[Nhcoll-l] Where should museums draw the line with generative AI?

[Thor Martin Jensen]

Steven,

Thank you for the detailed response. Your examples of AI-generated
worksheets and misdirected questions are exactly the kind of failures that
concern me. Systems generating content that sounds plausible but
fundamentally misunderstands what the institution actually holds or teaches.

I appreciate your point about trust. Museums are among the most trusted
institutions precisely because visitors know a person with expertise made
choices about what to show and how to explain it. Breaking that chain is
risky.

On your concern about the listserv format, you're right that BCC prevents
"reply all." That was unintentional. I genuinely wanted discussion, not
just promotion. I should have used a format that allowed group
conversation. My mistake.

On translation, you're absolutely right that automation has weaknesses,
especially with specialized terminology. At Walkie Talkie, we don't just
run content through a translator. Museums review and edit translations
before publishing. The AI handles the first draft to make 38 languages
feasible; curators ensure accuracy. It's a production tool, not a
replacement for expertise.

Your calculator analogy is useful. AI as efficiency tool is reasonable. AI
as creative authority is problematic. The line matters.
Best,
Thor Martin Bærug
Co-founder, Walkie Talkie
walkietalk.ie


On Wed, Dec 17, 2025 at 9:51 PM Sullivan, Steven <sulliv55 at miamioh.edu>
wrote:

> We've had teachers use AI programs that supposedly analyze our website to
> generate grade-appropriate worksheets for the students to fill out while
> visiting the galleries.  Of course 99% of the information in our galleries
> is not on our website.  Maybe we would want to change this in the future as
> the web and its use evolves but, presently, the bot/app has no idea what to
> productively compose, based on our site.  This does not stop a LLM from
> generating 10-20 questions, though.
>
> At best, the AI questions are answerable but don't relate to anything in
> our institution (eg, questions about dinosaurs when we have no fossils more
> recent than the Silurian, or magnetism when the closest we get is a
> magnetic wall that holds pictures in place to allow visitors to tell a
> story about the prairie).  Many of the questions are contextually
> pointless: "Why does the museum use magnets to teach?"  Often, though, the
> sentences are arguably grammatically correct but conceptually convoluted:
> "How can taxidermists address resource management and climate change in
> biomes?
>
> Similarly, much of the information visitors get from AI is simply wrong.
> It seems the longer the conversation, the more incorrect or misdirected the
> results get, the more they may support misapprehensions (like snakes will
> chase you, bear encounters are deadly, and outdoor domesticated cats can
> integrate with natural ecosystems.)  Granted, Wikipedia used to have a
> mistake in nearly every natural history article and it's now often pretty
> useful.  I expect AI to similarly improve.  That said, I won't ever publish
> or reference a Wikipedia article as I make exhibits, nor should any AI
> generated labels ever be used.
>
> I have found AI to be useful in brainstorming categories, rendering draft
> layouts, finding grey literature or other precedent work that can be
> inspirational to me but is hard to find online (like policy and procedure
> papers) and otherwise automating draft tasks.  It can also interpret
> cursive handwriting well enough to help me get unstuck, and as a
> translation aid, but the transcriptions of both are still full of
> mistakes.
>
> Like a calculator, AI might have value in the creation process.  But, the
> way a calculator is used, it is not necessary to cite it and the operator
> needs to understand the operations they are automating with the tool.  If
> the use of AI goes beyond that of an efficiency tool to that of a creative
> tool, it must be cited, but it also probably shouldn't be used.   The
> abhorrent AI exhibit at the San Francisco airport is an example that puts
> our whole industry at risk.  Most of our visitors find us among the most
> trustworthy of institutions.  Many of our visitors recognize that
> publically accessible AI information is mostly trash (and with that
> exhibit, find the deficiencies in "professional" AI, too).  They also value
> the human hand in our creative products.  At the very least, to maintain
> the public trust, it's important for us to minimize AI content in any
> published work.
>
> The number one question we get is "Is this real?"  We should be able to
> answer "Yes" for both objects and their labels and data.  AI can't ever do
> that.
>
> I wrote all of the above before noticing your disclaimer line.  Maybe
> translation is useful.  I used to be a translator, though, and have found
> weakness in all automation I've ever tried, especially when nuance and
> specialization are required.  That said, you used our industry listserv to
> spam us, since there is no "reply all" button; seeing that I was only
> replying to you made me look closer at the sub-signature bits.  This makes
> it seem like you are not interested in actual public discussion, just
> promotion.  This seems to align with the perception that AI, its users, and
> creators, are largely bad actors in a variety of ways.  I have not included
> the listserv address because maybe you just don't know how to work the
> technology and didn't intend to spam us and will rectify the issue and
> engage in actual discussion.  It's an important topic that is proceeding
> faster than policy or awareness and so far, at least in my institution,
> causes more problems than solutions.
>
> --Steve
>
> On Wed, Dec 17, 2025 at 1:00 PM Thor Martin Jensen <thormj at gmail.com>
> wrote:
>
>> Hi all,
>>
>> A growing number of startups are building museum solutions built entirely
>> on AI-generated interpretation. Visitors ask questions, and a language
>> model generates personalized explanations on the fly.
>>
>> This fundamentally changes what a museum is. Instead of encountering
>> institutional expertise, visitors receive algorithmic predictions optimized
>> for engagement. The museum’s voice, built on research and accountability,
>> gets replaced by pattern matching across training data that the institution
>> never reviewed.
>>
>> These systems cannot cite their sources because there are no sources.
>> They generate probable-sounding answers, not verified ones. When they get
>> something wrong, no one is accountable because no person actually made the
>> claim.
>>
>> A few questions I keep returning to:
>> Where does production end and interpretation begin? Translation and
>> transcription clearly help museums reach more people. But generating
>> explanations of objects based on visitor questions?
>>
>> Who is responsible when AI interpretation misleads visitors? The museum?
>> The vendor? The curator who approved the tool?
>> Should museums disclose when interpretation is AI-generated? If we hide
>> it, we break trust. If we reveal it, do visitors trust it less?
>>
>> What happens to institutional authority when knowledge becomes
>> untraceable to human expertise?
>>
>> I wrote more here:
>> https://www.linkedin.com/posts/walkietalk-ie_museums-curation-audioguides-activity-7407049070478852096-KUfB
>>
>> How are others thinking about this?
>>
>> Best,
>> Thor Martin Bærug
>> Co-founder, Walkie Talkie
>> walkietalk.ie
>>
>> *Full disclosure: I run Walkie Talkie, a multilingual audio guide
>> platform for museums. We use AI for translation and text-to-speech but keep
>> all interpretation with museum staff. I have a commercial interest in how
>> this question gets answered, but I genuinely want to hear how the field is
>> thinking about these boundaries.*
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>
>
> --
>
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> Steven M. Sullivan
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