Faculty/Staff Resources for AI

Artificial Intelligence (AI) is rapidly transforming higher education, impacting everything from the classroom to administrative operations. While AI offers significant opportunities across teaching, research, and administration, �� affirms that human agency must remain central. AI tools are intended to augment—not replace—the judgment, creativity, and output of students, faculty, and staff.

We strive to provide resources for responsible, ethical, and effective integration of AI at the university.

Training

CTLL AI Workshops

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Teaching Conversations: Approaching Alternative Assessments in the World of AI
February 4 (Wednesday)
Noon — 1:00 PM

Approaching Alternative Assessments in the World of AI
Led by Ralph Hale and Susan Brantley
Zoom
In this session, we will explore strategies to create assessments and evaluate students to ensure they are genuinely engaging with and understanding the material, rather than relying on AI to complete their work.

Website
AI Faculty Fellows Series: Using NotebookLM to Support Teaching and Learning in College Classrooms
February 6 (Friday)
Noon — 1:00 PM

Using NotebookLM to Support Teaching and Learning in College Classrooms
Led by Ralph Hale and Jennifer Sears, AI Faculty Fellows
This presentation will demonstrate how NotebookLM, an AI-powered tool, can be applied in higher education to provide student resources, assist with course development, and strengthen classroom pedagogy. We will also discuss how artificial intelligence can reduce faculty workload on increasingly complex tasks while promoting student success.
AI² - Academic Integrity & Artificial Intelligence
February 12 (Thursday)
12:30 PM — 1:30 PM

This workshop will be offered at two different times:
Thursday, February 12, 2026 | 12:30-1:30 p.m. OR
Friday, February 13, 2026 | 12-1 p.m.
Zoom

AI² - Academic Integrity & Artificial Intelligence
Led by: Richelle Keilholz, Assistant Dean of Students, Student Conduct and Integrity; and Caitlin Blank, Coordinator, Student Conduct
This program is designed to explore the academic integrity issues (including artificial intelligence) facing higher education. Join Dr. Richelle Keilholz and Caitlin Blank, from the Office of Student Integrity, to learn more about trends, prevention techniques, and much more! We will also have plenty of time for Q&A!
AI² - Academic Integrity & Artificial Intelligence
February 13 (Friday)
Noon — 1:00 PM

This workshop will be offered at two different times:

Friday, February 13, 2026
12-1 p.m.

Zoom

AI² - Academic Integrity & Artificial��Intelligence
Led by: Richelle Keilholz, Assistant Dean of Students, Student Conduct and Integrity; and Caitlin Blank, Coordinator, Student Conduct
This program is designed to explore the academic integrity issues (including artificial��intelligence) facing higher education. Join Dr. Richelle Keilholz and Caitlin Blank, from the Office of Student Integrity, to learn more about trends, prevention techniques, and much more! We will also have plenty of time for Q&A!
AI Awareness, Critical AI Literacy, and AI Fluency: Foregrounding Agency
February 17 (Tuesday)
12:30 PM — 1:30 PM

AI Awareness, Critical AI Literacy, and AI Fluency: Foregrounding Agency
Led by Members of the English Department’s Writing Across the Curriculum (WAC) Network��
In this session, we will look at three frameworks for addressing generative AI in classroom spaces: awareness, critical literacy, and fluency. These frameworks reject binaries such as boomer/doomer or embrace/resist when it comes to generative AI. We will highlight the questions and assumptions underlying each approach and introduce resources and writers who have adopted them. Finally, we’ll examine how each approach supports both teachers and students as agents whose scholarly labor should be valued.
Website
Fact vs. Fiction: Dispelling the Most Common Myths About AI in Higher Education
March 5 (Thursday)
12:30 PM — 1:30 PM

Fact vs. Fiction: Dispelling the Most Common Myths About AI in Higher Education
Led by Lindsay Linsky��
This session will unravel common misconceptions surrounding Artificial Intelligence (AI) in academia. Topics will include AI's role in the higher ed workplace, its impact on student learning, and its ethical implications. Equipped with real-world examples, participants will gain a clearer understanding of how AI can enhance educational experiences, promote innovation, and foster student success.��
Website
Leadership in Focus: AI, Leadership, and the Quest for Balance
March 16 (Monday)
Noon — 1:00 PM

