When it comes to AI, just saying no isn’t an option anymore.
Two essays recently appeared that show just how different the world is for university-level learners and researchers since the early days of ChatGPT. In November 2022, the first publicly available AI chatbot was laughable as a serious tool—little more than a parlor trick and easily characterized as a high tech, stochastic parrot. Just four years later, the circumstances have changed.
The research paper is now dead
To my mind, the traditional research paper as a tool of differential assessment is now dead. And I think it should be left to rest in peace.
Very few student papers have ever been read by anyone other than the student and the professor. There are exceptions, to be sure, but they’re rare. The point of the undergrad paper was to teach the student how to go through the research and writing process and, in the course of doing so repeatedly over several years where the requirements became successively more onerous, to hone their analytical and interpretive skills. The idea was that one left a history degree not only with knowledge about the past, but with an understanding of how to find things out about the past in a rigorous way. It was the skill of conceptualizing a problem, amassing the best evidence, and distilling an argument that was valuable to both the student’s intellectual life and to future employers.
[…]
[I]n my view, if it’s the traditional process that’s intrinsically valuable, contorting it to avoid AI at all costs risks reinforcing (or exacerbating) many of the problems posed by the tech in the first place. The risk is that we’ll lose focus on how students have to exist in the world they actually live in, not the one we wish still existed….
As my colleague Jim Clifford recently argued to much opposition on BlueSky, the significance of the automated AI paper is not that it’s an object of intrinsic scholarship or that it is something of actual value. It’s that the process itself has been automated.
No one wants or needs AI generated undergraduate papers except dishonest students. People often remind me that there are also lots of fields where AI cannot even complete the assignment because of the nature of the sources. Fair enough. But the fact of the matter is that it can complete the research process in a lot of cases, much as a human would do.
This means that it’s also going to start to be useful in many of the tasks that our university and college graduates would normally be paid to do after they graduate. The technical importance about the automated process I described above is that it is intelligible: you can reconstruct how the AI agent completed the task. This makes verification much easier even while the models themselves are also becoming more reliable.
Humphries ends his essay with this sentence that emphasizes the difference of the dinosaur-era AI of November 2022 from that of fall 2026: “The inflection point, then, is not that AI can write a research paper. It could do that two years ago. It’s that it can now do everything we once said mattered more than the paper itself and we can’t reasonably pretend otherwise.”
The essay is required reading for any teacher and especially for teachers of college undergraduates.
Research preservation as a mirror of research processes
The second article comes from a librarian’s perspective. How has the adoption of AI in research changed its preservation and, for that matter, the originating—and, importantly, linked and incremental—processes of discovery and research? The piece is by Dan Cohen. I found it interesting as a reflection of the kind of scientific revolution that is beginning to arise in labs and their reverberation in the institutions like libraries and archives where lab discoveries are recorded and preserved. The research conducted in labs must eventually become part of a larger and very rigorous scientific corpus—however awkwardly or haphazardly that corpus may come together. When AI agents take on roles as “co-researchers” how does that shift provenance, preservation, and ultimately responsibility in research? How are scientists accountable for the work their AI agents perform?
UPDATE September 18, 2026: Dan sent out a note that included “good thoughts, writing, and projects from the readers of this [i.e., his] newsletter” that related to the post from which my excerpt came. The responses showed some of the productive ways AI can bolster research. That was encouraging to see, but the responses came from people who were already accomplished scholars and researchers. The knot of complications of training and learning that undergraduates (especially) present universities seems to me to only tighten with AI. It isn’t loosened. But Dan’s post inspires, too.
The fusion of agentic AI, compute, research protocols, articles, and data is a much more complex, heterogeneous scholarly object than a book, journal, or PDF. It’s also much less fixed than those traditional objects. One curious aspect of LLMs, which sit at the command center of the disciplinary workbench, is that they are nondeterministic. When you engage with existing scholarly applications, such as an index-based search tool for journals or an analytical application such as Mathematica, these tools return the same response to the same input every time. This is not true for LLMs, which will give you a slightly different answer each time—a rather odd characteristic we are still getting used to.
This fluidity is acceptable when planning a vacation or composing an email message, but it is highly problematic for the scholarly record. Accurately preserving scholarship that used AI assistance requires far more work. For example, we might want to note which AI model was used by the researcher and store a transcript of their interaction, but is that really enough information? Do we also need to preserve the model itself? If so, how do we do that, given the enormous scale of even the smallest current models, not to mention the massive models to come? Moreover, since the outputs of LLMs are nondeterministic — in my experience, Claude’s search of academic literature returns a different slate of articles for identical research queries — simply recording the model used does little to preserve how it acted upon data, text, or other materials. Worse, disciplinary workbenches might have multiple agents running parallel processes for minutes or hours, rather than a single chat stream. How do we begin to understand and preserve what happens during these processes? If we are struggling to reconstruct what a swarm of AI agents did during a hacking event this summer, will the record of a university research project be at all legible in the coming years?
A list of the connectors available to the LLM is similarly amorphous: it gives us only an overview of the vast resources a scientist may have consulted, and not a concrete list of all of the materials scanned, summarized, or synthesized. Hopefully individual journal articles will be referenced in a more traditional fashion, i.e., in footnotes and a bibliography, but that is not guaranteed. More promisingly, we could save the skills documents in a preservation package, since they are relatively slim text files, and we have some experience saving similar files, such as with Protocols.io, a service that stores the rigorous processes a researcher might use in an experiment.
One thing is clear: the preservation of the scholarly record is going to require more intrusion into the researcher’s work and digital environment. If we continue to save only the end products of scholarship, such as an article or book, and associated artifacts like data sets, will there be an erosion of trust in the scholarly record given how many other digital tools played a role in the research? How can we discover and diagnose researcher errors when we can’t access the distinct blend of skills, connectors, data, resources, and AI models behind them? If the disciplinary workbench becomes a black box we can’t see into or understand, we will have trouble reconstructing the scholarly process and its conclusions.
Cohen’s essay let me know about some of the new tools that are being used. Exciting. Also a bit frightening. His periodic essays on Humane Ingenuity are always insightful.
The articles
Humphries, Mark. “No More Muddling Through.” Generative History, August 21, 2026. https://generativehistory.substack.com/p/no-more-muddling-through.
Cohen, Dan. “Scholarship Will Soon Be Much Harder to Save.” Humane Ingenuity, September 3, 2026. https://newsletter.dancohen.org/archive/scholarship-will-soon-be-much-harder-to-save/.
