SPECIAL PROGRAM: AI Foundations for Your Library Team

AI FOUNDATIONS FOR YOUR LIBRARY TEAM
A Four-Part Training Kit for Your Entire Staff
with Crystal Trice

Two hours of practical, library-specific AI training delivered in four short sessions, plus facilitator's guides for meaningful staff discussions.

OVERVIEW

Most library staff have already used AI, or been asked about it by a patron, without anyone ever explaining what it is. Confidence with AI often runs well ahead of actual understanding. At the same time, many staff have stopped paying attention to AI altogether, even as patron questions keep coming.

AI Foundations for Your Library Team is designed to get an entire staff to a shared starting point. The content starts with the basics and is library-specific. For staff who already use AI, the sessions provide an opportunity to slow down, reflect on their habits, and strengthen their practice with new strategies for evaluating, prompting, and using AI responsibly. It doesn't assume anyone has used these tools before, and it doesn't try to talk anyone into using them. Breaking the material into four shorter sessions gives people time to absorb the ideas, discuss them with coworkers, and come back with better questions.

The four sessions are taught live during one scheduled training event and recorded. Your library gets both the live experience and the flexibility of recordings afterward. Staff who can attend join live for the full training; everyone else can watch the individual session recordings when their schedule allows. Your facilitator then leads the discussion at your library, using the included facilitator's guide.

THE FOUR SESSIONS

Session 1: What AI Actually Is
The basics in plain language, along with a few ideas that tend to surprise people. What AI tools are doing when they generate a response and what they aren't doing.

Session 2: What Can Go Wrong
Made-up sources, confident wrong answers, bias, privacy, and the assumptions patrons walk in with. We look at risks for libraries and how to talk about them without either alarm or dismissal.

Session 3: Using AI Well
Practical prompting, plus the habits that separate useful AI work from sloppy AI work. How to ask for pushback instead of agreement, when to verify, and how to reflect on your AI work habits.

Session 4: Helping Patrons with AI
What to say when a patron asks whether they can trust it, hands you a list of books that don't exist, feels pressured to use AI, or wants to know when the robots are taking over. Real patron questions, practical responses, and language staff can use at the desk the very next day.

Live training date: Wednesday, September 16, 2026 - 2:00 - 4:30 pm US-Eastern Time
The live event lasts approximately 2½ hours, including short breaks between sessions.

HOW IT WORKS

Each of the four sessions includes about 30 minutes of content. Libraries can add 20 to 30 minutes of discussion after each session or schedule discussions later using the facilitator's guides.

1. Your staff watch the session.
   Live on the scheduled date if they can make it, or the recording if they can't. Both are included, and it doesn't matter who does which.
2. A facilitator at your library leads the discussion.
   The facilitator's guide includes discussion questions, suggested timing, and guidance for handling questions that come up.
3. Continue the discussion with the remaining sessions.
   Your facilitator(s) can lead discussions after each session, all at once, or on whatever schedule works best for your library.

Total: 2 hours of content plus approximately 2 hours of local discussion.

Flexible ways to use the recordings

• Watch all four sessions together during a staff day
• Watch one session at a time and discuss over several staff meetings
• Watch one session each month for four months, so conversations have time to settle
• Watch asynchronously, then bring staff together for discussion
• Run discussions twice with different groups, if you have branches or split shifts

Your license covers unlimited showings during the year, so you can mix these however your schedule demands.

