This two-part resource is designed to support instructors and students as they navigate the presence of generative AI tools, specifically Large Language Models (LLMs) such as ChatGPT, in the rhetoric and composition classroom. Part I of this resource offers an instructor-focused introduction to what LLMs are and how they operate, as well as an in-depth exploration of the privacy concerns and ethical considerations related to using a tool like ChatGPT. Additionally, Part I provides insights on the practical application of LLMs within the realm of reading and writing in the rhetoric and composition classroom, while promoting a modified stasis theory as a strategy for evaluating any generated output.

Part II of this resource offers student-focused tutorials that demonstrate how ChatGPT can augment the writing process for assignments commonly given in a rhetoric and composition course. These tutorials cover the evaluation essay, rhetorical analysis, Rogerian argument, annotated bibliography, and research essay—all while promoting the responsible and ethical use of AI in writing and research. With this comprehensive resource, instructors and students can not only build confidence in their understanding of generative AI within academia, but also build digital literacy that will serve them in the world beyond.

This lesson centers around the How AI Works: Creativity and Imagination? video from the How AI Works video series. Watch this video first before exploring the lesson plan.

Diffusion models generate images. Diffusion AI converts an image to noise, and trains an AI to reverse the process. In this lesson, students learn how AI can generate images, then explore a diffusion AI widget. Finally, the class wraps up with a discussion about whether or not these models are creative.

This lesson can be taught on its own, or as part of a 7-lesson sequence on How AI Works. Duration: 45 minutes

A module for the course: CS 59974: Special Topics in Artificial Intelligence - "Machine Learning in Action". Delivered at the City College of New York in Spring 2020 by Hunter McNichols as part of the Tech-in-Residence Corps program.

A module for the course: CS 59974: Special Topics in Artificial Intelligence - "Setting up Jupyter Notebooks". Delivered at the City College of New York in Spring 2020 by Hunter McNichols as part of the Tech-in-Residence Corps program.

Goal: To create a succinct yet thorough guide to the art and science of prompt engineering (a.k.a. prompt design, prompt crafting, prompt creation) to elicit the most relevant, effective and accurate outputs. This guide shares evidence-based tips and tricks to optimize responses from Natural Language Processing (NLP) Large Language Models (LLMs) like ChatGPT. Generative Artificial Intelligence (GenAI) performs best when prompted by a skilled communicator who understands how to structure and apply specific techniques for various purposes and use cases in order to generate the desired output.

Overview: This prompt engineering guide compiles material from online prompt engineering courses, YouTube videos, journal articles, and conversation threads with GenAI chatbots. All the material covered in this guide will help you understand the basic prompt engineering tips and tricks you can use to optimize interactions with artificial intelligence generators. Sample prompts and examples related to the humanities are interspersed throughout to illustrate best practices/techniques. However, you can easily adapt this guide to any field.

About this Guide: ChatGPT's mainstream debut in November 2022 sparked a seismic shift in education. Suddenly, educators faced a new challenge: how to teach and assess students in a world where AI could generate convincing text at the touch of a button. Recognizing this pivotal moment, a team at Collin College, supported by a Perkins Leadership Grant, sprang into action. Our mission? To harness the power of Generative AI (GenAI) for education. We assembled a diverse team of experts - from subject matter experts and librarians to accessibility gurus and administrative leaders. Together, we crafted this comprehensive GenAI guide, designed to empower both students and educators with the skills for responsible human-AI collaboration. Our journey didn't stop there. We partnered with Texas State Technical College to pilot the guide in their welding program, bringing AI literacy to a hands-on field. Now, we're excited to share this resource widely, gather your feedback, and measure its real-world impact on teaching and learning.

This guide provides student-driven projects that can directly teach subject area standards in tandem with foundational understandings of what AI is, how it works, and how it impacts society.

This guide provides student-driven projects that can directly teach subject area standards in tandem with foundational understandings of what AI is, how it works, and how it impacts society. Several key approaches were taken into consideration in the design of these projects. Understanding these approaches will support both your understanding and implementation of the projects in this guide, as well as your own work to design further activities that integrate AI education into your curriculum.

In this guide, students’ exploration of AI is framed within the context of ethical considerations and aligned with standards and concepts, and depths of understanding that would be appropriate across various subject areas and grade levels in K–12. Depending on the level of your students and the amount of time you have available, you might complete an entire project, pick and choose from the listed activities, or you might take students’ learning further by taking advantage of the additional extensions and resources provided for you. For students with no previous experience with AI education, exposure to the guided learning activities alone will create an understanding of their world that they likely did not previously have. And for those with some background in computer science or AI, the complete projects and resources will still challenge their thinking and expose them to new AI technologies and applications across various fields of study.

This lesson is intended for classrooms that want to show the entire How AI Works video series in a single day. It is not intended to be taught in sequence with the other lessons in this unit, which introduces each video one day at a time.

Students follow along with each video by matching vocabulary from the video, then answering a reflection question about each video. The lesson plan and slides are very sparse and open-ended to allow for improvisation and customization to fit your classroom.

