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How should a faculty deal with the problem of Artificial Intelligence (AI) generated texts?
For many years one of the main problems for academic essays and BA/MA/PhD theses was plagiarism. However with the recent advent of artificial intelligence (AI) that can write high quality texts (e.g. ChatGPT) a fresh concern has risen: was a text written by a student or by an AI?
Plagiarism scanners do not detect AI texts (I tried it!) because they are not copied and pasted. Even if you have a certain suspicion that a text might be written by an AI (e.g. because an overall direction in the text is missing or the text is just too good to be true given the student's previous essays) there is no real way to prove the suspicion.
How to deal with this new problem? Yesterday we had a faculty meeting where we discussed this issue of AI texts but no real conclusion - so I was thinking of getting some input from here ...

Comments are not for extended discussion; this conversation has been moved to chat.

TurnItIn seems to have made some advances in this area: https://universitybusiness.com/plagiarism-catcher-turnitin-announces-state-of-the-art-ai-writing-detector/?oly_enc_id=2804I9449778B7W.

One general approach is to make sure the assignment is not bullshitable.
ChatGPT generates bullshit, i.e. text that attempts to resemble genuine responses to the prompt, but does not attempt to actually be correct. The AI does not generally understand what it means to be correct, it just produces relevant text (relevant text is often correct, but often incorrect).
Unfortunately, many students use a similar process, so it can be hard to distinguish. So my suggestion is to design prompts where bullshit results in a bad grade (and is easy for you to detect), even if an automated tool cannot detect cheating.

ChatGPT uses nonexistent, made-up references. Ask students to submit references in a format that can be easily spot-checked.

Ask students to provide drafts of their work, incomplete versions showing their thought process, and maybe errors they made on the way.

Avoid open-ended prompts like "write X words about Y".

Prefer prompts with a global goal and require each local part of the essay to contribute to that goal. Take off points in the rubric for parts of the essay that do not clearly support the global goal. I.e. take off points for rambling or unnecessary material. Take points off for good material if it is not shown how that material contributes to the global goal.

Do not prompt for content for content's sake. Instead of a minimum word count, give a maximum word limit and require students to accomplish a definable goal within that limited space.

Caveat: I haven't tried this, my background is more in AI and less in essay assignments. Also, this answer is focused on essays because this kind of cheating on a longer text, like a thesis, should be much easier to detect.
Example reference with similar opinion: ChatGPT Is Dumber Than You Think (The Atlantic)

Good answer.  Asking for personal thoughts, connections directly to class content (as in specifying the class day) and other things are other ways to promote a lack of dung output (or at least to more easily be able to justify rejecting it wholesale).

To rephrase in a less flattering way: if a ChatGPT generated answer would result in a good mark for your homework assignment, the problem is with the tasks assigned for homework, not with the student that used ChatGPT.

"Do not prompt for content for content's sake." There was a reason why a lot of courses exams were nicknamed 'Bullshit 101' by most of my colleague. And for the same reason ,those courses received a lot of feedback.

I used to tell students that if 100% of the content was correct, but half of it was irrelevant, then that's a 50 % mark. At that point, I probably won't care if it's AI generated or just a student being sloppy, as it's approaching a fail anyway (because it's rarely 100% correct).

I was trying to explain to a colleague why I was actually quite excited about ChatGPT's emergence, and this articulates it very well - a forced push towards productive, shorter essays, which I hope also sets people up to write more clearly in their future papers. Fewer bullshit exams or assignments, simply because it's trivial to generate answers for them. I think we are going to need to teach writing to a higher standard, but, honestly? I think that's a great thing for research.

I had some interaction with ChatGPT, and it confidently stated that "10x10 - 2=62"...

@lupe I agree. This may force us to force the students to actually write good essays in the future.

The problem with requesting drafts is that not all of us write (or keep) drafts.

AI's don't actually understand what Go or chess is, yet they utterly destroy humans at it.  It creates advanced strategies people never dreamed of.   Yes, right now there are still many issues but it is rapidly evolving.  Right now it can easily outperform your average middle school student at most text based tasks.  It's only a matter of years before you start seeing fully AI generated pieces being published in notable journals. Lots of people are in the same denial you saw chess players in a couple decades ago -- it can never beat humans, it doesn't understand!! Oh wait.

And whether it is "bullshit" or not is completely irrelevant. All that matters is if a person can reliably distinguish between AI text and human text.  We are already to the point that for many things, your average person cannot.

@eps the important difference is that game-playing has a simple, measurable goal and AIs can be automatically trained to achieve it objectively. We can't automatically measure objective correctness for large language model output, and they aren't trained with a goal of being correct.

@eps "All that matters..." This comment changes if you get to choose which queries you ask, and pick ones where it's easier to distinguish.

Non bullshitable assignments might actually end up wrecking a bunch of humans. A lot of people get through most of their essay classes writing bullshit to a degree (I wish there was a non vulgar term for this but I know what you mean). ChatGPT and successively better AI writers are effectively already displacing this class of student

This answer is correct, but to declare, as many commenters do, that all bullshitable assignments are bullshit themselves is easy, but not true. By the same line of reasoning, most math assignments would be BS, since computers can perform them. Cheater-proofness is is not the only important measure of the quality or usefulness of an assignment.

@DanielHatton But ChatGPT is in the same league (or above) as CAS'es, isn't it?

@SidharthGhoshal I consider that a feature, not a bug... The fact that some students survived only writing BS was already a problem in the education system, and this is just forcing us to face the problem and solve it.

@quarague: This might be true for ChatGPT, but probably not for future AIs..

I would really not like showing drafts. As a student I pored long over a problem before writing an almost ready presentation. In the cases I failed, I failed utterly, and the story was completely irrelevant - I had to write everything from scratch. I do not have the long uninterrupted time periods any more that would permit me to go by this approach, but this style typically resulted in very coherent, compelling stories, which now takes me multiple runs to fine-tune towards; I still would prefer my old style if I could. So, the drafts idea is not really for everyone.

You ask the writer about the text
Frame change. You are not worried about computers writing papers. You are worried about people falsely claiming to be writing papers.
If a human writes a paper for another human you have the same problem. And the same solution: ask the writer or interview them about the text.
A human that generates parts or entire papers automatically will have a very hard time explaining said parts.

This is a great answer. Do we need new rules to account for AIs doing things? I don't see why. The problem of AIs is no different than the problem of humans. How do you know a student didn't subcontract their paper from someone in Bangladesh? How do you know they wrote it themselves? I don't know the answer to that, but it's an old question, and the AI problem is just the same old question.

To clarify: Are you suggesting that the instructor interview every student in the class, or just the suspicious ones?

Remark (also for the commenters): note that there are countries where exams with an oral part have always been the norm. Yes, for everyone.

@JamieB Yes it's "the same old question" but free online AI means it may occur much more often.

@EthanBolker yep, the problem with this answer is that it doesn't scale.  Sure you can do this for a small classroom with an occasional writing assignment.  But no one is going to routinely do this for bigger classes with routine writing assignments.

@JamieB this completely ignores scale, there is a GIGANTIC amount of difference in perceived morality, money, time, etc etc etc in firing up chatgpt and outsourcing a writing assignment.  It's like comparing the ENIAC with a smartphone. yes, they are both computers, but at some point the differences are so big it becomes a different thing entirely.  People could always cheat but this changes the game in big ways.  It also begs the question of how this stuff should work -- if you can use chatgpt to generate 90% of the paper and all you have to do is a little cleanup .... shouldn't you do that?

I'm with @eps here. We compile content using the computer and consider it completely OK to let computers help us with orthography and even grammar when writing. So using ChatGPT is using an unauthorized tool, comparable to using a calculator in a test where you're not supposed to — which is in a completely different league from hiring someone to do it for you.

No. I'm suggesting that faculty members read the texts they ask the students to write. Period. If they do not read what a student writes, what is the point anyway?

There is a similar, age-old problem of group work (one smart student does all the work for the entire group). My teacher found a solution: one student had to present the 3-person group's work in the classroom, and the entire group received the same grade based on the presenter's asnwers. Caveat: the presenter was chosen randomly by the teacher. So the entire group, not only the smart student, had to be prepared.

An interesting idea on this topic comes from Ben Thompson at Stratechery: instead of banning the use of AIs, require it - the students' job is not to produce an essay, instead they have to check and correct what the AI says about the topic.
A quote from his article:

Imagine that a school acquires an AI software suite that students are expected to use for their answers about Hobbes or anything else; every answer that is generated is recorded so that teachers can instantly ascertain that students didn’t use a different system. Moreover, instead of futilely demanding that students write essays themselves, teachers insist on AI. Here’s the thing, though: the system will frequently give the wrong answers (and not just on accident — wrong answers will be often pushed out on purpose); the real skill in the homework assignment will be in verifying the answers the system churns out — learning how to be a verifier and an editor, instead of a regurgitator.

