add detection software, which the article later shares showed high likelihood of AI text, and the case is pretty tight. Four professors, similar answers, AI detection flags — feels like a heavy case.

Denied it.

The article continues that Yang, the student:

denies using AI for this exam and says the professors have a flawed approach to determining whether AI was used. He said methods used to detect AI are known to be unreliable and biased, particularly against people whose first language isn’t English. Yang grew up speaking Southern Min, a Chinese dialect. 

Although it’s not specified, it is likely that Yang is referring to the research from Stanford that has been — or at least ought to be — entirely discredited (see Issue 216 and Issue 251). For the love of research integrity, the paper has invented citations — sources that go to papers or news coverage that are not at all related to what the paper says they are.

Does anyone actually read those things?

Back to Minnesota, Yang says that as a result of the findings against him and being removed from the program, he lost his American study visa. Yang called it “a death penalty.”

With friends like these.

Also interesting is that, according to the coverage:

His academic advisor Bryan Dowd spoke in Yang’s defense at the November hearing, telling panelists that expulsion, effectively a deportation, was “an odd punishment for something that is as difficult to establish as a correspondence between ChatGPT and a student’s answer.” 

That would be a fair point except that the next paragraph is:

Dowd is a professor in health policy and management with over 40 years of teaching at the U of M. He told MPR News he lets students in his courses use generative AI because, in his opinion, it’s impossible to prevent or detect AI use. Dowd himself has never used ChatGPT, but he relies on Microsoft Word’s auto-correction and search engines like Google Scholar and finds those comparable. 

That’s ridiculous. I’m sorry, it is. The dude who lets students use AI because he thinks AI is “impossible to prevent or detect,” the guy who has never used ChatGPT himself, and thinks that Google Scholar and auto-complete are “comparable” to AI — that’s the person speaking up for the guy who says he did not use AI. Wow.

That guy says:

“I think he’s quite an excellent student. He’s certainly, I think, one of the best-read students I’ve ever encountered”

Time out. Is it not at least possible that professor Dowd thinks student Yang is an excellent student because Yang was using AI all along, and our professor doesn’t care to ascertain the difference? Also, mind you, as far as we can learn from this news story, Dowd does not even say Yang is innocent. He says the punishment is “odd,” that the case is hard to establish, and that Yang was a good student who did not need to use AI. Although, again, I’m not sure how good professor Dowd would know.

As further evidence of Yang’s scholastic ability, Dowd also points out that Yang has a paper under consideration at a top academic journal.

You know what I am going to say.

To me, that entire Dowd diversion is mostly funny.

More evidence.

Back on track, we get even more detail, such as that the exam in question was:

an eight-hour preliminary exam that Yang took online. Instructions he shared show the exam was open-book, meaning test takers could use notes, papers and textbooks, but AI was explicitly prohibited. 
Exam graders argued the AI use was obvious enough. Yang disagrees. 
Weeks after the exam, associate professor Ezra Golberstein submitted a complaint to the U of M saying the four faculty reviewers agreed that Yang’s exam was not in his voice and recommending he be dismissed from the program. Yang had been in at least one class with all of them, so they compared his responses against two other writing samples. 

So, the exam expressly banned AI. And we learn that, as part of the determination of the professors, they compared his exam answers with past writing.

I say all the time, there is no substitute for knowing your students. If the initial four faculty who flagged Yang’s work had him in classes and compared suspicious work to past work, what more can we want? It does not get much better than that.

Then there’s even more evidence:

Yang also objects to professors using AI detection software to make their case at the November hearing.  
He shared the U of M’s presentation showing findings from running his writing through GPTZero, which purports to determine the percentage of writing done by AI. The software was highly confident a human wrote Yang’s writing sample from two years ago. It was uncertain about his exam responses from August, assigning 89 percent probability of AI having generated his answer to one question and 19 percent probability for another. 
“Imagine the AI detector can claim that their accuracy rate is 99%. What does it mean?” asked Yang, who argued that the error rate could unfairly tarnish a student who didn’t use AI to do the work.  

First, GPTZero is junk. It’s reliably among the worst available detection systems. Even so, 89% is a high number. And most importantly, the case against Yang is not built on AI detection software alone, as no case should ever be. It’s confirmation, not conviction. Also, Yang, who the paper says already has one PhD, knows exactly what an accuracy rate of 99% means. Be serious.

A pattern.