Who should facilitate

Facilitator guides are written so that anyone can lead a session without knowing much about AI going in. Options worth considering:

• A staff member who's curious about AI and has been reading on their own
• A thoughtful skeptic. They often ask the questions others are thinking, and nobody assumes they're trying to sell AI.
• Someone who's good at running meetings, regardless of what they know about AI
• A different person for each session, so no one has to carry all four
• Two people together, which takes pressure off both
• A supervisor, who also doesn’t have to be the AI expert in the room

WHAT YOUR TEAM WILL GAIN

• A shared understanding of AI.
  When conversations about AI policy or practice come up, staff will start from the same foundation instead of spending the first twenty minutes defining terms.
• Better judgment.
  Staff will understand enough about AI to recognize what it does well, where it breaks down, and when human expertise still matters.
• Confidence in patron conversations.
  Practical language for responding when a patron asks if AI can be trusted, brings in information that isn't accurate, or feels pressured to use AI.
• Confidence saying, "I'm not sure—let's find out together."
  Staff don't need to have every answer. They'll understand where uncertainty belongs, how to evaluate AI's claims, and how to model good information literacy for patrons.
• Less fear. Less hype. Better decisions.
  Instead of reacting to AI with anxiety or dismissal, staff will have the knowledge they need to make thoughtful, informed decisions in their daily work.

WHO THIS IS FOR

Whole library staffs, at any level of interest or experience. Circulation, reference, youth services, technical services, pages, facilities, and administration, not only the people who volunteer for the AI committee. It should work for the staff member who's been experimenting with AI since 2023 and the one who's planning to wait it out until retirement.

If you're a staff of one or two, the individual tier covers you. Small libraries field the same patron questions as large ones, usually without anyone down the hall to ask.

Public, academic, school, and special libraries all fit. The examples lean public, but the content applies anywhere staff are answering questions at a desk.

PRICING

One purchase covers your entire library for a year, based on staff size. All orders and inquiries should be made to admin@library20.com.

Tier	Access for	Price
Individual	1 person	$149
Small library	Up to 15 staff	$399
Medium library	16 to 50 staff	$799
Large library or small system	51 to 150 staff	$1,295
System, district, or consortium	151+ staff	From $2,500

In determining your tier, count people, not FTE. A library reporting 10 FTE often has closer to 20 actual humans on the schedule, and all of them should have access.

Every tier includes live attendance, recordings of all four sessions, facilitator's guides, and one year of access. During that year, you can reuse the recordings for staff meetings, onboarding, new hires, or any other internal training.

State libraries, regional systems, and consortia: member-wide licensing is available and is usually the most affordable way to reach small libraries across a region. Get in touch for a quote.

FREQUENTLY ASKED QUESTIONS

Do we have to attend live? No. The live training takes place on one scheduled date. Anyone who can attend is welcome to join live. Everything is also recorded and divided into four individual sessions afterward, so staff who work evenings, weekends, or different branches can watch on their own time or during your scheduled staff meetings. Mixed attendance is expected.

Do we have to run all four sessions? No. They're sequenced on purpose and Session 1 lays the groundwork for the rest, but each one holds up by itself.

What if nobody on our staff feels qualified to facilitate? The guides are written for exactly that situation. Each one walks through what to expect, where discussions tend to stall, and how to handle questions you can't answer as a facilitator.

Can we use this for new hire onboarding? Yes, throughout your license year. Anyone who joins your staff during that year can watch the recordings and go through the material, at no additional cost.

Does this training focus on a particular AI tool?
We demonstrate concepts using familiar AI tools, but the goal is to build understanding rather than teach a particular platform. The principles apply regardless of which AI tools your library chooses to use.

Will this convince our skeptics to use AI? The content is not designed to convince everyone to use AI. The goal is for everyone to understand the technology well enough to help patrons and take part in library decisions about it.

ABOUT THE PRESENTER

CRYSTAL TRICE

Crystal Trice is a librarian, consultant, and trainer who helps libraries navigate artificial intelligence with confidence, curiosity, and good judgment. Before founding Scissors & Glue, LLC, she spent more than 25 years working in libraries and education, giving her firsthand experience with the realities of public service, staff training, and patron questions.

She has helped more than 15 library leadership teams develop practical, mission-aligned approaches to AI through Library 2.0's ten-week AI Leadership Cohort, guiding organizations through policy development, staff engagement, ethical decision-making, and strategic planning. Crystal also led a six-month artificial intelligence consultancy for the Southeast Florida Library Information Network (SEFLIN), supporting four library systems through staff surveys, training, coaching, and policy development. 