Ben argues that the skills required to check and correct the unreliable AI's output are more relevant in the modern world than the skills required to write an essay in the first place. Perhaps as a bonus, the students will also put 2 and 2 together to realise that if they have an essay to submit to a different teacher, they can't expect ChatGPT to write a decent essay for them.

An extension this would be a question along the lines of "I asked an AI to write two different answers to the question '.....' discuss these answers,  the ayers and weakness of each,  and which is better"

This is the only correct, non-Luddite answer. Fighting the future is futile, instead we should embrace it.

@JonathanReez "This is the only correct [...] answer" - sorry, that's patently not true. The ability of doing quick estimates in the mind rather than relying on the pocket calculator comes in useful everytime when you have to check whether something is in the right ballpark. And I am not talking just science. Knowing what an increase/reduction of 2% of your salary means is a very useful skill to have. Thus, knowing how to do approximate arithmetics without a calculator is a critical skill even if everyone carries a powerful computer with them at all times. So is writing.

@CaptainEmacs The goal of the university is to produce members of society who can produce useful intellectual work. How they produce said work should be of zero relevance.

@JonathanReez I don't think there's any disagreement about your first sentence, but the "how" is relevant insofar as there are methods and practices which are likely to produce better intellectual work and one of the roles of higher education is to instil those practices. What I think you and Captain Emacs disagree about is the scope of those practices. Personally I would tend to agree with you that some of the skills we're currently expecting students to learn are going to be unnecessary by the time they would be relevant. But I also think it's better to learn something that might not be ...

... needed than to miss out on something that turns out to be important because you didn't see the point of it at the time. For all the people who were proverbially told by their maths teachers that they wouldn't always have a calculator available to do arithmetic, I think the teachers are vindicated by the fact that those people don't walk around the supermarket with the calculator app on their phone open all of the time when it would be beneficial for them to be doing some arithmetic. If schools had stopped teaching arithmetic because calculators made it obsolete, we'd be poorer for it.

@JonathanReez 'How' they produce this work is of relevance if you talk about academia. It is not so much of relevance in vocational institutions. The whole point about understanding the 'How' is the robustness of the knowledge. I can assure you that, even in non-intellectual craftsmen/women, those who understand that the different levels of 'how' are by far superior to those who have only one route of solving the problem and only nebulously understand how they achieve what they achieve. In academia, with the complexity of the material, this becomes even more important.

@CaptainEmacs sure and those who can produce a better final product will still get the publications/citations/credit/money/fame. But if you understand the 'how' yet produce an inferior product to those who don't... no one cares. Make great papers, get published. Make bad papers and you won't. The how is irrelevant.

@JonathanReez I may agree with you on end results produced by professionals. But, as long as you teach (and learn), the how is everything :-)

I want to raise three points to guide the discussion, but they're too long for a comment, so an answer it is:

We should assume NLP models will continue becoming 1. better and 2. widespread. Consequence of (1): it will eventually become impossible for the well-trained human eye to sense that an essay was written by a machine. Consequence of (2): the solution of a "textual fingerprint" for identifying an auto-generated text, as mentioned above, won't be feasible. Currently, this could still work, because there is only one company offering one model for generating ChatGPT-quality text, namely OpenAI itself. Fast-forward 2 years, and there will be many such companies, perhaps even explicitly aimed towards high-schoolers. In that case, a teacher would have to go down the list verifying the fingerprint with each service, and such a service would be foolish (in their own market interest) to offer such a service in the first place.
We should view this in the broader and existing context of ghost-writing. Some take solace in being able to discern the "bullshittery level" of ChatGPT, but this is tackling the wrong problem. For decades, students have paid others to write assignments for them, and obviously, the task of detecting those texts has nothing to do with detecting that the resulting text was human-generated, because it was -- just not by the right human.
Some have claimed that something like a thesis cannot be generated by a machine, because they "cannot be creative". We should refrain from these kinds of statements, because 1. even search algorithms can be creative, and 2. there are, sadly, many disciplines in academia where innovation hardly exists and bullshittery is the entire game. Ask ChatGPT to write an opinion piece, and it'll give you an essay at high-schooler level. Then ask it to do it in an academic style with complicated words, and you've entered the domain of master's theses (or higher!) in some domains.

Perhaps the take-home opinion essay is just dead, and the future consists of technical reports and intensively researched term papers. Perhaps a fingerprinting system will be the future, but it will be the students whose capacities will be fingerprinted and tracked through time, not the texts.

great answer, it really amazes me how many people think this tech is going to always be controlled by well meaning researchers.  and how many people that don't realize how quickly this tech is evolving.

While the idea of ChatGPT writing entire MA/PhD theses is certainly entertaining, right now the technology just isn't at that level. It can write multiple paragraphs at a time, and maybe a human can manually piece those together into a proper "paper", but it's limited in what the human can feed in. It's really a glorified chat bot, and it's not designed to write essays for students. For example, there's no supported way you can feed in an article/story and have ChatGPT respond to it. Moreover, there are simply limits to ChatGPT's semantic understanding.
In the future I suppose some companies might make a 'nefarious' version of ChatGPT and monetize it, considering all the various companies we already have that monetize academic dishonesty. When the technology is more at that level, hopefully there will be new tools on the detection side that can deal with the issue.

I can't comfortably let ChatGPT do something for me.

This is a great answer. The biased samples introduced to the public are just that. "AI" is a bubble. Wheres my self driving car? Lets see AI beat Zelda 1. Lets see it. Anyone who works hands on at a low level fundamentally understands the algorithm only does what its told, diametrically opposite the process of scientific discovery.

Perhaps the answerer doesn't know how bad, in fact, is the writing of some college undergraduates.

I think the point about things not being in the training set is a red herring. The training set probably doesn't include any movie scripts where the Teletubbies fight Superman, but it can generate those just fine. You might not be able to prompt it with a whole article, but you can take the key points from that article and form a prompt that gets you some response, then prompt it further to get it to change its response. (I first got a script where Superman is the good guy, then I asked it to make Superman the bad guy, then I asked it to also make the Teletubbies win.)

Besides, most essay-style assessments are not asking the students to produce any new knowledge - for PhD theses of course, but for coursework at undergraduate level the "training set" very likely includes previously-written essays about the same topic. The main reason that we don't need to worry just yet, I think, is that ChatGPT tends to produce a lot of rubbish that only resembles good academic work in superficial form, not in substance.

And me thinking that an AI writing good academic texts was a good thing (i.e. not nefarious). It seems that Luddism is starting to catch up with intellectual workers.

@user156207 that isn't actually how modern deep learning algos work.  if it simply was just doing what it was told by the programmer, the person who designed the chess algo would be the best chess player in the world.  indeed, it's actually quite hard to figure out why deep learning algos make the choices they do even when you have access to all the nuts and bolts.  And it would be trivial to have AI beat zelda 1, they have already tackled much more complicated games.

@lvella and it's nothing new, chess players were in extreme denial for a long time and then in a couple years time we went from humans reliably beating chess computers to it becoming completely pointless to even try.  Same thing happening now in the art world too.  it's funny -- almost all sci fi portrayed AI as taking over blue collar tasks but still having a lot of issues keeping up with people intellectually.  In reality AI is going to outpace humans in intellectual tasks far before it replaces your plumber.

@lvella The issue is not that academics don't want AIs to be able to do academic writing - many of the AI researchers making this possible are academics. The issue is that it makes it harder for us to correctly assess students' abilities, which is a thing that educators will need to be able to do for as long as there are still universities awarding qualifications.

Sort of an extension to André LFS Bacci's answer: consider an oral exam. It's not going to be ideal since oral exams have their own set of problems, but ChatGPT can't fake an oral exam, and neither can Chegg.
You could also use an oral exam as a check to see if the student wrote the work. It won't prove an AI wrote the essay, but it could indicate the student did not write it.

Difficult to do oral exams for 600 students in each of the 10 modules they take in a year. That's 6000 peak exams per year group, per year.

One might want to return to writing in-person essays.  This would only apply in situations where the length and time involved made that appropriate, but for certain assignments (not theses!) one could imagine either taking class time for this, or proctoring in some fashion.  Particularly as (current!) AI seems to "excel" with fairly short-form material, giving people 20 minutes to write such a short assignment in person may be a viable way to deal with it.
(That said, I like usul's answer better, and have used it for many years for certain longer assignments.)

Whatever happens, it needs to happen in class. Like maybe teaching them instead of them learning on their own by doing homework.

The International Baccalaureate (IB) is an international high school school curriculum used by many international high schools. Although it is for high school, one part of it can provide insight to the OP: the "Extended Essay" (EE), which is basically a 4,000 word essay.
For every student, the EE research and writing process includes three formal 20-30 minute "reflection sessions", which are interviews between the student and teacher, the purposes of which are to support the student's learning and progress, and to check for authenticity, i.e. to check if the essay was actually written by the student.

