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, ��Ӱҵ 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

Artificial Intelligence Academy

Navigate the future of higher ed with CTLL's Artificial Intelligence Academy. Gain practical strategies to integrate AI into your course design, balance ethics and academic integrity, and walk away with a ready-to-implement action plan for student success.

AI Academy Information

CTLL AI Workshops

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Why Bother? Writing Lab Reports: Strategies for Combatting AI-driven Futility
October 13 (Tuesday)
12:30 PM — 1:30 PM

Led by the Writing Across the Curriculum Network

In this session, members of the WAC Network partner with colleagues in Psychology to look at why we write lab reports, ways we can teach and assess the report genre, and how to help students value the research and writing processes they are practicing when drafting lab reports.
Using AI to Your Advantage: Planning and Updating Course Materials
October 22 (Thursday)
12:30 PM — 1:30 PM

Led by Jennifer Sears
This session focuses on ways to utilize AI tools - specifically NotebookLM - to prepare for future courses or to update existing materials.

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AI in the Classroom: Practical Strategies for Any Faculty
November 5 (Thursday)
1:30 PM — 2:30 PM

Led by Jane Morris
This session introduces faculty to practical, beginner-friendly strategies for using artificial intelligence (AI) in the classroom. Participants will explore how AI can support teaching, enhance student learning, and be integrated into assignments across disciplines, with a focus on simple approaches that can be implemented immediately.��
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AI Series: Moving Beyond the Five-Paragraph Essay
November 19 (Thursday)
12:30 PM — 1:30 PM

Led by Wenxin Guo and Mohan Menon��
Restructuring assignments toward multi-modal outputs (podcasts, video pitches, interactive portfolios) or scaffolding the writing process (grading the brainstorm, the outline, the AI-assisted draft, and the final reflection).

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Three Simple Ways to Start Using AI This Semester
February 4 (Thursday)
12:30 PM — 1:30 PM

Led by Jane Morris
This session introduces faculty to three simple, practical ways to begin using artificial intelligence (AI) in their courses right away. It focuses on low-effort strategies to support teaching, enhance student engagement, and explore AI tools with confidence.��
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AI Series: Redesign One Assignment
February 25 (Thursday)
12:30 PM — 1:30 PM

Led by Chuck Robertson
In this session we will use Stanford's Practical Examples, Mollick's Seven Ways, and Miller's What Not To Do to explore effective approaches for integrating AI into teaching and learning. The goal is for every faculty member to leave with at least one revised assignment and a clear AI-use statement they can implement in their course.��
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Leveraging AI: Practical Uses for Departmental Leadership
March 15 (Monday)
Noon — 1:00 PM

Led by Mohan Menon

Explore practical ways artificial intelligence can support the work of department chairs and academic leaders. This session highlights tools and strategies for improving efficiency, communication, and administrative workflows.

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Teach Smarter with MagicSchool AI: Practical Tools for Faculty Success
March 25 (Thursday)
12:30 PM — 1:30 PM

Led by Jennifer Sears and Kellie Whelan-Kim
Discover how MagicSchool AI can help streamline course preparation and enhance teaching effectiveness. In this hands-on session, faculty will explore practical tools for creating instructional materials, developing assessments, generating engaging learning activities, and improving course communication. Participants will leave with ready-to-use strategies for integrating AI into their workflow while promoting responsible and effective use of AI in higher education.
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AI Series: From Chatbots to Scientific Discovery
April 8 (Thursday)
12:30 PM — 1:30 PM

Led by Yong Wei
This talk introduces the basic ideas behind modern AI, from large language models such as ChatGPT to AI systems that can analyze images, molecules, and materials. Using accessible examples, I will explain how AI learns patterns from data and how similar techniques are helping scientists accelerate discovery in areas such as materials design, and biomedical research.��
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Teaching Students to Create Their Own Study Resources with AI
April 21 (Wednesday)
Noon — 1:00 PM

Led by Lori Furbush
This workshop explores how educators can teach students to use AI to create their own study resources. The session will highlight practical ways students can generate summaries, study guides, review questions, and other materials that support deeper learning, stronger retention, and more independent study habits. The workshop leader will also share personal experiences and preliminary study data on the effectiveness of these approaches.

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The "Bring Your Own A.I." Exam
April 29 (Thursday)
12:30 PM — 1:30 PM

The "Bring Your Own AI" Exam
Led by Wenxin Guo and Mohan Menon
Explore assignments where students must use AI to generate a solution, but where 70% of their grade is based on their critique and verification of the AI's output, including tracking down hallucinations, evaluating accuracy, and improving the prompts used to generate the response.
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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.

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