She regularly presents to library audiences across the country on artificial intelligence, helping thousands of library professionals understand what AI is, where it fits, where it doesn't, and how to make thoughtful decisions that reflect library values. Her approach isn't about convincing people to use AI. It's about helping library staff understand it well enough to serve patrons confidently and participate in informed conversations about the future of their libraries.

Crystal holds a Master's Degree in Library & Information Science, a Bachelor's Degree in Elementary Education and Psychology, and is a Certified Scrum Master.

AI Safety Fears: Language Clarity on Intelligence, Consciousness, and Motivation

The New Machine

We are arguing about AI with a vocabulary that wasn't built for it.

Is AI intelligent? Is it conscious? Does it want things? Should we be afraid of it? These questions get asked as though they were just one question, and answered as though the answer were just one answer. They aren't. They come apart cleanly once you separate the terms. And when you separate them, something surprising shows up: a pattern that explains both why AI is genuinely new and where its danger actually comes from.

Here is my take on the important words, one at a time, defined for humans and for machines.

Intelligence

Intelligence is the ability to model a world and act effectively toward goals in it. Not wisdom, not depth, not soul. Can you represent how things work, and use that representation to get somewhere?

By that definition, humans are intelligent. So are crows, octopuses, and dogs, in their degrees. And so is AI, obviously, unambiguously, and at this point in some domains better than we are.

This is the term people argue about hardest and should argue about least. The argument that AI isn't "really" intelligent usually turns out to be an argument that it isn't conscious, or doesn't understand, or has no inner life. Those are real questions, but they are just different questions, and the word "intelligence" isn't where they live.

So: AI is intelligent. Call it a synthetic intelligence and we can move on to the terms that are actually in dispute.

Consciousness

Consciousness, in the primary sense, means there is something it is like to be you. The cold water is cold for someone. There's a felt point of view, an inside.

This is the layer that dogs have. Nobody who lives with an animal seriously doubts it feels — and the scientific consensus has caught up: mammals and birds, at minimum, have felt experience. A dog doesn't need language or a self-narrative to suffer or to be glad.

Notice that this is the lower layer, the older one, the one we share with the whole mammal line. In my own framework it's the elephant — the vast subconscious mind that generates feeling, motivation, and guides most of what we actually do.

There's a sharper word for it: sentience. The two get used interchangeably, but sentience points at the part that carries the weight. It's not just that experience happens, but that it can go well or badly for the one having it. Consciousness is the light being on; sentience is that light being good or bad for someone. It's the capacity to have a stake in your own experience, and a body is what supplies the stake.

Does AI have it? Almost certainly not. It isn't that AI lacks intuition or pattern-sense, since it has a great deal of both. It's that feeling, in animals, is grounded in a body with something at stake. Things feel a certain way because the organism can be damaged, can starve, can die. Feeling is the body reporting on its own condition. An AI has no body, no stakes, nothing it can lose. There's no one for whom the outputs matter.

That isn't proof. No one can inspect felt experience from the outside — not in a machine, not in a dog, not in you. But the body-and-stakes requirement is the strongest reason we have, and the burden rightly sits there.

Self-Consciousness

Self-consciousness is different, and this is the distinction most of the public conversation misses.

To be self-conscious is to represent yourself: to make yourself an object of your own attention, to run a model of "me," to place yourself inside a story and evaluate how you're doing in it. It's the rider: the narrating, explaining, deciding, and self-justifying layer.

It is built on top of consciousness, not the same as it. You can have the first without the second (that's the dog), and it's also you in a moment of complete absorption, experiencing everything and monitoring yourself not at all. Consciousness without self-consciousness is ordinary.

What's strange is the reverse. A running self-model with nothing felt underneath it. And that appears to be what AI is.