In the first reflection session, they discuss the student's proposed research question.
In the second reflection session, they discuss the student's first draft.
In the third reflection session, they discuss the student's second (and final) version.

This system has been used by the IB for a long time, and it seems to work, as far as checking for authenticity. Perhaps a modified system could be considered for university.

Yes, indeed, but quite labor-intensive. I think the questioner wants to know how do do this more "automatically", ironically to filter out "automatic" writing. :)

@paulgarrett I did not get the impression that the OP is only interested in quick fixes.

@paulgarrett ... "quite labor-intensive".   But, in the future, we can get computers to do those interviews of the students. :)

The question points to an underlying development that was identified by Ray Kurzweil: The continuing application of the No True Scotsman argument to intelligence. What we consider "truly" intelligent has changed with the advancing capabilities of machines, on the grounds that a task that can be mechanized is by definition not a sign of "true" intelligence. Far into the 1900s, playing world class chess or being able to translate reasonably well between a dozen languages would have been considered a sign of the highest intelligence. So would have been the ability to write reasoned essays in college grade English about almost any topic known to mankind (which is what ChatGPT does, of course).
The progress made in information technology shows us that all these tasks can be done by mechanisms. Nobody in their right mind, apart from the occasional excited google engineer, would claim that these mechanisms are "truly" intelligent. Because we don't think of ourselves as mechanisms, we backtrack and change our classification of what we consider "truly" intelligent. Everything that is rule-based is obviously not truly intelligent: You can beat it with stupid brute force. Everything that is simply based on pattern recognition is not truly intelligent: There is no true "understanding" and no "originality". But alas: The texts are good enough to earn certificates and pass German college term tests. And this is just a prototype.
There are a couple conclusions here. We can either backtrack further and say:

Much, if not most of what we do in academia is not "truly" intelligent. The amount of original, creative, essentially unpredictable work is small.
Much of what we do professionally (and what college education prepares us for) is not truly intelligent work. Programmers, radiologists, lawyers: Just pattern recognition and -application.
Our education and our professions are on the brink of being obsolete.

This is scary.
Or we hold our ground and continue to consider our education and professions at least somewhat intelligent. Then we cannot deny that we have produced intelligent machines. The google engineer was right. Rather sooner than later machines will be able to do any intellectual task we can do, including the ones one might currently consider original, creative, and essentially unpredictable. They will probably be able to perform them better than we do. In fact, they will probably become able to perform intellectual tasks that are entirely beyond our regular reach.1
This is even more scary.
My guess is that we will take a much bigger step back than ever before: We will redefine what it means not to be intelligent but to be human. We will be forced to  realize that intelligence is not what defines us as human. It is probably not even art that defines us, or only insofar as art defines us as individuals.2 Instead, it is emotions: Love, compassion, passion, even hate. Machines are unable to feel and will be unable to feel for the foreseeable future.
P.S. Are you still waiting for an answer to your question?

Using ChatGPT is not any more plagiarism than using a pocket calculator. If it gets you results, it's a useful tool.
Therefore, don't be a Don Quixote. Instead of a futile attempt at preventing the use of ChatGPT e.a., embrace it. Kaya3 wrote an answer in this direction. The future of academia and humanity lies not in defending the indefensible but in employing the useful.
Change the curriculum to stay relevant. Nobody teaches how to manually draw a root in algebra any longer, or matrix tricks. Try to teach things which may be hard for AI even in 20 years.

1 I'm not necessarily hinting at the prospect of a technological singularity which often involves an unpleasant quasi-religious sentiment; a much weaker development would suffice: Machines continue to improve on cognitive tasks (like recognizing cancer cells, designing mechanical things, predicting the weather, making investment decisions, driving a car). We increasingly find that they do it better than we typically do, and we increasingly rely on them. This is a gradual development without tipping points of any kind. (It is funny that the proponents of a singularity recognize that technological development is exponential but fail to see that exponential curves are emphatically void of singularities; quite to the contrary: They look the same everywhere. The discovery of fire, advent of agriculture or the industrial revolution have disrupted societies much more than a mechanical lawyer or programmer ever could.)
2 As today, different individuals would produce different art. This would include mechanical individuals, i.e. different neural nets, or differently trained neural nets (the equivalent of separately raised identical twins). Like today, experts (including, of course, mechanical experts) would be able to make an educated guess which individual (including, of course, mechanical individuals) created a given piece of art, or at least which tribe and era it is from (e.g. 17th century Flemish, 19th century Xhosa, or 202x Dall E3 lineage).

At the moment, and as a minimum, you need a clear policy on its use, perhaps forbidding its use. That isn't enough, of course, but you need to make it clear. That will ameliorate the problem slightly, as most students will comply if the policy is stated in a reasonable way that emphasizes the learning goals. The reasoning behind a policy needs to be made as clear as the policy itself (as usual).
Mid term, it is likely that AI solutions will emerge that can catch the use of such things with fair accuracy, maybe even good accuracy. Some are already underway. The might even provide a good balance between false positives and false negatives. The former might be handled if students were always subject to a follow up oral presentation of essays and such as is done with theses.
I think that the development of a detection tool is a worthwhile AI research project at the moment.
Long term, it is harder. I don't think (but am not certain) that ChatGpt in particular tries to obfuscate its use, but that can happen.  At some point, we may just need to completely change the techniques we use to encourage and evaluate student honest work. It is worth spending some time with that now, and trying out ideas. Oral exams don't scale well, but are harder to misuse.
At the moment the AI text generation isn't very creative and a careful reading might catch a lot of it - especially for longer texts. But AI generated work and poor but honest work might be harder to distinguish.
As I understand it, ChatGpT doesn't have access to the internet (say, Wikipedia). That would change the game, perhaps, but might also make plagiarism detection easier.

I'll note that forbidding its use might not be the only proper policy. Use with citation might be considered as you develop such a policy.

I had downvoted this as theoretical. The line drawn for using writing aides is arbitrary. How is it any better if a student gets writing help from a human? The human is better at it and provides more contribution to a written article. The technology for material contribution does not exist. Enforcement of any policy is practically impossible. The entire discussion about regulation is a giant waste of time. Its planning for something that cannot happen in reality.

@user156207, so you think that having no policy whatever is better? No guidance whatever? Or do you think that policy is only to guide punishment, not behavior?

im saying a couple disparate things. 1. Youre planning for something that isnt going to happen. 2. By planning for AI to possibly take over essay writing is tacit admission that you dont grasp ai potential 3. Whatever academic policy that is in place almost certainly is adequate. 4. If any of the above is false, its unenforceable anyway, rendering this entire conversation moot. Theres an assumption there is a problem, for which you provide a sane, reasonable solution. Im suggesting you question the assumption. There is no problem, nor will there be in our lifetimes. So why plan?

Lets put it a different way. Academic essay integrity is about thoughts and ideas. Machines do not have thoughts or ideas-- and you have no reason that is going to change. If the purpose of the essay is grammar or syntax, then sure a machine can help the human cheat, but the machine is not breaking new ground in scientific discovery or thought generation then gifting idea-ownership to the human.

@user156207, note that they are already banned here with no really effective enforcement mechanism. And see https://arxiv.org/abs/2011.01314, helpfully provided by user/mod cag51. Look at academia meta for more.

@user156207 you made your opinion quite clear enough with one of those comments, no need to repeat it. Anyway, a) lots of people, including experts of the domain, strongly disagree that there will be no problem “in our lifetimes” b) even if there was only a low probability of this happening, it would be haphazard to not prepare for it anyway c) already today ChatGPT is a problem – not in that it actually generates serious scientific content yet, but in that it easily generates such a quantity of convincing-looking bogus that it risks overloading the human gatekeepers.

@leftaroundabout just following up buffys questions. Im not saying im right. Im saying i disagree. I feel those "experts" are selling you something. I myself have expertise in prediction. I myself have seen a lot of promises that arent being kept. Alas. What are your preparations for my cat doing my homework in trigonometry?

" it is likely that AI solutions will emerge that can catch the use of such things with fair accuracy, maybe even good accuracy." I am less confident of that if the ChatGPT etc can leverage Generative Adversarial Learning, where the system learns to produce reaslistic images etc. by learning to fool a classifier that distinguishes real objects from fakes.  I think automatic paraphrasing language models are more of a problem for dissertations than ChatGPT, I think that is more of a problem for teaching undergraduate programming.  But +1, stated policy should be the first step,

Before deciding to prohibit the use of ChatGPT altoghether as partially suggested in this answer, I would advise considering its more legitimate uses as well. For example, I personally found ChatGPT quite helpful for giving me suggestions on how to improve my writing during the editing stage. I believe this use of ChatGPT is no more problematic than the use of other writing assistant tools like Grammarly and allows the student to get rapid feedback on their writing (of course, they would still need to think about it critically, as some suggestions from OpenGPT will likely be nonsensical).

