Events

AI is shifting from models that answer a question to systems that pursue a goal. Deciding what to do, taking action, observing the result, and adjusting. Think of an assistant that researches a question across many sources, or one that debugs code by running it and reading the errors. This tutorial lays the groundwork for understanding that shift: what makes a system "agentic," how the core reason–act–observe loop works with tools and memory, and the key patterns behind most agents. We'll also cover the parts that are easy to underestimate: when you actually need an agent versus a simpler workflow, how single agents extend to multi-agent systems, and what makes agents genuinely hard to build reliably, from evaluation to cost to safety. No framework experience needed, and this is the first in a series shaped by what you want to learn next.

Graduate students from Computer Science will lead this presentation.
This session will not be recorded.

Intended Audience: K-State faculty, staff and students.

Date: Monday, October 19, 2026
Time: 11:00 am - 12:00 pm
Location: Online via Zoom

This interactive one-hour workshop helps faculty move beyond asking whether AI is allowed toward deciding how, when, and why it can support learning. Participants will examine their responses to AI, challenge common misconceptions, explore purposeful prompting, redesign one assessment, and apply ethical, transparent practices that preserve human judgment.

This session will be led by Dr. Suzanne L. Porath, College of Education, K-State.

Date: Tuesday, October 13, 2026
Time: 11:45 am - 12:45 pm
Location: Hale Library, room 359

This was in-person only and not recorded.

AI can make the task of creating a literature review much less daunting. By speeding up many basic tasks, it can give you time to focus on reading, identifying themes, and recognizing gaps in the research. And its abilities to critique, brainstorm, and debate, can help you sharpen your understanding and improve your writing.

In this session, the presenter will provide step-by-step demonstrations of how he uses a combination of completely free or freemium AI-powered tools to enhance each of the activities involved in going from the spark of an idea to a fully developed literature review. The list of tools you will see in action includes Consensus, Heuristica, Keenious, Inciteful, Latimer, Moara, Microsoft 365 Copilot Chat, SearchWhisperer, and Undermind.

Jason Coleman, Hale Library, will lead this presentation.

Intended Audience:

This session is designed for faculty and graduate students who have some experience with generative AI and want to use it for serious research. No coding required. It is the second in a series. The first session covered categories of AI tools. The next session will show how general chatbots can be turned into research agents.

Date: Wednesday, October 7, 2026
Time: 3:30 pm - 4:30 pm
Location: Online

An overview for beginners on how LLMs (Large Language Models) work under the hood. We will introduce how LLMs process prompts to responses, what hardware is needed for different quality models, and how to put a model to work. We will discuss cloud providers (Anthropic, OpenAI, etc.) and local options for self-hosted LLMs. We will also have hands-on activities to create a simple agent that actually performs tasks. By the end of the session, attendees will have a better understanding of how models work and how to make models work for you securely.

This session was in-person only and presented by Sawyer Borror, Library IT.
It was not recorded.

Date: Thursday, September 24, 2026
Time: 2:30 pm - 3:45 pm
Location: Hale Library, second floor, AI Studio

This workshop introduces participants to the fundamentals of image processing and how artificial intelligence is transforming the way visual data is analyzed, interpreted, and understood. Participants will learn how images are represented digitally, how common processing techniques such as segmentation and feature extraction work, and how AI models such as convolutional neural networks, vision transformers, and vision-language models are used for tasks like classification, detection, image generation, and visual understanding. The session will emphasize practical concepts, real-world applications, and research opportunities. This session is presented by K-State graduate students.

Intended Audience:

The university faculty, staff, and students; designed to pull back the curtain on AI with no technical background required.

Date: Wednesday, September 16, 2026
Time: 2:00 pm - 3:00 pm
Location: Online

This session was not recorded.

This is an opportunity to pose questions about AI to a panel of five people with different types of expertise. Given the scope and complexity of AI, there will doubtless be questions that stump us. However, we anticipate that we will be able to answer most of them, at least in part. Questions can be about anything, e.g., ethical issues, policies, best practices, capabilities at K-State, use cases, technical details, or how to get more extensive help.

The panelists are:

• Theresa Merrick Cassidy, Senior Instructor in English
• Jason Coleman, Professor at K-State Libraries
• Pascal Hitzler, Professor in Computer Science
• Bruce Glymour, Professor in Philosophy
• Paul Lowe, Associate Vice President for Research

Tara Coleman, Associate Dean at K-State Libraries, will moderate the session.

Intended Audience: This session is open to anyone.

Date: Monday, September 14, 2026
Time: 1:30 pm - 2:30 pm
Location: Hale Library, Room 581 (Hemisphere Room)

This session was not recorded.