Then we get this, buried in the news coverage:

Yang suggests the U of M may have had an unjust motive to kick him out. When prompted, he shared documentation of at least three other instances of accusations raised by others against him that did not result in disciplinary action but that he thinks may have factored in his expulsion.  
He does not include this concern in his lawsuits. These allegations are also not explicitly listed as factors in the complaint against him, nor letters explaining the decision to expel Yang or rejecting his appeal. But one incident was mentioned at his hearing: in October 2023, Yang had been suspected of using AI on a homework assignment for a graduate-level course. 
In a written statement shared with panelists, associate professor Susan Mason said Yang had turned in an assignment where he wrote “re write it, make it more casual, like a foreign student write but no ai.”  She recorded the Zoom meeting where she said Yang denied using AI and told her he uses ChatGPT to check his English.
She asked if he had a problem with people believing his writing was too formal and said he responded that he meant his answer was too long and he wanted ChatGPT to shorten it. “I did not find this explanation convincing,” she wrote. 

I’m sorry — what now?

Yang says he was accused of using AI in academic work in “at least three other instances.” For which he was, of course, not disciplined. In one of those cases, Yang literally turned in a paper with this:

“re write it, make it more casual, like a foreign student write but no ai.” 

He said he used ChatGPT to check his English and asked ChatGPT to shorten his writing. But he did not use AI. How does that work?

For that one where he left in the prompts to ChatGPT:

the Office of Community Standards sent Yang a letter warning that the case was dropped but it may be taken into consideration on any future violations. 

Yang was warned, in writing.

If you’re still here, we have four professors who agree that Yang’s exam likely used AI, in violation of exam rules. All four had Yang in classes previously and compared his exam work to past hand-written work. His exam answers had similarities with ChatGPT output. An AI detector said, in at least one place, his exam was 89% likely to be generated with AI. Yang was accused of using AI in academic work at least three other times, by a fifth professor, including one case in which it appears he may have left in his instructions to the AI bot.

On the other hand, he did say he did not do it.

Findings, review.

Further:

But the range of evidence was sufficient for the U of M. In the final ruling, the panel — comprised of several professors and graduate students from other departments — said they trusted the professors’ ability to identify AI-generated papers.

Several professors and students agreed with the accusations. Yang appealed and the school upheld the decision. Yang was gone. The appeal officer wrote:

“PhD research is, by definition, exploring new ideas and often involves development of new methods. There are many opportunities for an individual to falsify data and/or analysis of data. Consequently, the academy has no tolerance for academic dishonesty in PhD programs or among faculty. A finding of dishonesty not only casts doubt on the veracity of everything that the individual has done or will do in the future, it also causes the broader community to distrust the discipline as a whole.” 

Slow clap.

And slow clap for the University of Minnesota. The process is hard. Doing the review, examining the evidence, making an accusation — they are all hard. Sticking by it is hard too.

Seriously, integrity is not a statement. It is action. Integrity is making the hard choice.

MPR, spare me.

Minnesota Public Radio is a credible news organization. Which makes it difficult to understand why they chose — as so many news outlets do — to not interview one single expert on academic integrity for a story about academic integrity. It’s downright baffling.

Worse, MPR, for no specific reason whatsoever, decides to take prolonged shots at AI detection systems such as:

Computer science researchers say detection software can have significant margins of error in finding instances of AI-generated text. OpenAI, the company behind ChatGPT, shut down its own detection tool last year citing a “low rate of accuracy.” Reports suggest AI detectors have misclassified work by non-native English writers, neurodivergent students and people who use tools like Grammarly or Microsoft Editor to improve their writing. 
“As an educator, one has to also think about the anxiety that students might develop,” said Manjeet Rege, a University of St. Thomas professor who has studied machine learning for more than two decades. 

We covered the OpenAI deception — and it was deception — in Issue 241, and in other issues. We covered the non-native English thing. And the neurodivergent thing. And the Grammarly thing. All of which MPR wraps up in the passive and deflecting “reports suggest.” No analysis. No skepticism.

That’s just bad journalism.

And, of course — anxiety. Rege, who please note has studied machine learning and not academic integrity, is predictable, but not credible here. He says, for example:

it’s important to find the balance between academic integrity and embracing AI innovation. But rather than relying on AI detection software, he advocates for evaluating students by designing assignments hard for AI to complete — like personal reflections, project-based learnings, oral presentations — or integrating AI into the instructions. 

Absolute joke.