@Magma, actually I recommend a policy concerning its use, not one forbidding its use necessarily. But some uses probably need forbidding, as plagiarism, though a bit weird here, is still a problem.

@Magma there are other language model based AIs that can help with wording etc. without the issue of it providing content, which might be less problematic in that context.

the problem with the idea of a detection tool is that human language is not static -- if AI writing becomes commonplace human speech will begin to resemble it (and visa versa).  Actually, that has already started  happening due to things like grammerly.  You will never be able to reliably detect advanced AI text if both AI and human generated text converge together.  In many ways this reminds me of how teachers in the 80's / 90's desperately tried to prevent calculator use for math homework.  "but what if your calculator breaks!" eventually people saw the silliness and just accepted reality,

@eps, yes, as I said, the long term problem is harder than the mid term one.

In a few years, when current students enter the workforce, it seems likely they will be using AI-based language tools like they are using Google today. They will be using these tools to access existing knowledge, to polish the presentation of their results, to translate text that they have written in their native tongue into the language they are supposed to work in, or maybe even as research assistants that contribute genuinely new results. Their tools will likely be vastly superior to what is available now. I would argue we should prepare our students for that future.
With that in mind, I think a scalable and relatively future-proof way of dealing with AI assistance is to supply an AI-generated response to a homework assignment as part of the assignment. The instructor would set an essay assignment as usual, but in an additional step they would also use the best model they can get their hands on to auto-generate a response, maybe in an iterative fashion where the model first generates an essay outline and then fills in the chapters. They would then add the process they used to generate the essay to the methods section of this example essay. The students will then be graded on whether and by how much they managed to improve on this baseline. A brief critique and grading of the baseline could maybe be included by the teacher as well to show the students in what ways the AI-generated text is still failing.
In this way, the topic of AI assistance can be openly discussed in class; students get naturally exposed and can discuss different ways to use AI; they can discuss whatever limitations current AI still has; they learn about attributing credit; and they have to learn and ultimately demonstrate how to do better than the output of a current state of the art public model. Compared to schemes that rely on oral examinations, additional teacher workload should also be relatively low, as the baseline essay they will be producing will be produced in a highly automated way.

One IT website has called ChatGPT “Dunning-Kruger as a service”. It creates very convincing bullshit.
It’s very annoying and you might need to train people up to detect it (the convincing nonsense, not that it was created by an AI), but for a while at least you can judge these submissions just by their quality.

You could also call it, "Ask Wikipedia". I very strongly doubt that the content of the bot's output on any topic will vary from what Wikipedia already has to say.

Just one small aspect: For student papers require the bibliography to also include YOUR library's call numbers for books and the addition of the link used for online access of journal articles or the call number if accessed print. This is also good for ghostwritten work, as it makes the ghostwriter have to use your library, upping the cost. ChatGPT will just write bullshit, or forget to add this. I know this is not standard for publications, but for training writers you get them to show the library work they (supposedly) did.

Perhaps a direct inquiry to the student, such as "did you write this yourself" would provide the desired response.

If all students were honest, no exams would be needed...

Going along with EvilSnack's answer, perhaps this should be seen as an opportunity rather than a threat. There are some reflections here about how schools used to view Wikipedia and similar sites. While there is certainly backlash against the increasing use of AI, unscrupulous people are already seeking to advance its use past the point where we can tell it is used. News articles, social media posts, forum messages, and even telephone calls are already hosted by AI bots.
Maybe have students generate an article or essay, and run them through the process of proofreading, fact-checking, and editing. Changes should be tracked along the way (a feature that's part of any decent word processor these days), and the students should be prepared to defend their actions and choices. This could also be a collaborative assignment.
Along the way, the kids may notice peculiarities and biases that we might not, and thus become better critics of AI-generated copy than we could hope to. This should also help inform them on the topics of misinformation and deception.
Even if AI is not widely adopted, academia should always approach these sort of matters thoroughly, rather than immediately regarding them as a challenge to tradition.

(I'm adding another answer because it's fundamentally different from the other one I wrote)
There are tools available now to detect AI-written text. One way is to get another AI to classify the text; the other way is to add a watermark to the AI-generated text. Watermarking is more sophisticated than physical watermarking; it modifies the output text (by the original text-generating AI) in a way that is detectable by computers, but not by humans.
See sources for classifiers and watermarking respectively.

Ask the AI questions about the subject of the assignment. If you ask enough questions, you will gain a good idea of the AI's limitations, and also a good idea of the AI's writing style.
Then in the assignment, require the student to answer a question that the AI was unable to answer.
The answers you receive from the students will show the AI's limitations and style if they are using the AI, otherwise they will at the very least be the work of a live human being.

Example of a usable essay prompt that IA cannot legitimately respond to?

That would depend on the topic and the level of knowledge you want to see.

Topic: Free will (from Philosophy). Level of knowledge: basic understanding of the standard arguments for and against free will. But I don't know what your area of expertise is, so it may be better for you to choose your own example. I think an example would strengthen your answer.

this answer assumes humans won't eventually learn to mimic AI generated text (which is already happening because of text choice assistance apps). it also assumes there wont' be different ai's with different types of voices and writing styles.

Students are expected to understand grammar and punctuations but grammarly is allowed and even recommended by many tutors.... students can use grammarly to improve the quality of writing. If grammarly was embraced why shouldn't other AI advancements be embraced. If 20 student's out of 100 use chatgtp to answer the same question won't turnitin flag some of them for plagiarism? Even when you use chatgtp you still have to read and edit the output ... you have to assume the answer you are getting has not been submitted elsewhere... It cannot write 15000 words dissertation or even 5000 words essay asking the student to analyze a case study... The student will need to ask several questions to get a good word count and also proof read and also format references.... At masters level Chatgtp is just like advanced Google .... It gives you the information you need instead of having to search multiple websites... But you still have to study the output and make it align with your intentions... The output will not always be original as more people use it and as more students submit to turnitin database or safe assign... Schools should wait before implementing any policy... Despite turnitins claim students are able to beat it using paraphrasing softwares... Turnitin is just another company trying to sell it's services... Institutions should not always jump at every service or product or update offered by the company.... PowerPoint slides with narration cannot be done by chatgtp... Student input is still needed... The process of creating the slide is the learning process it makes no difference whether the answer was provided by chatgtp Google or school library...


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Writing the introduction section of an academic journal
I have a question about writing the introduction section of an academic journal.
In particular, is it appropriate to include the potential impact of the research being proposed in the introduction section? For example, lines that begin with the following:
'This research offers a promising avenue for advancing our understanding of ...'
Are they supposed to be found in the introduction? Or is this kind of writing appropriate in some other section, if so, what section could it be?

Are you writing an article in an academic journal? or are you preparing the preface to a special issue of an academic journal? please be clearer

What do the journal guidelines say? Structure of introductions are very field and journal dependent.

@EarlGrey This is for a normal issue academic journal, not a special issue.

Is it the introduction to the journal or the introduction of a paper you are submitting to the journal? sorry for asking again.

You should often follow the norms of the field you publish in, so that your fellow readers can quickly extract information from your papers.
With that caveat aside, it is perfectly sensible to discuss the potential impact of your paper in the Introduction. I personally have a mental four-paragraph template for the Introduction that goes something like:
Context

Adding numbers is fun, as established with the seminal result that 1+1=2 (von Count, 1982).

Challenges

However, larger addition problems can be challenging, as seen in several recent unsuccessful attempts to add 1 to 2 (Ernie and Bert, 1984).

Our Awesome Paper

In this work we offer a comprehensive derivation that 1+2=3, drawing from number theory, Diophantine arithmetic, and analytic continuation via deep neural networks.

Future Work

We expect our methods may be of assistance in tackling the yet harder problem of adding 1 to 3.

I would say an introduction to most pieces of academic writing should highlight what's important about the piece as well as the topic and argument. If what's important about your writing is the impact it will have on future reasearch, it should definitely be in the introduction; if not, it doesn't need to be.

