This section is designed to build confidence about what Generative Artificial Intelligence (GenAI) means for the future of education by closely studying the operations, limitations, and theoretical value of a Large Language Model (LLM) like ChatGPT. To this end, this section seeks to explain what language modeling is and how this process contributes to an LLM’s tendency to generate inaccurate information. Additionally, this section considers how the design of an LLM—specifically, the collective knowledge it is trained upon—can contribute to the perpetuation of biases. Lastly, this section encourages critical thinking about the value of an LLM from a theoretical standpoint regarding the writing process and collaborative learning. By the end of this section, you should be able to articulate how an LLM like ChatGPT operates, as well as the value and limitations of this design within the evolution of learning.

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

This two-part unit provides instructors with materials to encourage student development of information literacy skills. The unit can be understood as supplemental materials for the OER textbook Informed Arguments: A Guide to Writing and Research with particular focus on the research aspect of writing and argumentation, or the materials could be useful on its own, for instructors who do not teach with the Informed Arguments textbook. It addresses, most specifically, how to find and evaluate source material. It covers things like types of sources, biases, peer-review processes, and other information literacy skills helpful for successful college writing. It includes 1) general instructor notes, 2) an online discussion activity, 3) a quiz about evaluation of sources, 4) a Research Journal (short essay) assignment, 5) a rubric for the short essay, and 6) a further information resource guide for faculty about information literacy and the college classroom.

Part 1: https://pressbooks.library.tamu.edu/engl1301/chapter/research-and-argumentation-teacher-facing-lesson/
Part 2: https://pressbooks.library.tamu.edu/engl1301/chapter/research-and-information-literacy-student-facing-assignment/

Author: Michael Gardin
Editor: Mary Landry, C. Anneke Snyder
Supervisor: Terri Pantuso

This two-part unit provides instructors with materials to encourage student development of information literacy skills. The unit can be understood as supplemental materials for the OER textbook Informed Arguments: A Guide to Writing and Research with particular focus on the research aspect of writing and argumentation, or the materials could be useful on its own, for instructors who do not teach with the Informed Arguments textbook. It addresses, most specifically, how to find and evaluate source material. It covers things like types of sources, biases, peer-review processes, and other information literacy skills helpful for successful college writing. It includes 1) general instructor notes, 2) an online discussion activity, 3) a quiz about evaluation of sources, 4) a Research Journal (short essay) assignment, 5) a rubric for the short essay, and 6) a further information resource guide for faculty about information literacy and the college classroom.

Part 1: https://pressbooks.library.tamu.edu/engl1301/chapter/research-and-argumentation-teacher-facing-lesson/
Part 2: https://pressbooks.library.tamu.edu/engl1301/chapter/research-and-information-literacy-student-facing-assignment/

Author: Michael Gardin
Editor: Mary Landry, C. Anneke Snyder
Supervisor: Terri Pantuso

This resource contains a news article, a facilitation guide, and two activity handouts. Students flip coins, gather and analize data, and gain an appreciation for the challenge of generating truly random data. Then the students are guided through forming a statistical hypothesis, gathering and analyzing evidence, and interpreting their analysis using p-values. The lesson ends with a discussion of Benford’s Law. This activity aligns with MATH 1342 Learning Outcome 8: Perform hypothesis testing using statistical methods.

Upon successful completion of this assignment, students will
- create a well-organized personal narrative essay that includes all the essential components of a story, such as an introduction, setting, characters, plot (rising action, climax, falling action, and resolution), and conclusion.
- apply descriptive writing techniques, including the use of vivid diction and dialogue, to paint a clear picture in the reader’s mind and maintain an authentic writer’s voice.
- evaluate the effectiveness of their drafting by seeking feedback from peers and revising for clarity, organization, tone, and audience awareness.

Author: Kimberly Stelly
Editor: Mary Landry, C. Anneke Snyder
Supervisor: Terri Pantuso

Upon successful completion of this assignment, students will
- define and distinguish the various types of phrases and clauses.
- analyze sample texts to recognize and label what phrases and clauses are present.
- construct sentences that incorporate specific types of phrases and clauses.