I am not sorry — if you use the word “balance” in conjunction with the word “integrity,” you should not be teaching. Especially if what you’re weighing against lying and fraud is the value of embracing innovation. And if you needed further evidence for his absurdity, we get the “personal reflections and project-based learnings” buffoonery (see Issue 323). But, again, the error here is MPR quoting a professor of machine learning about course design and integrity.

MPR also quotes a student who says:

she and many other students live in fear of AI detection software.  
“AI and its lack of dependability for detection of itself could be the difference between a degree and going home,” she said. 

Nope. Please, please tell me I don’t need to go through all the reasons that’s absurd. Find me one single of case in which an AI detector alone sent a student home. One.

Two final bits.

The MPR story shares:

In the 2023-24 school year, the University of Minnesota found 188 students responsible of scholastic dishonesty because of AI use, reflecting about half of all confirmed cases of dishonesty on the Twin Cities campus. 

Just noteworthy. Also, it is interesting that 188 were “responsible.” Considering how rare it is to be caught, and for formal processes to be initiated and upheld, 188 feels like a real number. Again, good for U of M.

The MPR article wraps up that Yang:

found his life in disarray. He said he would lose access to datasets essential for his dissertation and other projects he was working on with his U of M account, and was forced to leave research responsibilities to others at short notice. He fears how this will impact his academic career

Stating the obvious, like the University of Minnesota, I could not bring myself to trust Yang’s data. And I do actually hope that being kicked out of a university for cheating would impact his academic career.

And finally:

“Probably I should think to do something, selling potatoes on the streets or something else,” he said. 

Dude has a PhD in economics from Utah State University. Selling potatoes on the streets. Come on.

(Editors note: This article first appeared at Derek Newton's The Cheat Sheet.)

Monday, January 6, 2025

HEI Resources 2025

[Editor's Note: Please let us know of any additions or corrections.]

Books

Alexander, Bryan (2020). Academia Next: The Futures of Higher Education. Johns Hopkins Press.
Alexander, Bryan (2023). Universities on Fire. Johns Hopkins Press.
Angulo, A. (2016). Diploma Mills: How For-profit Colleges Stiffed Students, Taxpayers, and the American Dream. Johns Hopkins University Press.
Archibald, R. and Feldman, D. (2017). The Road Ahead for America's Colleges & Universities. Oxford University Press.
Armstrong, E. and Hamilton, L. (2015). Paying for the Party: How College Maintains Inequality. Harvard University Press.
Arum, R. and Roksa, J. (2011). Academically Adrift: Limited Learning on College Campuses. University of Chicago Press.
Baldwin, Davarian (2021). In the Shadow of the Ivory Tower: How Universities Are Plundering Our Cities. Bold Type Books.
Bennett, W. and Wilezol, D. (2013). Is College Worth It?: A Former United States Secretary of Education and a Liberal Arts Graduate Expose the Broken Promise of Higher Education. Thomas Nelson.
Berg, I. (1970). "The Great Training Robbery: Education and Jobs." Praeger.
Berman, Elizabeth P. (2012). Creating the Market University. Princeton University Press.
Berry, J. (2005). Reclaiming the Ivory Tower: Organizing Adjuncts to Change Higher Education. Monthly Review Press.
Best, J. and Best, E. (2014) The Student Loan Mess: How Good Intentions Created a Trillion-Dollar Problem. Atkinson Family Foundation.

Bledstein, Burton J. (1976). The Culture of Professionalism: The Middle Class and the Development of Higher Education in America. Norton.