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Writing an academic reference for a friend that was a fellow student and not a mentee
Important Context: I should note that while we are both American and did our BA/MA in the US, both of us have/will have gone abroad for our PhDs. My friend is applying to a university in the UK and has already made personal contact with potential supervisors in this department.
I completed my PhD a few years ago and have been working as a postdoc at a well-respected European university since. I have a friend from my master's degree, which we both completed about 8 years ago, with whom I have remained in contact and acted as a bit of an academic mentor for throughout our MA and now as she has decided to pursue a PhD. I have a solid understanding of her academic abilities (know most of her grades, for example, and worked with her in group projects and understand her learning abilities and academic drive) and her personal commit, which I do think would make her a good fit for a PhD.
My dilemma: She asked if I could act as an academic reference for her as she has not been in touch with any of our instructors from our MA in the past 8 years, and I do, of course, work in academia. In the moment, I said yes, but now that I have received the link and it specifically asks that I explain "how long I have known the candidate and in what capacity", I realize that I would need to note that I was a fellow student, not just explain when we met and what I believe her academic abilities to be.
I was originally assuming I could simply write something like, "I have known X since her time in Y degree at A school" and then proceed to explain different examples of her work ethic and abilities (of which I have many because, as I mentioned, I have been mentoring her and know her story in quite some detail). Now, I am wondering if it will be noticeable if that is all I say or if it will hurt her if I specifically mention that we were students together during that time and I have acted as an academic mentor to her since then.
QUESTION: Will it hurt her either way if I: (1) only mention knowing her from when she started her MA and specifically do not mention in what capacity or (2) do include that I was a fellow student with her in her MA?
P.S. I have read quite a few other questions here that are similar, but many of them seemed to be noting that the person could not actually speak to what the readers would want to know (i.e. academic performance and abilities), but I do believe I can speak confidently to such information.

In case you've had worked on some projects or papers together, you could describe your experience and evaluation in reference to those joint works.

I'd answer "in what capacity" with something like "fellow student and (later) unofficial mentor."

One of the points to consider with LORs is how advisors are going to view the perspective of the letter writers themselves. They are going to look at a letter and ask "Is this person writing from a perspective similar to mine?" and if that letter writer is a PI/advisor, their words are going to carry the most weight because the answer is going to be yes.
In some cases, post-docs and others can provide a valuable perspective when they have overseen the applicant in a research capacity, but that seems not to be the situation here. You can skirt around the fact that you are writing as a friend, but the fact is that rank matters when writing letters and the reader may see through this, then wonder if there is a more significant reason than time as to why she selected a peer to write for her instead of a faculty member. If this were my friend, I would urge her to reconnect with former mentors over using personal connections.

Unless explicitly requested that it had to be written by an academic supervisor, I think an academic peer can serve as a good referee/recommender as well ! It feels equivalent to a colleague of the similar position/rank, writing a letter of recommendation for another colleague.
I think most importantly, as you have mentioned, you believe you can speak confidently about such information. If you can provide concrete examples to back them up, I think it would be perfectly fine !

Thanks. I'm not sure exactly what the wording provided to her was, but she said "academic reference" to me, which implies to me that was the only explanation/direction provided. Question: Do you think I should be clear that I was a fellow student or leave it at "I have known X since her time in Y degree at A school" and begin describing her suitability and providing examples?

@NGlynn i feel that both are fine. But do let your friend know, as what the other answers has said, that since you both are peers to each other, the letter may carry less weight.

Axiom: the weight that an LOR carries is, very loosely speaking, equal to the credibility of the letter-writer (in the context the LOR is related to) times the strength of the writer’s recommendation.

If you believe this axiom is correct, then your letter will carry just as much weight as it would if it were stating the exact same opinions but expressed from the point of view of a postdoc of equal repute to you and who knows your friend as well as you but in a different capacity (for example as her mentor, coauthor, etc).
Personally, I believe this axiom is correct.

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Why is bibguru not detecting an ISBN reference?
The WHO have a report entitled

Global report on hypertension The race against a silent killer

ISBN 978-92-4-008106-2
When I put the ISBN in BibGuru it cannot find the report.  Which is strange as it's not like the WHO is a fringe organisation making up ISBN references.
It's also sadly ironic because I'm sure the intention is to highlight the silent killer but I can't cite the report via BibGuru.  Any ideas why the ISBN cannot be found on BibGuru?

Are you sure BibGuru's database covers WHO reports in the first place? The documentation in the help section seems to be rather lacking so it's hard to tell. And what do you mean by "I can't cite it"? Surely you can add it manually?

Changed question to read I can't cite the report via BibGuru I like to use these services to be sure I don't get anything wrong. I thought the point of an ISBN is to have a central registry that service can reference? BibGuru advertise ISBN to citation as a service

For what it's worth, the "+More" button seems to allow adding references manually via BibGuru. Given that there are many ISBN registration agencies covering different countries (remember, the system was introduced in 1970) it seems at least possible that such websites wouldn't cover everything. Anyway, I hadn't heard of the site until your post, so I have no definitive answer.


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Consequences of unwittingly reinventing an idea
Say you have an idea and you can't find any paper or book that explicitly states that idea. You don't know for sure if you're right in assuming that you came up first with that idea so you decide to publish it. What are the consequences if you're wrong and the idea has already been published. Do the reviewers point that out for you before the paper gets published? Is your career over because of that?
To be more specific, I have an idea for constructing iterative formulas for solving systems of nonlinear equations for any convergence rate. I also have an elegant proof for that. The problem is that I have found just one article that explicitly states that idea just for functions of one variable, but doesn't expand on it.( just one paragraph and then continues with something else)

If you're lucky, the reviewers will point out the earlier publication. If you're unlucky, the reviewers will not know about the earlier publication, your paper will get published, and later, when (or if) someone points out the earlier publication, you will feel lousy about it. This happened to me twice; in both cases, I published (in the same journal as my paper) a one-page note acknowledging the prior publication of the result. My career did not end.

Ideas are a dime a dozen. Executing on the idea is the hard part.

Possible duplicate of Is it plagiarism if someone else came up with something before you did?

@Wrzlprmft, I don't really think this is a dup of the post you mentioned, though they are similar. The one asks if it's plagiarism. This one, while using the term plagiarism as well, is really asking what happens if you try to publish something without realizing it's been done.  I think I will try to edit the subject.

If it was an honest mistake, it probably won't be a big deal.  That said, some authors react worse than others.

@JonCuster Kind of a strange conclusion, but I guess it depends on what "idea" means.  What does 'executing an idea' mean in mathematics?  I'd say that, at least in mathematics, ideas are worth their weight in gold.

@BinaryBurst Are you saying you have an $O(1)$ algorithm for solving systems of nonlinear equations?  I mean... you have to at least read in the equations first, right?

No not $O(1)$. It's a method to construct the iterative formula up to the desired convergence rate then you use that formula to iterate until you get a desired number of exact digits.

Ignorance is not plagiarism. If you write that "To the best of my knowledge" the algorithm is new, you imply that you researched the matter duly (I would suggest to consult some experts if you are kind of an outsider to the field) and found no prior publication. If such a publication exists, it will not be plagiarism. It may be embarrassing, but not a career killer by any means (see first comment to your question).
However, if you know that a suggestion exists for the one-dimensional case, you should state that: "This algorithm has been previously suggested for the one-dimensional case by XXXX." You can also qualify this statement to better highlight what your work adds: "...but no proof of convergence was offered." or "Here I generalize the algorithm to n dimensions and provide a proof of convergence."

We didn't know that a result was already proven (this was in olden times when print journals took years to publish). After the paper was accepted we put a "Note added in proof" mentioning the other paper. We had some citations as "and independently by". (Our proof was different and only a part of the paper.)

It is the duty of reviewers (in fact, part of THE main duty is to see if the paper makes a original contribution). If you have made  an honest effort to find previous work, talked to your colleagues and/or advisor ( if applicable), your job is done. Plus most good ideas are rediscovered ALL THE TIME, in different subfields, different context etc. However, don't be this guy:
Rediscovery of calculus in 1994: what should have happened to that paper?

I'm pretty sure I'm not ;D

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Postdoc in reputed university vs Assistant professor in below average university
I am a fresh Ph.D. scholar. I have applied for several academic jobs and recently I got acceptance for a Postdoc position in a university (world QS rank 71) and an assistant professorship offer in a university which is not among top 1000 university in the world but known as an average university in my subject area.  Being a fresh Ph.D. scholar what should be my priority? For both positions, the contract is 2 years. Which job experience, postdoc or AP, will give me better chance to get a better position after 2 years?

what is your subject area?

@krebto Computer science

It's probably worth explaining what it means that the AP position has a contract of two years.  Is it not a tenure-track/continuing position?  You can't get good advice on the job titles alone; you have to consider duties (especially teaching), etc.

@ben for AP the contract can be extended after every 2 years. It's a newly established department; HOD told me that besides teaching responsibilities I may expect a lot of administrative and managerial responsibilities. For postdoc position I will work on two projects along with 1 PhD and 1 MS student.

@MBK That sounds like a giant red flag to me.  Unless that department is where you want to be forever, I wouldn't do it.

@MBK You should add the details in your comment into the question. What your responsibilities will be as a postdoc and assistant professor are quite relevant to the question.

I just checked the University that is 71st on QS...please DONT go there, you will destroy your life

What are your long-term goals? Advice would differ depending on whether you're looking for somewhere stable to raise a family or to publish as much research as possible.

The fact that the AP position is only two years is very suspicious. The danger is that you spend two years doing lots of teaching and administration and little research, which means you will struggle to get another academic job afterwards. Unless the AP position is permanent (or tenure-track), I would definitely prefer the postdoc in terms of career advancement.

