New Workshop: "AI for Archiving"

AI for Archiving
A Library 2.0 / Learning Revolution Workshop with Reed Hepler

OVERVIEW

Archives and special collections face unprecedented challenges and opportunities as artificial intelligence transforms how we preserve, process, and provide access to historical materials. This workshop addresses the practical realities archivists and special collections librarians encounter when integrating AI tools into their workflows—from appraisal and accessioning to metadata creation and public engagement. Participants will examine how AI can support archival functions without compromising the fundamental principles of provenance, authenticity, and contextual integrity that define professional archival practice. The session will explore specific use cases where AI collaboration enhances efficiency and access while maintaining the critical human judgment necessary for ethical stewardship of cultural heritage materials.

The workshop emphasizes objective-centered practices that archivists can implement immediately in their institutions. Participants will learn how to use AI tools to generate preliminary finding aids, create descriptive metadata, analyze collection strengths for appraisal decisions, and develop interpretive materials for exhibits and digital collections. Each application will be examined through the lens of archival ethics and professional standards, ensuring that AI serves as a collaborative tool rather than an autonomous decision-maker.

The session will address concerns about AI-generated hallucinations in historical contexts and demonstrate verification strategies to protect against the distortion of historical narratives. Participants will also explore how AI can support patron services, including enhanced discovery systems and transcription projects, while maintaining the archivist's role as mediator between researchers and primary sources.

By the conclusion of this workshop, participants will possess a framework for evaluating AI feasibility in archival contexts and a toolkit of specific applications they can adapt to their institutional needs. Attendees will leave with conversation templates for common archival tasks, a decision matrix for determining when AI collaboration is appropriate, and strategies for maintaining human-centered practices in AI-assisted workflows. The workshop will also address how archivists can teach patrons to critically evaluate AI-generated historical information, positioning archives professionals as essential guides in an era when historical narratives are increasingly mediated by algorithmic systems. Participants will understand how to leverage AI capabilities while preserving the intellectual work that distinguishes professional archival practice from mere digitization.

LEARNING OBJECTIVES: 

• Evaluate the appropriateness of AI tools for specific archival functions (appraisal, arrangement, description, preservation, access) using professional standards and ethical frameworks
• Apply AI collaboration techniques to create metadata records, finding aids, and interpretive materials while maintaining archival principles of provenance and original order
• Implement verification protocols that protect against AI hallucinations and ensure the accuracy of AI-assisted historical research and description
• Design patron-facing services and instruction that help researchers critically evaluate AI-generated historical information and understand the irreplaceable role of archival expertise

The recording and presentation slides will be available to all who register. 

DATE: Tuesday, June 2nd, 2026, 2:00 - 3:30 pm US - Eastern Time

COST:

• $129/person - includes live attendance and any-time access to the recording and the presentation slides and receiving a participation certificate. To arrange group discounts (see below), to submit a purchase order, or for any registration difficulties or questions, email admin@library20.com.

TO REGISTER: 

Click HERE to register and pay. You can pay by credit card. You will receive an email within a day with information on how to attend the webinar live and how you can access the permanent webinar recording. If you are paying for someone else to attend, you'll be prompted to send an email to admin@library20.com with the name and email address of the actual attendee.

If you need to be invoiced or pay by check, if you have any trouble registering for a webinar, or if you have any questions, please email admin@library20.com.

NOTE: Please check your spam folder if you don't receive your confirmation email within a day.

SPECIAL GROUP RATES (email admin@library20.com to arrange):

• Multiple individual log-ins and access from the same organization paid together: $99 each for 3+ registrations, $75 each for 5+ registrations. Unlimited and non-expiring access for those log-ins.
• The ability to show the webinar (live or recorded) to a group located in the same physical location or in the same virtual meeting from one log-in: $399.
• Large-scale institutional access for viewing with individual login capability: $599 (hosted either at Learning Revolution or in Niche Academy). Unlimited and non-expiring access for those log-ins.

REED C. HEPLER

Reed Hepler is a digital initiatives librarian, instructional designer, copyright agent, artificial intelligence practitioner and consultant, and PhD student at Idaho State University. He earned a Master's Degree in Instructional Design and Educational Technology from Idaho State University in 2025. In 2022, he obtained a Master’s Degree in Library and Information Science, with emphases in Archives Management and Digital Curation from Indiana University. He has worked at nonprofits, corporations, and educational institutions encouraging information literacy and effective education. Combining all of these degrees and experiences, Reed strives to promote ethical librarianship and educational initiatives.

Currently, Reed works as a Digital Initiatives Librarian at a college in Idaho and also has his own consulting firm, heplerconsulting.com. His views and projects can be seen on his LinkedIn page or his blog, CollaborAItion, on Substack. Contact him at reed.hepler@gmail.com for more information.

 

MORE UPCOMING EVENTS:

 

 May 19, 2026

 May 22, 2026

 May 29, 2026

Cognitive Sharpening, or Thinking in Conversation with AI

I've known something about myself for a long time: I tend to think better in conversation than I do alone. Not always, and not for everything. But often, and especially when the thinking matters, I benefit from another mind to think out loud with.

One of my favorite quotes is, "How will I know what I'm thinking until I hear myself say it?"

For example, I read an article or have an idea, and I notice that something in it bothers me, but I can't yet say what. The reaction is real before it's articulate. The work of articulating it is the work of finding out what I actually think--which seems weirdly backward, but happens to me a fair amount. When I'm trying to surface what my mind is actually responding to in something I've read or heard, talking out loud helps me to get there. 

