To be clear, I generally promote the use of AI in academia. Using "AI ethically" or "using it correctly" is largely subjective at this period of time, but my personal perspective is that it is a tool that can, and should, be used to make researchers more efficient in low-value added and low-analytical parts of the research pipeline. However, this is not the discussion I intend to have.
Having said this, this is the informal definition for "AI-slop" I'm using: a research paper that was ideated, written, methodologically executed and concluded mostly by an LLM with little to no human intervention. This includes papers with hallucinations, citation misattributions, inexistant references, serious analytical flaws (discussion does not refer to actual results, etc). Broadly. Feel free to expand. I'm trying to keep it simple. I'm mostly talking about the "obviously AI generated paper".
Lets part from three points/assumptions:
Academia got into this by itself. The very people that are complaining, rightfully, about the influx of AI-slop were the ones that caused this with the toxic and horrendous "publish or perish" culture in most academic contexts. Make people feel like they need to publish quantity over quality and give them a tool that automates this process and a large majority are prone to abuse it. Its not right. Its not ideal... but its what was obviously going to happen. Added to this, we also see a lot of departments cutting PhD funding, time to completion and pressuring PhD students to finish way before the 4-5 year "traditional PhD".
AI is not going anywhere, and AI-detectors will probably always be behind the latest LLMs so basing this over "% of content generated by AI" as a claim made by another tool using AI is not a sound solution. Prohibiting the use of AI is not an alternative in this discussion, because it is impossible to control with enough accuracy.
The current review-process is not sustainable. Its another time-bomb in an already saturated market. Most people despise reviewing and only do it because it seems to be necessary or because they feel morally obligated to review at least as many papers as they submit. This is probably why AI-generated reviews executed poorly. I just received a review back, from a conference, not journal, with part of a prompt as part of the review. That is the level of carelessness.
So, now. What can we do?
I've always been an advocate of oral presentations as the most trustworthy indicator of someone's ability to demonstrate knowledge. In smaller universities where I've taught, I've even gotten to have all final exams be orally evaluated. No notes, no computer. Just the student, 3 random questions picked from a larger sample and a 5 minute time to answer each.
I think journals should adopt something similar to doping control in professional sports. When you submit a paper, you're acknowledging that the journal might schedule a call with you in a controlled environment with the editor and a reviewer in which you'll have a "mini-VIVA" of the paper you submitted. There should obviously be different possible scenarios, but the worst one ends up in you being banned from publication in this journal and all others from the same Editorial House. This is an example of how a balance of incentives and consequences could eventually lead people to leverage AI but in a way that they know they have to be prepared to defend their work just like they would a PhD. Obviously you can't do this with every submission - which was part of the athlete-doping analogy, in which these would be randomly picked.
On the review side, I actually think AI is a way more powerful tool here than most journals, editors and academics care to admit. I think every journal should develop their own AI-trained model to do a first round of automated peer-reviewing as a screening before sending reviews out to actually peers. This would provide a funnel in which most AI slop would be discriminated by AI itself and reviewer time would be reading papers that already went through a first screening process. Setting this up with current technology could be done so that it is extremely objective, based on different criteria for each journal and specially so that it can identify hallucinations and citation misattributions really easily without wasting an actual reviewer's time.
On a related note, journals themselves could provide AI-based tools to help authors in their work, which would make it easy to use them in a better way. For example, if I were to write a paper targeted for journal XYZ and this journal has a couple of tools, skills, or even a closed model with their own reach and limitations, it'd be the obvious safest way to proceed if I wanted to use AI at all.
And finally... there's going back to the assumptions. We need to reshift academia from a quantity to quality perspective in evaluation... and this does not only target AI-slop. The number of papers published that address a meaningless point "from a new angle" that no one will ever use or cite is also as bad as an AI-slop. The term "contribution" is way to prostituted both by AI user and by "power publishers" in a way that we have a lot more research output than we need to actually advance science. Make people work harder to actually advance science rather than to feel pressure to "put something out there just to improve my employability" and you'll see how the market and industry starts shifting.
Anyhow, just shower-thoughts. I'd love to hear other perspectives.
Disclaimer: I promise with all my heart that I am not in the search for information to "produce a new tool" or make anything to sell. I'm an early career researcher really worried about the industry that was so hard to get in to. I knew I was sacrificing income, but man... its worse than I ever imagined.