Author: Pujarinee Mitra
Editor: Mary Landry, C. Anneke Snyder
Supervisor: Terri Pantuso

Upon successful completion of this assignment, students will
- prepare for reading a text by analyzing its title through the 5W1H (Who, What, When, Where, Why, and How) questioning technique.

Author: Christopher Manes
Editor: Mary Landry, C. Anneke Snyder
Supervisor: Terri Pantuso

Upon successful completion of this lesson, students will
- analyze a text by completing a graphic organizer that identifies an author’s use of evidence and rhetorical devices.
- evaluate the impact of these devices on the target audience.

A PowerPoint lesson is included

Author: Brandi Morley
Editor: Mary Landry, C. Anneke Snyder
Supervisor: Terri Pantuso

This handout serves as a tool for introducing students to textual analysis, encompassing its definition, objective, significance, various approaches, and considerations for analyzing text.

Authors: Brandi Morley, C. Anneke Snyder
Editor: Mary Landry
Supervisor: Terri Pantuso

Upon successful completion of this lesson, students will
- review the steps in the writing process (Prewriting, Drafting, Revising, Editing, Publishing).
- generate ideas about a topic using three prewriting strategies (Listing, Freewriting, Clustering).
- reflect and discuss the various prewriting strategies as well as their importance to the writing process.

A PowerPoint lesson is included.

Author: Brandi Morley
Editor: Mary Landry, C. Anneke Snyder
Supervisor: Terri Pantuso

In this section, you will gain insights about privacy and confidentiality concerns related to a form of Generative Artificial Intelligence (GenAI) known as Large Language Models (LLMs) and, specifically, OpenAI’s policies about ChatGPT.

The full extent of privacy and confidentiality risks in relation to ChatGPT, which relies on collective intelligence for information gathering and dissemination, has not been fully realized. Users should be mindful of OpenAI’s terms of use, particularly as those terms are subject to change. Though OpenAI claims to not share private user information, the language around such statements is vague and contradictory, and there is a strong possibility that personal information may be monitored by human proctors. Moreover, educators who are bound to the legal obligations outlined in FERPA should be particularly concerned about how student privacy could be potentially violated by using ChatGPT and other GenAI technologies.

After reading this section, you should be able to articulate and discuss OpenAI’s significant terms of use and privacy policy, consider the potential privacy and intellectual property violations contained within the collective intelligence paradigm, and communicate your own concerns about privacy and confidentiality in relation to GenAI technologies.

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

This handout provides definitions and examples of media propaganda techniques, including Name-Calling, Glittering Generalities, Transfer, Testimonials, Plain Folks, Card-Stacking, Bandwagon Appeals, Half-Truths, Loaded Words, Obfuscation, Straw Man Arguments, and Deliberate Manipulation.

Authors: Sharon Haigler, C. Anneke Snyder
Editor: Mary Landry
Supervisor: Terri Pantuso