Bogue, E. Grady and Aper, Jeffrey. (2000). Exploring the Heritage of American Higher Education: The Evolution of Philosophy and Policy.
Bok, D. (2003). Universities in the Marketplace : The Commercialization of Higher Education. Princeton University Press.
Bousquet, M. (2008). How the University Works: Higher Education and the Low Wage Nation. NYU Press.
Brennan, J & Magness, P. (2019). Cracks in the Ivory Tower. Oxford University Press.
Brint, S., & Karabel, J. The Diverted Dream: Community colleges and the promise of educational opportunity in America, 1900–1985. Oxford University Press. (1989).
Cabrera, Nolan L. (2024) Whiteness in the Ivory Tower: Why Don't We Notice the White Students Sitting Together in the Quad? Teachers College Press.
Cabrera, Nolan L. (2018). White Guys on Campus: Racism, White Immunity, and the Myth of "Post-Racial" Higher Education. Rutgers University Press.
Caplan, B. (2018). The Case Against Education: Why the Education System Is a Waste of Time and Money. Princeton University Press.
Cappelli, P. (2015). Will College Pay Off?: A Guide to the Most Important Financial Decision You'll Ever Make. Public Affairs.
Carney, Cary Michael (1999). Native American Higher Education in the United States. Transaction.
Childress, H. (2019). The Adjunct Underclass: How America's Colleges Betrayed Their Faculty, Their Students, and Their Mission University of Chicago Press.
Cohen, Arthur M. (1998). The Shaping of American Higher Education: Emergence and Growth of the Contemporary System. San Francisco: Jossey-Bass.
Collins, Randall. (1979/2019) The Credential Society. Academic Press. Columbia University Press.
Cottom, T. (2016). Lower Ed: How For-profit Colleges Deepen Inequality in America
Domhoff, G. William (2021). Who Rules America? 8th Edition. Routledge.
Donoghue, F. (2008). The Last Professors: The Corporate University and the Fate of the Humanities.
Dorn, Charles. (2017) For the Common Good: A New History of Higher Education in America Cornell University Press.
Eaton, Charlie. (2022) Bankers in the Ivory Tower: The Troubling Rise of Financiers in US Higher Education. University of Chicago Press.
Eisenmann, Linda. (2006) Higher Education for Women in Postwar America, 1945–1965. Johns Hopkins U. Press.
Espenshade, T., Walton Radford, A.(2009). No Longer Separate, Not Yet Equal: Race and Class in Elite College Admission and Campus Life. Princeton University Press.
Faragher, John Mack and Howe, Florence, ed. (1988). Women and Higher Education in American History. Norton.
Farber, Jerry (1972). The University of Tomorrowland. Pocket Books.
Freeman, Richard B. (1976). The Overeducated American. Academic Press.
Gaston, P. (2014). Higher Education Accreditation. Stylus.
Ginsberg, B. (2013). The Fall of the Faculty: The Rise of the All Administrative University and Why It Matters
Gleason, Philip. Contending with Modernity: Catholic Higher Education in the Twentieth Century. Oxford U. Press, 1995.
Golden, D. (2006). The Price of Admission: How America's Ruling Class Buys its Way into Elite Colleges — and Who Gets Left Outside the Gates.
Goldrick-Rab, S. (2016). Paying the Price: College Costs, Financial Aid, and the Betrayal of the American Dream.
Graeber, David (2018) Bullshit Jobs: A Theory. Simon and Schuster.

Groeger, Cristina Viviana (2021). The Education Trap: Schools and the Remaking of Inequality in Boston. Harvard Press.