Generic LLMs are great generalists, but they often fall short on tone or structure when you need them for a specific task. In this tutorial, we'll look at how to fine-tune an LLM to a specific task through additional training. We will discuss what fine-tuning actually does to a model, when it's better than just prompt engineering or other methods such as retrieval-augmented generation (RAG), and how to approach it. We'll walk through the full lifecycle, from framing the problem and preparing your data for fine-tuning, to training, evaluating, and shipping a specialized model. By the end, you'll have a solid grasp of how fine-tuning works and be ready to begin to apply it to your own tasks. Free and open to faculty, staff, and students, this session is led by K-State graduate students.

Date: Thursday, September 10, 2026
Time: 11:00 am - 12:00 pm
Location: Online

This session was not recorded.

We invite faculty and staff to a community discussion of AI in the classroom, focused on obligations to offer instruction in the proper use of AI. Many faculty have restricted the use of AI in their classes, sometimes outright forbidding any use as cheating. Others have permitted limited use for some but not other chores, and some have simply foregone any restriction on the use of AI. We will engage the questions: "Under what circumstances, if any, is it permissible to forbid the use of AI?", and "Under what circumstances, if any, must instruction be offered in the proper use of AI?" This session will be led by Dr. Bruce Glymour, Department of Philosophy, K-State.

Date: Wednesday, September 9, 2026
Time: 3:30 pm - 4:30 pm
Location: Hale Library, room 307

This was an in-person only event and was not be recorded.

Generative AI tools can expedite and enhance several of the processes involved in creating a literature review. They can also fail in ways that are easy to miss. Source summaries can misrepresent the content; syntheses can obscure important nuance; and analyses can miss key insights presented in figures. Unfortunately, the more polished the output looks, the more trust it gets. This session addresses how to use these tools effectively while being realistic about what they can and cannot do.

We'll start by mapping the current landscape, from general chatbots like Claude and ChatGPT to dedicated scholarly tools like Elicit, Consensus, Undermind, SciSpace, and Asta, to new features in traditional library databases. You'll learn how to craft prompts that leverage the affordances of semantic search. We'll also examine where these tools tend to go wrong, both in finding sources and in describing what those sources actually say. And you'll leave with concrete strategies for checking AI output.

Intended Audience:

This session is designed for faculty and graduate students who have some experience with generative AI and want to use it for serious research. No coding required. It is the first in a series. Later sessions will go hands-on with the dedicated tools and show how general chatbots can be turned into research agents.

Tuesday, Jul 14, 2026
11:00 am to Noon

This workshop introduces participants to the fundamentals of image processing and how artificial intelligence is transforming the way visual data is analyzed, interpreted, and understood. Participants will learn how images are represented digitally, how common processing techniques such as segmentation and feature extraction work, and how AI models such as convolutional neural networks, vision transformers, and vision-language models are used for tasks like classification, detection, image generation, and visual understanding. The session will emphasize practical concepts, real-world applications, and research opportunities.

Intended Audience

The broader university community; designed to pull back the curtain on AI with no technical background required.

This session was not recorded.

Tuesday, Jun 30, 2026
11:00 am to Noon

Generic LLMs are great generalists, but they often fall short on tone or structure when you need them for a specific task. In this tutorial, we'll look at how to fine-tune an LLM to a specific task through additional training. We will discuss what fine-tuning actually does to a model, when it's better than just prompt engineering or other methods such as retrieval-augmented generation (RAG), and how to approach it. We'll walk through the full lifecycle, from framing the problem and preparing your data for fine-tuning, to training, evaluating, and shipping a specialized model. By the end, you'll have a solid grasp of how fine-tuning works and be ready to begin to apply it to your own tasks. Free and open to faculty, staff, and students, this session is led by a K-State graduate student.

This session was not recorded.

Wednesday, May 27, 2026
11:00 am to Noon

AI Ethics Roundtable - a forum for discussion and Q&A about the ethics of AI use across teaching, research, and campus life, with ethicists from the Department of Philosophy. Discussants will include Dr. Bruce Glymour, whose work focuses on algorithmic bias and the philosophy of science. He is a frequent contributor to community discussions on AI and brings a thoughtful, accessible approach to a topic that can feel overwhelming. After brief opening remarks, the floor is yours. Bring the questions you've been wrestling with - about classroom policies, research integrity, bias in tools you're being asked to adopt, or anything else on your mind. Free and open to all faculty and staff.

This session was in person and not recorded.

Monday, May 11, 2026
Noon to 1:00 pm
Hale Library, room 181