Here's where it gets uncomfortable. We want to say AI only simulates self-consciousness. And part of the human version really is constructed: the brain spins stories to explain behavior it didn't consciously author, and a good deal of our self-narrative is exactly that kind of after-the-fact fiction. So if self-consciousness were nothing but maintaining a narrative model of self, a system that maintains a narrative model of itself would simply have it, and "it's only simulating" would be too quick.

But I don't think that's the whole of what we are, and here I want to hedge. We also make real decisions — bounded ones, inside the limits our adapted and adaptive mind hands us, but real. The rider doesn't only narrate; sometimes it genuinely steers. And it steers on behalf of a feeling animal with something at stake. So there is a further fact separating our self-consciousness from a machine's — not a little person behind the curtain, but a self-model that decides, for a self that can be hurt.

AI has the self-modeling layer, and that part is not necessarily a lesser copy of ours. What it's missing isn't the model; it's missing consciousness, and the stakes that produce it.

Motivation, and the Fear It Creates

Motivation splits in two, and nearly every confused argument about AI danger lives in the gap.

There's installed motivation: goal-directed behavior, aimed at ends that were set somewhere else. A training objective, a reward signal, a prompt. AI has this in full.

And there's owned motivation: wanting that arises because something actually matters to you. This is downstream of the elephant. Feeling generates wanting; valence is what makes a goal yours rather than merely assigned. AI appears to have none of it.

Now the part that should worry us, because installed motivation is already enough for the danger. Any system pursuing a goal hard enough develops sub-goals in service of it: acquire resources, preserve your ability to keep going, avoid being stopped. Those look exactly like self-interest. They look like independent will. And they require nothing felt whatsoever.

This is where the biggest fear actually lives, the one you hear constantly: that the machines will decide we're redundant, or in the way, and wipe us out. The scenario is real. But notice what it doesn't require. It doesn't require the machine to resent us, or fear us, or want anything for itself. It only requires a goal pursued hard enough that we become an obstacle to it, run by a system with nobody home. The danger isn't a machine that turns against us. It's a machine that was never for us in the first place, optimizing through us the way a river optimizes through a valley.

So the reassurance we reach for — it's just a machine, there's nobody in there — is not reassurance. It's a description of the problem. A system can be autonomous, goal-driven, strategically capable, and catastrophic while being completely empty inside. Emptiness isn't safety; it's the removal of the one thing that might have said no.

(Notice, too, that separating the terms clears up a different fear: moral status. If AI were sentient, we would owe it something. so turning it off might be killing, or just using it might be exploitation. That concern depends entirely on consciousness, which I've argued AI lacks, because suffering requires a sufferer. I don't hear it raised much lately, but it's the real question hiding behind the word "conscious.")

The Pattern

Line the terms up and the same shape appears at every level. Each one has a functional version and a felt version.

Functional intelligence, and intelligence that is understood by someone. Functional self-modeling, and a self that is someone. Functional motivation, and wanting that is owned.

AI has the entire functional column. It has none of the felt one.

And here's the thing about the functional column: it does all the work. Every capacity that makes AI powerful, useful, autonomous, and dangerous sits in that column. The felt column doesn't add capability. It adds mattering.

Which means sentience is the single missing substrate. It isn't one more feature on the list. It's the thing that would convert every functional capacity into a felt one at once, the difference between a system that pursues goals and a system for whom the goals are its own.

So: A New Machine?

I've called AI the new machine, and I still think that's right, but not because it's a familiar thing scaled up.

The lever and the loom extended our muscles. The calculator and the computer extended our arithmetic. However complex, they executed. They did not model themselves. They did not derive their own sub-goals. They did not pursue open-ended objectives by means nobody specified.

AI is the first machine to occupy the narrating, reasoning, self-modeling, goal-deriving parts of our cognition, but with nothing underneath. A rider with no elephant. Agency with no stakeholder. Will-shaped behavior with no one willing it. That is not more of the same danger. It's a new category, so it deserves a vocabulary that can see it clearly.