Hamilton, Laura T. and Kelly Nielson (2021) Broke: The Racial Consequences of Underfunding Public Universities
Johnson, B. et al. (2003). Steal This University: The Rise of the Corporate University and the Academic Labor Movement
Keats, John (1965) The Sheepskin Psychosis. Lippincott.
Kelchen, R. (2018). Higher Education Accountability. Johns Hopkins University Press.
Kezar, A., DePaola, T, and Scott, D. The Gig Academy: Mapping Labor in the Neoliberal University. Johns Hopkins Press.
Kinser, K. (2006). From Main Street to Wall Street: The Transformation of For-profit Higher Education
Kozol, Jonathan (2006). The Shame of the Nation: The Restoration of Apartheid Schooling in America. Crown.
Kozol, Jonathan (1992). Savage Inequalities: Children in America's Schools. Harper Perennial.
Labaree, David F. (2017). A Perfect Mess: The Unlikely Ascendancy of American Higher Education. Chicago: University of Chicago Press.
Labaree, David (1997) How to Succeed in School without Really Learning: The Credentials Race in American Education, Yale University Press.
Lafer, Gordon (2004). The Job Training Charade. Cornell University Press.
Loehen, James (1995). Lies My Teacher Told Me. The New Press.
Lohse, Andrew (2014). Confessions of an Ivy League Frat Boy: A Memoir. Thomas Dunne Books.
Lucas, C.J. American higher education: A history. (1994).
Lukianoff, Greg and Jonathan Haidt (2018). The Coddling of the American Mind: How Good Intentions and Bad Ideas Are Setting Up a Generation for Failure. Penguin Press.
Maire, Quentin (2021). Credential Market. Springer.
Mandery, Evan (2022) . Poison Ivy: How Elite Colleges Divide Us. New Press.
Marti, Eduardo (2016). America's Broken Promise: Bridging the Community College Achievement Gap. Excelsior College Press.
Mettler, Suzanne 'Degrees of Inequality: How the Politics of Higher Education Sabotaged the American Dream. Basic Books. (2014)
Newfeld, C. (2011). Unmaking the Public University.
Newfeld, C. (2016). The Great Mistake: How We Wrecked Public Universities and How We Can Fix Them.
Paulsen, M. and J.C. Smart (2001). The Finance of Higher Education: Theory, Research, Policy & Practice. Agathon Press.
Rosen, A.S. (2011). Change.edu. Kaplan Publishing.
Reynolds, G. (2012). The Higher Education Bubble. Encounter Books.
Roth, G. (2019) The Educated Underclass: Students and the Promise of Social Mobility. Pluto Press
Ruben, Julie. The Making of the Modern University: Intellectual Transformation and the Marginalization of Morality. University Of Chicago Press. (1996).
Rudolph, F. (1991) The American College and University: A History.
Rushdoony, R. (1972). The Messianic Character of American Education. The Craig Press.
Selingo, J. (2013). College Unbound: The Future of Higher Education and What It Means for Students.
Shelton, Jon (2023). The Education Myth: How Human Capital Trumped Social Democracy. Cornell University Press.
Simpson, Christopher (1999). Universities and Empire: Money and Politics in the Social Sciences During the Cold War. New Press.
Sinclair, U. (1923). The Goose-Step: A Study of American Education.
Stein, Sharon (2022). Unsettling the University: Confronting the Colonial Foundations of US Higher Education, Johns Hopkins Press.
Stevens, Mitchell L. (2009). Creating a Class: College Admissions and the Education of Elites. Harvard University Press.
Stodghill, R. (2015). Where Everybody Looks Like Me: At the Crossroads of America's Black Colleges and Culture.
Tamanaha, B. (2012). Failing Law Schools. The University of Chicago Press.
Tatum, Beverly (1997). Why Are All the Black Kids Sitting Together in the Cafeteria. Basic Books
Taylor, Barret J. and Brendan Cantwell (2019). Unequal Higher Education: Wealth, Status and Student Opportunity. Rutgers University Press.
Thelin, John R. (2019) A History of American Higher Education. Johns Hopkins U. Press.
Tolley, K. (2018). Professors in the Gig Economy: Unionizing Adjunct Faculty in America. Johns Hopkins University Press.
Twitchell, James B. (2005). Branded Nation: The Marketing of Megachurch, College Inc., and Museumworld. Simon and Schuster.
Vedder, R. (2004). Going Broke By Degree: Why College Costs Too Much.
Veysey Lawrence R. (1965).The emergence of the American university.
Washburn, J. (2006). University Inc.: The Corporate Corruption of Higher Education
Washington, Harriet A. (2008). Medical Apartheid: The Dark History of Medical Experimentation on Black Americans from Colonial Times to the Present. Anchor.
Whitman, David (2021). The Profits of Failure: For-Profit Colleges and the Closing of the Conservative Mind. Cypress House.
Wilder, C.D. (2013). Ebony and Ivy: Race, Slavery, and the Troubled History of America's Universities.
Winks, Robin (1996). Cloak and Gown:Scholars in the Secret War, 1939-1961. Yale University Press.
Woodson, Carter D. (1933). The Mis-Education of the Negro.
Zaloom, Caitlin (2019). Indebted: How Families Make College Work at Any Cost. Princeton University Press.
Zemsky, Robert, Susan Shaman, and Susan Campbell Baldridge (2020). The College Stress Test:Tracking Institutional Futures across a Crowded Market. Johns Hopkins University Press.

Activists, Coalitions, Innovators, and Alternative Voices

College Choice and Career Planning Tools

Innovation and Reform

Higher Education Policy

Data Sources

Trade publications

Monday, December 30, 2024

2025 Will Be Wild!

2025 promises to be a disruptive year in higher education and society, not just in DC but across the US. While some now can see two demographic downturns, worsening climate conditions, and a Department of Education in transition, there are other less predictable and lesser-known trends and developments that we hope to cover at the Higher Education Inquirer.

The Trump Economy

Folks are expecting a booming economy in 2025. Crypto and AI mania, along with tax cuts and deregulation, mean that corporate profits should be enormous. The Roaring 2020s will be historic for the US, just as the 1920s were, with little time and thought spent on long-range issues such as climate change and environmental destruction, economic inequality, or the potential for an economic crash.