This process is super interesting when the conversation partner is an AI. When I work through an idea with an AI, the LLM brings something unique: the accumulated articulations of everyone who has ever thought about anything adjacent to what I'm thinking now. The conceptual vocabulary. The cross-references. While a human conversational partner usually serves as a sounding board, offering feedback or even pushback, an AI partner offers articulation. 

An example

Last week, I was reading an article about Oklahoma's permanent ban on cell phones in schools, and I had one of those "why does this feel wrong" reactions: I agreed with the outcome for students, but was bothered by the legislation in a way I couldn't fully articulate. So I did what I now do more frequently: I started a conversation with Claude.

I didn't ask for a position, an essay, or an argument. I just started typing my thoughts as I had them: that the bill seemed to presume public schooling was an unquestioned good, that state legislation was replacing parental authority, and that state legislation was also replacing local authority. I noted that it bothered me at layers, and I was trying to organize my thinking.

What came back was a list of layers that named what I was sensing, including some I hadn't fully formed, like the iatrogenic loop in which institutions create the problems they then propose to solve. The conversation then surfaced a sentence I could build on: agreeing with an outcome is not the same as endorsing the mechanism that produced it. I rewrote it, and the AI flagged words that softened the move. I rewrote it again, adding a clause about the institution's role in creating the problem in the first place. By the third pass, I had a couple of sentences that compressed the entire argument into one line:

Agreeing with an outcome is not the same as endorsing the mechanism that produced it. And the agreement disinvites scrutiny of the institutions themselves, of their role in actually creating the problems in the first place, and of the assumptions that have inverted the natural priority of decision-making over children.

Total elapsed time: maybe twenty minutes. Most of that was me deciding, refining, and selecting--or, to be clear, thinking. The kind of thinking that I like to do. The AI didn't write the sentence. I did it with its help. But I almost certainly wouldn't have arrived at that compressed, cleanly articulated structure if I'd been thinking alone.

The mode

This is the experience I want to name, because the prevailing public conversation about AI doesn't yet have language for it. I get that it seems like a shortcut, but we wouldn't call it that if we were talking to a human. And this is like talking to a really well-read, articulate human.

The two failure modes everyone talks about with regard to using LLMs are real, and I've written about both. Cognitive offloading is when we hand a tool a task that requires no thinking and gain back the time (like using a calculator for arithmetic or a GPS for navigation). The trade-off is not without costs (mathematical capability or directional orientation), but generally seen as worth it. Cognitive surrender is when we hand a tool a task that should better be our thinking, and then accept whatever it returns as our own. The trade-off there is potentially catastrophic; the loss is the thinking itself. A student who asks an AI to write the essay has surrendered the thinking that the essay was supposed to produce in them.

But I'm identifying a third mode that the discourse doesn't really touch yet, one that affects most for people who think carefully as a part of their job or way of being. I'm proposing to call it cognitive sharpening. 

Cognitive sharpening is neither offloading nor surrender. The thinking remains ours; the AI is the partner that helps us find what we are starting to articulate and want to pursue more fully. We bring the seed thought, the felt reaction, or the editorial judgment. The AI brings the conceptual range, the fast articulation, and the cross-domain retrieval. Like a good conversation with a human, we are involved in a back-and-forth refining process. The output is ours because the thinking was ours, but it arrives sooner, sharper, and more precise than it would have if we had been doing the thinking alone.

What makes this mode newly possible isn't AI as a writing tool. It's AI as a thinking partner available at speed and detail. Conversation has always been generative for thinkers. What often goes missing isn't the value of conversational thinking; it is a partner who is always present who can keep up with every thread, retrieve the relevant articulation in seconds, and incur no social cost for half-formed thoughts. Language was already abundant before LLMs. What just became abundant is a kind of cognitive companionship. Sharpening is the mode that abundance enables.

With cognitive sharpening rather than cognitive surrender, the editorial authority never leaves us. In the conversation I just described, the AI offered several layers, and I picked one as the through-line. The AI offered two sentence variants, and I drew on elements from both. The AI noted that the word 'inverted' was stronger than 'reversed,' which I had already kept for the same reason. At every junction, I was the one deciding what cohered with what I was actually trying to say. It didn't feel like surrender but like sharpening.

Three things

The first is that cognitive sharpening can really only reward people who already know how to think and don't want to lose that. It's not a substitute but an accelerant. A person who doesn't want to think won't be sharpened; they'll be replaced by it, or at least produce work indistinguishable from what the AI alone would have produced. The seed has to be ours. The selecting has to be ours. The voice has to be ours. The AI cannot supply any of those things, and the moment a person tries to make it supply them, the mode flips from sharpening to surrender.

The second is that this mode probably rewards a particular kind of thinker — the verbal thinker, the conversational thinker, the person who works ideas out by talking them through. I've always been one of those. My entire workflow is mobile and dictated; I listen to my own drafts read aloud as part of editing; I refine ideas by saying them out loud. For me, AI conversation is a native fit because it's an extension of how I already think. For a person whose native mode is solitary writing in a notebook, AI conversation might feel like an interruption rather than an amplification. I suspect the effect on cognition isn't uniform and depends on what kind of cognition you were already doing.

The third is that most public discourse oscillates between two poles: AI will replace human thinking (the surrender frame), or AI is just another tool (the offloading frame). Neither captures AI as a dialogic partner.

Naming

I don't think this mode is for everyone, and I don't think it should be. I do think it deserves a name, because what it makes possible for some of us is among the most significant cognitive shifts in human history. The conversation about AI should not present the only choices as surrender or indifference.

The thinking is still mine, and the conversation sharpens it.