Thank you for choosing the Dana Center Math Pathways (DCMP) Curriculum resource. The DCMP course programs are research-based and developed from the DCMP Curriculum Design Standards. To obtain the complete course, which includes instructional resources, rubrics, PowerPoints, and answer keys for the preview and practice assignments, you can visit the Dana Center Curriculum Resource Portal to request access. For a low-cost digital version that integrates seamlessly with most Learning Management Systems (LMS), you will need to fill out a Lumen Learning Online Homework Manager (OHM) request form. For any other questions, concerns, or support, please contact Charles A Dana Center danacenter@austin.utexas.edu.Licensing These materials are copyrighted © 2020 by the Charles A. Dana Center at The University of Texas at Austin and are licensed under CC BY-NC-ND 4.0. Under this license, these materials are available to copy and redistribute in any medium of format for non-commercial use only. Appropriate attribution is required. Alterations to formatting are acceptable (e.g., font change, removal of a nonmathematical image, addition of accessibility tools), but derivatives of these materials are prohibited. These materials constitute a comprehensive course curriculum, which was carefully designed to promote scaffolded conceptual understanding and increase levels of student persistence. Because of the intentional design of the course, we recommend using the lessons in the order they are given. Some lessons refer to previous lessons or anticipate future lessons. The following conditions are examples of acceptable use of these materials.a)     Educators and administrators may reproduce and use one printed copy of the material for their personal use without obtaining further permission from the University, so long as all original credits, including copyright information, are retained.b)    Educators may reproduce multiple copies of pages for student use in the classroom, so long as all original credits, including copyright information, are retained.c)     Educators may reproduce and use parts of the materials in presentations, so long as all original credits, including copyright information, are retained.d)    Educators and administrators may reproduce these materials for use for professional development within their departments so long as all original credits, including copyright information, are retained. e)     Educators and administrators may upload the materials to a learning management system so long as all original credits, including copyright information, are retained.f)     Educators and administrators may provide these materials to a local print shop to create copies of the student materials. All original credits, including copyright information, must be retained. These materials may be sold through your department or in college bookstores in order to recover printing costs. g)    Educators and administrators agree that they will not post instructor answer documents in any student-accessible locations.   

This unit begins with the collection of student data that will be used throughout the course. Several early lessons have a student success focus of creating a learning community, forming effective study groups, and crafting written arguments. Mathematically, topics include voting schemes, descriptive statistics and graphical displays, theoretical probability, conditional probability, conversions, indices, weighted averages, expected value, simple and weighted moving averages, part-to-part and part-to-whole ratios, absolute and relative change.

This unit begins with the collection of student data that will be used throughout the course. Several early lessons have a student success focus of creating a learning community, forming effective study groups, and crafting written arguments. Mathematically, topics include voting schemes, descriptive statistics and graphical displays, theoretical probability, conditional probability, conversions, indices, weighted averages, expected value, simple and weighted moving averages, part-to-part and part-to-whole ratios, absolute and relative change.

This unit begins with the collection of student data that will be used throughout the course. Several early lessons have a student success focus of creating a learning community, forming effective study groups, and crafting written arguments. Mathematically, topics include voting schemes, descriptive statistics and graphical displays, theoretical probability, conditional probability, conversions, indices, weighted averages, expected value, simple and weighted moving averages, part-to-part and part-to-whole ratios, absolute and relative change.

This unit begins with the collection of student data that will be used throughout the course. Several early lessons have a student success focus of creating a learning community, forming effective study groups, and crafting written arguments. Mathematically, topics include voting schemes, descriptive statistics and graphical displays, theoretical probability, conditional probability, conversions, indices, weighted averages, expected value, simple and weighted moving averages, part-to-part and part-to-whole ratios, absolute and relative change.

This unit begins with the collection of student data that will be used throughout the course. Several early lessons have a student success focus of creating a learning community, forming effective study groups, and crafting written arguments. Mathematically, topics include voting schemes, descriptive statistics and graphical displays, theoretical probability, conditional probability, conversions, indices, weighted averages, expected value, simple and weighted moving averages, part-to-part and part-to-whole ratios, absolute and relative change.

This unit begins with the collection of student data that will be used throughout the course. Several early lessons have a student success focus of creating a learning community, forming effective study groups, and crafting written arguments. Mathematically, topics include voting schemes, descriptive statistics and graphical displays, theoretical probability, conditional probability, conversions, indices, weighted averages, expected value, simple and weighted moving averages, part-to-part and part-to-whole ratios, absolute and relative change.

This section builds on previous sections and provides engagement with more complex graphical displays. Topics include analyzing stacked and comparative stacked column graphs, motion bubble charts, and heat maps.  Some of the displays you encounter will be misleading or erroneous. You will also start from data and choose a way to model the data. You will be asked to draw conclusions and make decisions based on multiple pieces of quantitative information, and to write about those conclusions and decisions.