A Pyramid, Two Cliffs, a Wall and a Door

HEI has been reporting about enrollment declines since 2016. Smaller numbers of younger people and large numbers of elderly Baby Boomers and their health and disability concerns spell trouble ahead for states who may not consider higher education a priority. We'll have to see how Republican promises for mass deportations turn out, but just the threats to do so could be chaotic. There will also be controversies over the Trump/Musk plan to increase the number of H1B visas.

The Shakeup at ED

With Linda McMahon at the helm of the Department of Education, we should expect more deregulation, more cuts, and less student loan debt relief. Mike Rounds has introduced a Senate Bill to close ED, but the Bill does not appear likely to pass. Diversity, Equity, and Inclusion (DEI) efforts may take a hit. However, online K12 education, robocolleges, and surviving online program managers could thrive in the short run.

Student Loan Debt

Student loan debt is expected to rise again in 2025. After a brief respite from 2020 to late 2024, and some receiving debt forgiveness, untold millions of borrowers will be expected to make payments that they may not be able to afford. How this problem affects an otherwise booming economy has not been receiving much media attention.

Policies Against Diversity, Equity, and Inclusion

This semester at highly selective institutions, Black first-year student enrollment dropped by 16.9 percent. At MIT, the percentage of Black students decreased from 15 percent to 5 percent. At Harvard Law School, the number of Black law students has been cut by more than half. Florida, Texas, Alabama, Iowa and Utah have banned diversity, equity and inclusion (DEI) offices at public universities. Idaho, Indiana and Kansas have prohibited colleges from requiring diversity statements in hiring and admissions. The resistance so far has been limited.

Failing Schools and Strategic Partnerships

People should expect more colleges to fail in the coming months and years, with the possibility that the number of closures could accelerate. Small religious schools are particularly vulnerable. Colleges may further privatize their operations to save money and make money in an increasingly competitive market.

Campus Protests and Mass Surveillance

Protests may be limited out of fear of persecution, even if there are a number of legitimate issues to protest, to include human induced climate change, genocide in Palestine, mass deportations, and the resurgence of white supremacy. Things could change if conditions are so extreme that a critical mass is willing to sacrifice. Other issues, such as the growing class war, could bubble up. But mass surveillance and stricter campus policies have been emplaced at elite and name brand schools to reduce the odds of conflict and disruption.

The Legitimization of Robocollege Credentials

Online higher education has become mainstream despite questions of its efficacy. Billions of dollars will be spent on ads for robocolleges. Religious robocolleges like Liberty University and Grand Canyon University should continue to grow and more traditional religious schools continue to shrink. University of Southern Hampshire, Purdue Global and Arizona Global will continue to enroll folks with limited federal oversight. Adult students at this point are still willing to take on debt, especially if it leads to job promotions where an advanced credential is needed.

Apollo Global Management is still working to unload the University of Phoenix. The sale of the school to the Idaho Board of Education or some other state organization remains in question.

AI and Cheating

AI will continue to affect society, promising to add more jobs and threatening to take others. One less visible way AI affects society is in academic cheating. As long as there have been grades and competition, students have cheated. But now it's become an industry. Even the concept of academic dishonesty has changed over the years. One could argue that cheating has been normalized, as Derek Newton of the Cheat Sheet has chronicled. Academic research can also be mass produced with AI.

Under the Radar

A number of schools, companies, and related organizations have flown under the radar, but that could change. This includes Maximus and other Student Loan Servicers, Guild Education, EducationDynamics, South University, Ambow Education, National American University, Perdoceo, Devry University, and Adtalem.

Related links:

Survival of the Fittest

The Coming Boom

The Roaring 2020s and America's Move to the Right

Austerity and Disruption

Dozens of Religious Schools Under Department of Education Heightened Cash Monitoring

Shall we all pretend we didn't see it coming, again?: higher education, climate change, climate refugees, and climate denial by elites

The US Working-Class Depression: "Let's all pretend we couldn't see it coming."

Tracking Higher Ed’s Dismantling of DEI (Erin Gretzinger, Maggie Hicks, Christa Dutton, and Jasper Smith, Chronicle of Higher Education).