Background: How Neural Networks Work
Pairs with: AI & The Environment, AI & Dance, AI & Facial Recognition
Length: 2-4 hours

Curriculum aligns to:
- NGSS Engineering standards
- ISTE standards
- Common Core ELA/Literacy standards
- Also maps to CSTA standards

Description: GANs are often used when machines create new images or video content. This lesson explores how each work
Pairs with: AI & Deepfakes
Length: 2-4 hours

Curriculum aligns to:
- NGSS Engineering standards
- ISTE standards
- Common Core ELA/Literacy standards
- Also maps to CSTA standards

Description: An introductory hands-on deep dive into the technical details about how machines hold the information that they’ve learned. In the end, students will teach others what they have learned
Pairs with: Everything
Length: 2-4 hours

Curriculum aligns to:
- NGSS Engineering standards
- ISTE standards
- Common Core ELA/Literacy standards
- Also maps to CSTA standards

In this section, we will examine how generative AI (GenAI) tools may assist with academic reading and research. Examples of content generated by ChatGPT will show how GenAI may be incorporated into a classroom setting. Each section offers suggestions for use and various strategies that could be incorporated for those who wish to allow the use of these tools for assignments. Included throughout are suggestions on how to promote students’ ethical and effective use of these tools and to possibly limit their use if desired. By the end of this section, you should be able to use GenAI to support reading practices.

Author: Jonahs Kneitly
Contributors: Gwendolyn Inocencio, Mary Landry, C. Anneke Snyder
Designers: Irene AI, Sweta Kailani
Supervisors: Terri Pantuso, Sarah LeMire

In this section, illustrative examples from ChatGPT show how to incorporate Large Language Models (LLMs) into the writing process while considering ethical concerns associated with such tools, namely avoiding plagiarism or exploitation of AI-generated content. The advent of public access to LLMs means they are now a critically important aspect of digital information literacy. As such, this technology must be addressed in the composition classroom with guided instruction. We recommend a strategy that models application of a modified version of stasis theory to all LLM-generated content.

After reading this section you should be prepared to teach stasis theory as a strategy for continual interrogation that helps rhetors discern whether generative-AI content exhibits appropriate depth, scope, and quality, along with the appropriate next steps in argumentation, writing, or research.

Author: Gwendolyn Inocencio
Contributors: C. Anneke Snyder, Mary Landry, Jonahs Kneitly
Designers: Irene AI, Shweta Kailani
Supervisors: Terri Pantuso, Sarah LeMire

Addresses trends in the Information Technology (IT) industry, with an emphasis on modern communications and Internet technologies and database and Web technologies, and their role in supporting the integration of information systems. Presents framework for understanding integrating concepts and the strategic and organizational factors impacting success of IT in business. The strategic importance of information technology is now widely accepted. It has also become increasingly clear that the identification of strategic applications alone does not result in success for an organization. A careful coordination of strategic applications, information technologies, and organizational structures must be made to attain success. This course addresses strategic, technological, and organizational connectivity issues to support effective and meaningful integration of information and systems. This course is especially relevant to those who wish to effectively exploit information technology and create new business processes and opportunities.

A brief five-module course designed as a non-credit-bearing introduction to AI tools for high school and college students. Adapted from a similar course by Rush University and shared under the CC BY NC SA 4.0 International License.

This lesson centers around the How AI Works: What is Machine Learning? video from the How AI Works video series. Watch this video first before exploring the lesson plan.

In this lesson students are introduced to a form of artificial intelligence called machine learning and how they can use the Problem Solving Process to help train a robot to solve problems. They participate in three machine learning activities where a robot - AI Bot - is learning how to detect patterns in fish.

This lesson can be taught on its own, or as part of a 7-lesson sequence on How AI Works. Duration: 45 minutes

On the eve of the CC Global Summit, members of the CC global community and Creative Commons held a one-day workshop to discuss issues related to AI, creators, and the commons. The community attending the Summit has a long history of hosting these intimate discussions before the Summit begins on critical and timely issues.

Emerging from that deep discussion and in subsequent conversation during the three days of the Summit, this group identified a set of common issues and values, which are captured in the statement below. These ideas are shared here for further community discussion and to help CC and the global community navigate uncharted waters in the face of generative AI and its impact on the commons.

Introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. Applications of rule chaining, heuristic search, constraint propagation, constrained search, inheritance, and other problem-solving paradigms. Applications of identification trees, neural nets, genetic algorithms, and other learning paradigms. Speculations on the contributions of human vision and language systems to human intelligence.

Presents the main concepts of decision analysis, artificial intelligence, and predictive model construction and evaluation in the specific context of medical applications. Emphasizes the advantages and disadvantages of using these methods in real-world systems and provides hands-on experience. Technical focus on decision analysis, knowledge-based systems (qualitative and quantitative), learning systems (including logistic regression, classification trees, neural networks), and techniques to evaluate the performance of such systems. Students produce a final project using the methods learned in the subject, based on actual clinical data. (Required for students in the Master's Program in Medical Informatics, but open to other graduate students and advanced undergraduates.)