A good boss (both personally and scientifically) is everything. Try to find out where you will get the best (direct) supervisor/professor because that is where you will most likely be both happier and more productive.

University ranking offers almost no information; you need to look at the quality and reputation of the departments that are offering you jobs.  For the sake of answering the question, let's assume that you have a postdoc offer from a reasonably strong CS department (KU Leuven qualifies) and an assistant professor offer from a mediocre department. I also assume that you eventually want a faculty position in a strong department---neither of your current offers qualifies.  Finally, let's assume these are the only two options you are willing to consider.
Take the postdoc.
First, moving from a weaker department to a stronger department is significantly harder than moving in the other direction.  Hiring committees will make snap judgments about you based on the reputation of your current department; all else being equal, applications from stronger departments are more successful.  (Of course, all else is never equal.  And the fact that people shouldn't make snap judgments like this is irrelevant.) Same goes for funding applications and conference/journal publications.
Second, weaker departments (at least in the US) have less (or no) expectation of faculty research, and therefore provide fewer resources for research. Those resources include strong graduate research-active colleagues, and (most importantly) time. Weaker departments tend to have higher faculty teaching loads with weaker (or no) TA support, and you've already been told to expect a large administrative workload.
In short, if you accept a position in a significantly weaker department, you are likely to be less productive, and the research you do publish is likely to be valued less.  This discrepancy will offset any benefit from accepting a faculty position earlier.

Downvoter: Care to explain your disagreement?

I downvoted for many reasons. One is that you usually have more opportunitis to get grants and students as associate professor, leading to more papers, possibly some last author papers. And you get leadership experience. All of these are very important to get further grants and better jobs. So I think your idea of how hiring committees work is not right, and most certainly not true for many hiring committees.

One is that you usually have more opportunitis to get grants and students as associate professor — Not in my experience, but this may vary by field. (My field doesn't have "last authors".) — I think your idea of how hiring committees work is not right — I've been on my department's hiring committee for about 15 years, including three as committee chair, and most of my former PhD students hold tenure-track faculty positions.

In the hiring committees I have seen, people are judged mostly on their research output: the number of papers and the reputation of the journals. If you already got a grant this is a big plus. Some grants require people to have a permanent position. My personal experience is that a good boss massively increases research output, a top 10 university (I worked in 2 top 10 EU universities) does not. Especially as a young postdoc you need papers, not "university credentials without papers", hence my own answer and downvote of this one. I also think your bold "take the postdoc" is much too direct.

(I worked in 2 top 10 EU universities) — Europe and the US have very different faculty hiring practices and politics.  That said, I'm not proposing "university credentials without papers", but the exact opposite.  Taking a faculty position with fewer resources and a higher teaching load means publishing less.  Conversely, taking the postdoc means publishing more.

I think you should think about the final result that you plan to achieve at the end of this work. For example, the goal is to publish as many scientific articles in the best journals as possible. Then you must assess your abilities and resources to achieve this goal (based on your experience during PhD). If you think that you have enough strength to generate ideas and publish good articles in good journals, then professorship is for you. If you are not ready for an independend job and needs some supervision, then go for a postdoc.

@levgen Thank you for your answer. I can generate results independently. However, being part of a research group which is already producing good results will definitely increase my capacity to publish more. The question here is what is more beneficial for a fresh PhD? 2 years of AP experience in an average university (less publication but independent job experience)  or  2 years of postdoc in reputed university (more publication but not an independent job experience)

@MBK Again, if you are sure that working as an assistant professor will not lead to a sharp drop in your performance, I would choose a professor. If there are doubts about the ability to work completely independently, then go for a postdoc. At the same time, the university's rating itself is not so important for your case in my opinion.

If the Assistant Prof. position will eventually lead you to tenure and if you feel confident you can manage it, you should go for it.
A postdoc in a reputable university doesn't mean anything if you don't end up publishing good papers, which of course you can't know in advance.
In the end, the job of a postdoc is to find a stable position, and you have been given the opportunity to reach it through the Assistant Prof. contract.

At the end, both are job. Based on your previous research achievements, e.g., number and venue of publication, you can choose PostDoc or Asst. Professor. It's not only about independent research, many factors are related. Also be aware that this short term Asst Prof. deals with overloaded teaching assignments, in that sense, PostDoc will bring your own research identify.

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Editor invited after accept minor revisions required
I sent an article and it was accepted minor revision required. After resubmitting the paper the status appeared editor invited and has not changed in more than a week. The first time I submitted it, they just assigned an editor. What does this mean?

It means you should chill out.

To wait and not to worry.
Editor might be busy to handle your submission.
You might expect that since it is a minor revision, then it will be accepted almost immediately. Sometimes it is indeed the case. But this depends how efficient the journal is.

But this depends on...?


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PhD Application
I was contacting a potential supervisor for a PhD program. I was happy to get a reply from the Professor stating that my proposed research interests is in line with his research activities.
The Professor then asked me to look for other financial sources since he currently doesn't have the funding to support my research. As per his guidance I have successfully submitted my application to the suggested scholarship.
Now, I would like to know whether I should update the Professor about my submitted application and also share my application details??

There is nothing wrong in it. Especially if you are aware of admission procedure and you know that professor plays important role in admission process.

Yes, you should. This is common courtesy actually.


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What aspects of software shared with a quantitative paper are the most important?
I'd like to get the software-sharing aspect of my research paper right. (This empirical paper in health economics relies on code for modelling and simulation.) Besides open-sourcing the code on GitHub, what other aspects could be valuable to readers? For example:

Well-structured (clean) code
Tests
Documentation
Usage examples
GUI

Knowing this would help me prioritise the development efforts.

"Would it be to check the code for bugs, validate the paper methodology, reproduce the results or apply it to similar problems?" - Yes.

@BryanKrause You'd do all of these? But which aspects should I prioritise?

What would it mean to prioritize those things differently? How are you going to provide software that validates the methodology and reproduces the results but cannot be used to check for bugs? Are you creating something new that would be reasonable to apply to similar problems or are you using existing packages where someone would be better off using those packages and documentation directly?

@BryanKrause For example, I'd decide how much to focus on adding unit tests. Normally, do you comb through thousands of lines of spaghetti code submitted with a paper?

If code for a paper consists of thousands of lines of spaghetti code, which of "check the code for bugs, validate the paper methodology, reproduce the results or apply it to similar problems" could the code be useful for?

@BryanKrause All of the above, except it'll be extremely difficult and time-consuming. I guess the current question is too obvious, so I'll edit it.

I think that's the wrong answer; I think it's not useful for any of them. Code that makes things extremely difficult and time-consuming for others is not useful.

Probably better than nothing, especially for some users. (I once spent 3 weeks translating 48 pages of horrible C code.) Also, it still might reproduce the paper results and work for other inputs.

Overview, examples, installation instructions, clear versioning, paper citation info. The code base will change.

Congratulations on open sourcing your code! If only more researchers shared your interest in software quality :)
As to what to do next:

Well-structured (clean) code
Tests
Documentation
Usage examples
GUI

None of the above! (Well, not exactly.)
The most effective thing you can do to increase the likelihood that other researchers will be able to reuse your software is probably:

Provide clear and detailed installation instructions, including what operating system(s) the code will run on, and what dependencies are required; and

Try out the installation instructions yourself, on a fresh machine with no other software installed, to see if it works.

The idea here is that if other researchers can't even run your code, it is unlikely that tests, clean code, or documentation will help. You might be surprised how often this happens! Sadly, in my field (though I don't know about yours), perhaps 9 out of 10 software projects fail to replicate at this stage, due to various missing dependencies, broken build scripts, or platform incompatibilities. The problem only becomes worse with time.
Depending on your field, other solutions may be worth investing in:

For example, creating a website where your tool can be accessed / tried out, if feasible, can be a good way to increase accessibility, but takes a greater time investment and a web hosting service.

Providing a pre-built binary that will work on common machines (e.g., x86 windows and Mac OS M3) could also be worth considering.

Finally, if you are worried about long-term availability of your work (for instance, after current operating systems and dependencies become obsolete), you can also package up a version of your code with a virtual machine software like VirtualBox. This is a common solution for long-term digital artifacts, used for example by the ACM Digital Library. The downside of this is that downloading and configuring VirtualBox often makes it difficult to reuse your work in the shorter term, at least, for all but the most determined users. So it should not be considered a replacement for good installation instructions.

The installation/deployment part is something I did not prioritise -- that's very helpful to know! I guess Docker is a no-no unless it's for CS people. Would running pre-built binaries get scary messages from Mac OS, like "danger: unknown developer", and frighten some people off?

@schrödingcöder It turns out that Docker isn't even cross-platform. It's a common misconception, as it used to be largely true (through emulation), but is no longer true for newer macs (M1-M3). About the unknown developer warning, I wouldn't be too worried, but that's another thing to mention in your installation instructions :)

There is a big misunderstanding, and it starts with your besides.