Topic: AI, Leadership, and the Quest for Balance: What Your Faculty and Students Need to Know
Speaker: Bryson Payne

Are you ready to lead in the era of artificial intelligence? Don’t just adapt to the future – shape it! This workshop is designed to provide you with insights and strategies to navigate the transformative impact of AI on higher education.
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Website
AI Faculty Fellows Series: Exploring the AI Assessment Score (AIAS)
March 20 (Friday)
Noon — 1:00 PM

Exploring the AI Assessment Score (AIAS)
Led by Jane Morris
A flexible framework for incorporating AI technologies into educational assessment. Designed to support educators in selecting the appropriate level of AI use by students.
The Mirage of AI Ethical Use
April 24 (Friday)
Noon — 1:00 PM

The Mirage of AI Ethical Use
Led by Yacine Kout
Since the release of ChatGPT in 2022, the idea of "AI ethical use" has been employed to sensibilize students to various problems tied to AI in academia. The goal is to inform them and acquaint them with responsible AI use. This presentation shows that the idea of “AI ethical use” is a mirage and the question of using AI responsibly is nonsensical. The goal of this presentation is to broaden our collective understanding of issues tied to AI, to move the needle from AI as an individual and individualistic tool, and to reconnect discussions about AI to the democratic goals of higher education.
Website
AI Faculty Fellows Series: An Exploration of AI Applications on College Education
April 30 (Thursday)
12:30 PM — 1:30 PM

An Exploration of AI Applications on College Education
Led by Wenxin Guo and Mohan Menon
In this exploration, we will study: 1) How AI could help educators to enhance their teaching efficiency in the classroom; 2) How AI could play the role of a tutor when students need help outside of the classroom; 3) How AI could support educational planning for college institutions.

Additional training

Resources & FAQ

AI Terms to Know
Aritificial Intelligence (AI): At a fundamental level, AI systems work by processing massive amounts of data through complex algorithms (rules) and specialized models to achieve goals like:

Learning from patterns (Machine Learning).

Problem-solving and decision-making.

Understanding and generating human language (Natural Language Processing).

Recognizing images and complex data structures.

Agentic AI: A type of artificial intelligence capable of acting on behalf of a user or function to complete tasks or achieve goals. These systems, known as AI agents, often operate with autonomy and limited human intervention, generating responses, making decisions, or interacting with other software tools based on user prompts, instructions, or learned behavior.

Bias: Refers to flaws in the data used to train AI, or a flaw in the design of the algorithm itself, that can amplify existing societal biases.

Chatbot: An AI tool designed to simulate human conversation.

Deep Learning (DL): A method of training an AI system that processes data in a way inspired by the human brain. DL models are typically used to recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions.

Generative AI (GenAI): A type of artificial intelligence that can create new content (e.g., text, images, audio, or computer code), rather than just analyzing or classifying existing data. The generated output is highly dependent on the training data. A key drawback is that when untrained on a topic, it may create fictional content (hallucinate) or if trained on biased data, the results will also be biased.

Hallucinations: A condition where a Large Language Model (LLM) generates prompt results on items it was not trained on, often creating nonsensical or inaccurate outputs.

Human Oversight: In the context of AI, this ensures that human beings maintain appropriate control, accountability, and ethical responsibility over the design, development, deployment, and operation of Artificial Intelligence systems.

Large Language Model (LLM): A computational model recognized for its ability to achieve general-purpose language generation and other natural language processing tasks, such as classification.

Machine Learning (ML): A branch of AI and computer science that focuses on using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy.

Prompt Injection: A specialized type of cyber-attack against LLMs, where malicious inputs are disguised as legitimate ones, resulting in the return of erroneous results or the leaking of sensitive information.

Training Data: The data used for the purpose of training AI tools to make decisions, make predictions, or generate content.

Privacy and Security of Personal Information
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