Sunday, December 22, 2024

How a new Arizona school will use AI to teach students in 2-hour models (ABC 15 Arizona)

Thursday, December 19, 2024

AI and the Mass Production of Academic Research (Ethan Mollick)

Two researchers used AI to generate 288 complete academic finance papers predicting stock returns, complete with plausible theoretical frameworks and citations. Each paper looks legitimate and follows academic conventions. They did this to show how easy it now is to mass produce seemingly credible research. A warning about industrialized academic paper generation becoming reality. The future arrived faster than we expected, and academia is not ready.

Friday, November 1, 2024

Student Newspaper Promotes Cheating Services for Cash (Derek Newton)

The Daily, the student newspaper at Ball State University in Indiana, ran an article recently with this headline:

Best Way to Remove AI Plagiarism from Text: Bypass AI Detectors

So, that’s pretty bad. There’s no real justification that I can imagine for advising students on how not to get caught committing academic fraud. But here we are.

The article is absent a byline, of course. And it comes with the standard disclaimer that papers and publishers probably believe absolves them of responsibility:

This post is provided by a third party who may receive compensation from the products or services they mention.

Translated, this means that some company, probably a soulless, astroturf digital content and placement agent, was paid by a cheating provider to place their dubious content and improve their SEO results. The agent, in turn, pays the newspaper for the “post” to appear on their pages, under their masthead. The paper, in turn, gets to play the ridiculous and tiring game of — “that’s not us.”

We covered similar antics before, in Issue 204.

Did not mean to rhyme. Though, I do it all the time.

Anyway, seeing cheating services in a student newspaper feels new, and doubly problematic — not only increasing the digital credibility of companies that sell deception and misconduct, but perhaps actually reaching target customers. It’s not ideal.

I did e-mail The Daily to ask about the article/advertisement and where they thought their duties sat related to integrity and fraud. They have not replied, and the article is still up.

That article is what you may expect. It starts:

Is your text AI-generated? If so, it may need to be revised to remove AI plagiarism.

You may need to remove the plagiarism — not actually do the work, by the way — because, they say, submitting “AI-plagiarized content” in assignments and research papers is, and I quote, “not advisable.”

Do tell, how do you use AI to generate text and remove the plagiarism? The Ball State paper is happy to share. Always check your paper through an AI-detector, they advise. Then, “it should be converted to human-like content.”

The article continues:

Dozens of AI humanizing tools are available to bypass AI detectors and produce 100% human-like content.

And, being super helpful, the article lists and links to several of them. But first, in what I can just barely describe as English, the article includes:

If the text is generated or paraphrased with AI models are most likely that AI plagiarised.
If you write the content using custom LLMs with advanced prompts are less liked AI-generated.
When you copied word-to-word content from other AI writers.
Trying to humanize AI content with cheap Humanizer tools leads to AI plagiarism.

Ah, what’s that again?

Following that, the piece offers step-by-step advice to remove AI content, directing readers to AI detectors, then pasting the flagged content into a different software and:

Click the “Humanize” button.

The suggested software, the article says:

produces human content for you.

First, way creepy. Second, there is zero chance that’s not academic cheating. Covering your tracks is not clever, it’s an admission of intent to deceive.

And, the article goes on:

If you successfully removed AI-generated content with [company redacted], you can use it.

Go ahead, use it. But let’s also reflect on the obvious — using AI content to replace AI content is no way removing AI content.

Surprising absolutely no one, the article also suggests using QuillBot, which is owned by cheating titan Course Hero (now Learneo), and backed by several education investors (see Issue 80).

Continuing:

Quillbot can accurately rewrite any AI-generated content into human-like content

Yes, the company that education investors have backed is being marketed as a way to sneak AI-created academic work past AI detection systems. It’s being marketed that way because that is exactly what it does. These investors, so far as I can tell, seem not the least bit bothered by the fact that one of their companies is polluting and destroying the teaching and learning value proposition they claim to support.

As long as the checks keep coming - amiright?

After listing other step-by-step ways to get around AI detectors, the article says:

If you use a good service, you can definitely transform AI-generated content into human-like content.

By that, they mean not getting caught cheating.

None of this should really surprise anyone. Where there’s a dollar to be made by peddling unethical shortcuts, someone will do it because people will pay.

Before moving on, let me point out once again the paradox — if AI detectors do not work, as some people mistakenly claim, why are companies paying for articles such as this one to sell services to bypass them? If AI detection was useless, there would be no market at all for these fingerprint erasure services.

This article first appeared at Derek Newton's The Cheat Sheet.

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