Besides open-sourcing the code on GitHub

Using a centralized service as Github as the repository of one's own code is ... damaging open-source. And science in the long-term.
An example of some critiques can be found here: https://sfconservancy.org/blog/2022/jun/30/give-up-github-launch/
The rest you propose is all good, in principle, but in the end a software is as good as the assumptions behind it and as bad as the bugs it includes, so spending time on these two extremes (assumptions = scientific publications, bugs= bugs hunting) is all you should do, and the rest is "nice to-have" (if your days are 36 hours long ;D ). Try to document the code on the go (Doxygen and similar systems), so you do not need to do afterwards.
Good luck!

I didn't see GitHub this way, but the link does have valid points -- they're good to know. But GitHub is so enmeshed in my and other's workflows, a bit like an addiction. I wonder if you've looked into alternatives and could recommend something decent.

As an alternative to Github, I once worked with a compnay having a private gitlab server. It was working smoothly, but I am not sure if the same critiques apply to gitlab as well. For simple access to code, there is Zenodo https://about.zenodo.org/ which is open, public-funded (i.e. not for profit) and in principle there are clear clause whether an AI can scan its content and use for its own purposes.

While I agree with the criticisms of GitHub, I don't see how simply putting your code there is "damaging open-source." In fact if you attach a permissive license (e.g., MIT or CC BY) anyone can copy your code and later upload it to some other platform anyway.

@CalebStanford exactly!


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Can an applicant who has 16 years of education directly apply for PhD admission in electrical engineering at US universities?
Can an applicant who has 16 years of education (BS Electronics Engineering) directly apply for a PhD position in electrical engineering at US universities? Or are they only eligible for admission in masters of electrical engineering?

Regarding the issue of Masters vs. Ph.D. admission in the U.S., see the X = United States answer to the question How does the admissions process work for Ph.D. programs in Country X?

Please pay attention to the answer of the linked question. In particular, "The applicant is normally expected to hold at least a bachelors degree or be close to completion. There are rare exceptions to both of these."

Anyone can apply for admission to any program with any credentials.  The issue is whether the application has a reasonable chance of success.
Overall, it is very rare that applicants are allowed to bypass the usual admission requirements, but there are cases where this happens based on the unique circumstances of a particular applicant.
A well-known example in physics is John Moffat, who entered a PhD program in Cambridge without any prior education in physics following an exchange of letters with Einstein.
I personally know of two other examples (one an MD, the other a fast food worker) who were admitted into PhDs in Mathematics with no previous training in the field but after they had demonstrated suitable competences.
Personal experience in the field may replace formal academic competence for admission, but I wish to emphasize that this is extremely unusual and that not every department is open making this kind of exception.  If indeed you are admitted outside of the usual requirements, expect to have to take “remedial” courses, and possibly an entire preparatory year, in order clear any condition of your admission.


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Do journals check for plagiarism after a paper is accepted?
When I first submitted an article to a journal, Turnitin showed 10% plagiarism. However, it went through many revisions and each time I added some text, but did not check for plagiarism again, because I somehow could not access Turnitin for some reasons. Now that my paper is accepted, at this stage I have signed the copyright, will it be scanned for plagiarism again?
I have heard that each time we submit the paper (even if it is a revision) it is checked, but not post acceptance. Is this true?

If submissions to turnitin go into their database, the percentage will rapidly increase…

No I did not select the option of saving to database in Turnitin  and i only submitted once.

If you didn’t plagiarise, why are you checking it on Turnitin?

@isolatedmatrix as a pragmatic matter you not only don't want to have plagiarized, you don't want to give the appearance of having plagiarized. There is also the problem of unconcious plagarism.

@CharlesE.Grant I’ve always wondered about unconscious plagiarism, so thank you for the link! That being said, if it’s referenced correctly and genuinely written by (one of) the author(s), there shouldn’t be an issue.

Submitting to turnitin does not check plagiarism, it checks word similarity. If someone hasn't written the paper and wonders if it's plagiarized they might use similarity to decide if they should be suspicious. If it's not plagiarized and you know that because you wrote it you shouldn't submit it to any software to check similarity because there is no reason to.

Why would you submit an article to turnitin before you submit it to the journal for review? You know your work so you should know if you plagiarized. Not to mention your manuscript should have been reviewed by co-authors or trusted collaborators before submission to the target journal. You are overcomplicating the process. Frankly you sound unaware and/or underprepared to submit a competitive manuscript.

Jason, some relatively low quality journals use plagiarism checkers.

Maybe so and that is why they are "low quality". The peer review process would catch plagiarism, but maybe some journal some where is trying to outsource that process to AI. I personally would feel suspicious of any journal, editor, collaborator or any academic that included such a step in the publishing process for a research manuscript.

If you are trying very hard to cleverly plagiarize (intentionally attribute to yourself the work of others) by using careful wording (paraphrasing) or other tricks, then you should be concerned. Otherwise, ignore the issue unless the checks indicate that your research is sloppy and you should have done more background research. In the latter case, do that research.
Clever plagiarism might get past a brain-dead checker but it won't, likely, fool reviewers. And even if it does get past them, those in the field finding your work will know.
Yes, the above is rough, and I don't accuse or suspect you of that. Therefore, given that the ideas are yours, relax and present them in the clearest way possible. If people, then, raise issues deal with them as they occur. It isn't a game. It is a search for truth.
If the journal is reputable (as is likely true of IEEE journals) and none of the reviewers have suggested other sources or mentioned plagiarism then relaxation is warranted on this issue.  Have a culturally appropriate beverage and do the next edit.

Aside: I'll guess that as things like turnitin merge with things like LLM AI tools, more and more things will get flagged that are not, in fact, examples of plagiarism, though possibly of incomplete background research.
In the current situation, journal acceptance isn't an automated process, though some automation might be used. Papers are published or not based on the informed judgement of reviewers and editors.

I have written a review paper so its very natural that some of  my words will match with that in the paper i reviewed. The article is my original idea and my Professor who has worked a lot in that field has approved the idea and why would i copy some other people work I really have no incentive to do so


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Do desk rejects happen after one and half months?
I submitted a paper to a math journal and after one and half months (the status showed "under review") it was rejected without any report. Does this mean it was a desk rejection?

Not sure what kind of answer you're looking for, but the brief answer is "yes".

If this is something that happened to you right now, also keep in mind that the preceding one and a half months include new years, christmas and thanksgiving, which might simply have delayed things compared to normal.

I think this is not usual, but if the editor has trouble finding referees this can make them decide to desk reject.
I've been waiting almost two months for the editor to assign reviewers to one of my papers and I'm expecting a desk rejection at some point if they don't manage to find reviewers. The peer-review crisis is probably making this problem more common.

An apparent case of desk reject after failing to find referees can actually be found in one of OP's earlier questions. One and a half months over the holidays seems too short a period to give up on though, so the comment about a quick expert's opinion seems more likely IMO.


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Authorship of class notes written by students
Recently in my department we decided to re-organize the teaching program. For this reason, I had to prepare almost from scratch a new course, which I taught this semester.
The course is on a topic on which I do actual research, so I was happy to do that, but it was also quite an effort, as I had to prepare a lot of slides, while some parts with heavy mathematics, which I taught using the blackboard, were provided as handwritten notes.
The priority was to give the students enough material to prepare the exam, in a form that was "good enough". I had a plan to write everything down on a proper electronic document, but of course this will take time.
With my great surprise, at the end of the semester I discovered that a group of students decided to write down their notes of the course electronically (using LaTeX). They also reproduced the graphs I drew on the blackboard using computer programs. They were so kind to give me all the material, even the source code.
I asked if I could provide this material to all the students, and they gladly accepted.
Of course, I will have to revise everything and add some missing parts, but not starting from zero will save me a lot of time.
Now, my question is about the authorship of the notes that will be distributed: what would be a proper way to give everyone the right credits for their work? To be specific, I am thinking to 3 possible strategies:

Put the teacher and all the students in the author list on the first page of the notes. However, maybe this can be confusing because the students did not develop the actual content of the notes.
Put the teacher as author on the first page, and the students in an acknowledgements section, or in the preface, or something equivalent.
A "middle way", which I would prefer: put the teacher as author, and the students as something like "Notes prepared by", both on the first page. Maybe add anyway some acknowledgements to properly thank the students for their work.

Before proceeding, I would like to know if there are rules on this, or some guidelines.

Do the students provide intellectual content of their own or simply capture your ideas? Is what they wrote "creative"?

#3 looks fair. It makes clear who did what and gives proper credit. No misunderstandings. It does not make clear who owns the copyright, but for that, you ideally would need to consult an expert. I suspect the copyright is either with you or the university, but I am not a lawyer.

@Buffy I would say no creative work, they just put my explanation in a written form.

The students acted as scribes and have no right to authorship. However, since without them, the document would not exist, you can give them authorship and explain the genesis of the document in an introduction. The problem with that solution would come later, if a student claims rights accruing from authorship, because you decide to make the lecture notes into a book. For example, take a student who claims in twenty years that that student wrote a book with you in order to foster their career, but you would not give that student a recommendation. Other problems could arise if you would derive creative work from these lecture notes. The problem with leaving the students out is the lack of acknowledgment of their work, but that seems to be the lesser evil.
Authors who employ a proof-reader that also makes suggestions for clarity and checks for accuracy (i.e. a doctoral student in academia) have the same conundrum. At one point of increasing involvement, the proof-reader turns into a co-author. Not only is there a sliding scale, but in addition, different cultures have different attitudes about the meaning of authorship. The only thing we know is that someone acting as a secretary is not entitled to authorship, whereas someone who writes substantial text that is incorporated into the work is a co-author.


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How novel should conference papers be?
This is probably field specific. I'm mainly interested in the STEM (science, technology, engineering, and mathematics) fields and psychology/neuroscience.
So, like the title says, how novel should conference papers be? I'm early in my career and find it difficult to judge if my work is yet publishable or not.
Suppose my advisor says nothing about the work I have done, and I have to decide myself.

In an ideal world...

The answer to this question is completely different in the context of computer science versus mathematics, for instance.  I suggest narrowing it to a specific field.

So, like the title says, how novel ideas should conference papers present? I'm early in my career, and find it difficult to judge is my work yet publishable or not.

This is extremely field specific and even specific to different conference venues. But conference venues, by offering (supposedly) quick turnaround of papers, do emphasise novelty of results.
Speaking as a reviewer, in judging novelty, I look at four main things:

Is novelty made clear: Have the authors clarified what methods have been presented in the literature and clearly argued how their work differs?
Does the novelty make sense: Oftentimes, authors strap things onto previous works or make unusual design choices (seemingly) just to be novel, and then they adapt their evaluation accordingly. If you say, for example, you have designed a new "hexagonal" wheel that's different from the round wheels in the literature and you show that it's less inclined to run away down hills, that's obviously problematic. (Though the analogy is a hyperbole, this case is quite common in my experience.)
Did I learn anything: If the results are new but are already quite obvious, or are trivially derived as a synthesis of existing results, novelty is poor. As a reviewer, I need to learn (or at least confirm with solid data) something non-trivial!
Could this lead to further research: It is certainly a boon for the paper's novelty if it could lead to a new line of research; i.e., if it's novelty warrants further investigation, and could lead to further publications, particularly by other authors.


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How to maximize performance in social science PhD program?
I am currently enrolled in a social science (management/information systems) PhD program, and I have spent a lot of time thinking/experimenting with how to maximize my performance. First I tried working 80 hours a week - this led to a hard time focusing, more time spent doing support activities (e.g. reading) rather than core activities (e.g. writing, coding etc.). Now I'm starting to settle more towards a 40-50 hour week that keeps me more energized the hours that I actually work.
I'm wondering, in your experience, what is the optimal balance of work/rest for graduate academic performance in the social sciences? Other factors that stimulate your academic performance?

I think specifying a general field will help. Answers for big-lab science will be different compared to more individual and theory heavy fields. But most important advice: if it always feels like work then maybe it is not the right job.

Thanks. That's great feedback. My field is social science, so a lot of alone work, writing is the highest priority. I've edited my post.

I too have struggled at times to find this balance.  I'm coming from math and computer science (rather than social science), but I think the field matters less than the type of work.  If what you're trying to do is cognitively intense, then it's really important to be well-rested and to maintain focus.  In grad school, I often would bang my head for hours on problems without much progress.  Now if I work for 45 minutes or an hour with little progress, I'll go for a short walk, get a light snack, do jumping jacks, or play a quick web game; all of these are ways to reset my focus and attention.
Probably the single most useful skill I've learned is to be more aware of my current state (energy level, focus, sleepiness, hunger, emotional state) and try to choose work appropriate for (doable in) that state.  For more thoughts on this idea, I recommend the advice given by Terry Tao here: http://terrytao.wordpress.com/2008/08/07/on-time-management/

Thanks! That's a great answer. Do you think you could elaborate a bit more on your thinking with regards to taking a whole evenings, weekends, and vacations to rest? What you have described provides great insight into the "micro-cycles" of work and rest, but I'm thinking there are also "macro-cycles".


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How to interview a candidate for a university teaching position?
We are interviewing some candidates for a part-time teaching faculty position to teach 2 upper level undergraduate courses in mathematics.
Since it is my first time to be in such committee and since this is a teaching job on the university level, I wanted to know what are the criteria/questions that are teaching-related and that should be asked to the candidate in the interview?

I want to point out that the fact that you are asking this question strongly suggests that you're not really qualified to perform this interview. You should probably rely heavily on other committee members who have more experience with this sort of hiring. (Yes, I know that you may have to do it because "no one else is any more qualified", but teaching is as complex a profession as any, and you can't hope to become expert enough to find a solid teacher based on a few questions in an online forum.)

@eykanal That is exactly what I have done already after posting this, I told the chair that I am not experienced with such committees. He said, talk to senior faculty to know about how this should be done. I just thought of keeping this question on so I might gain some tips from the experiences of others out there. Nobody is born knowing everything, and everything has a 1st time, including being in a committee.

You'll want to ask some standard questions that any experienced educator would ask a potential teacher:

Request to see a model lesson. It doesn't have to be a full lecture, just something to see their style. Ideally the entire committee should be there for this part.
Ask to see a university-level lesson plan of their creation.
Ask how they would approach creating a semester-long curriculum for an advanced "special topics" course. I suggest the "special topics" idea because this lets you see whether they can do it for themselves or whether they are only able to follow pre-set curricula.
Ask how they'll ensure that the top students are being adequately challenged while the weaker students aren't being left behind.
Ask how they handle unruly students. Even though this is fairly rare at the university level, they should have a ready answer.
Have them explain some concepts to you and make sure for yourself that you are comfortable with how they explain them. (Similar to model lesson, but for specific topics.)

You may also want to mention things like office hours requirements, whether they'll get a TA, and related administrative stuff.

My university requires a teaching portfolio to be attached to the job application. This will typically include:

Teaching practice (your education, courses)
History of teaching (activities, awards)
Student feedback (with comments on how did your teaching chanre as a response to these comments)
Materials you prepared for students (with comments on how and why they evolved)

This does not really answer the question about how the interview is done.

Because there was no such question.

The title is such a question.


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How to introduce myself briefly in a get together for new faculty members?
For the new faculty orientation, there is a get together where they will ask every new faculty member of all departments to say a few words about him/her self.
What are the best practices regarding that? any advice/examples/templates of how to do that briefly, eloquently and professionally?

Relax.  They don't bite.

Partly, you'll want to copy what others are doing.  If everyone else only talks for 15 or 20 seconds, then almost anything that you say in a 3-minute monologue will be poorly received.  Information that you'll likely want to mention includes: name, department, research interests, possibly where you've just moved from, and maybe a bit about you personally (I enjoy triathlons; married with two kids; active in a barbershop quartet; etc.)  One point about your research interests: keep it very nontechnical.  Rather than give many details, say something like I write computer programs to help blind people use computers, or I study 18th century literature, particularly the works of SoandSo.  Most likely, this activity will be pretty informal.  If you're still a little nervous about it, you might ask another junior faculty member in your department.

Assuming you have many departments, mostly your speech shouldn't cross 1 minute max. You should include below details.
Full Name and Native Place
Department,
Job post ( Jr / Sr / Head etc )
Year of experience, Previous work place if any
Specialization/ Research subject, Anything additional course you are taking for future, future prospects.
if time permits abt family. Welcome others and happy to join the team to move further.


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Affiliation for publication
I recently got accepted into one of the schools in Europe. According to the anticipated date mentioned in the offer letter, my studies will start in Dec 2017. Now, the problem is that I've got two papers ready for submission at the moment and I don't have the faintest idea what to put for my affiliation. Any ideas?
Just to clarify that this question is not a duplicate:
When I was working on the above-mentioned studies, I was not either employed by or affiliated with my former university. My former supervisor did help me out with some parts of the papers, but rather as a colleague or a friend than as a "supervisor" in the true sense of the word.
Thank you

I think it is a common practice as your affiliation you use the one that you had during performing the work. This is especially true when you do not have new affiliation. For example, a PhD student that publishes his/her PhD paper while he/she is already two years into a postdoc. Many people will still leave the PhD affiliation, probably also add new one. Otherwise, it will look weird that affiliation of your and your advisor is different.

Otherwise, it will look weird that affiliation of your and your advisor is different. — No, it'll just look like you graduated.

When two authors have completely different affiliations this looks as though they are just collaborators....Obviously, all this does not matter much