What key terms should I know within artificial intelligence (AI) discourse?

Artificial Intelligence (AI)

Artificial Intelligence, often referred to as AI, encompasses computational systems created to imitate human behaviors and cognitive processes. In essence, computer scientists strive to design AI that can augment or replicate specific human cognitive functions, such as problem solving, experiential learning, pattern recognition, and decision making.

Generative AI (GenAI)

Generative AI is a branch of artificial intelligence trained on vast datasets (text, images, audio, code, etc.) in order to generate contextually relevant outputs. ChatGPT is a well-known formation of GenAI designed to generate human-like language in response to a prompt; in fact, the “G” in ChatGPT stands for “generative” to reflect this nature. However, GenAI covers a great variety of generators, such as image generators like Midjourney and DALL-E 2, or voice generators like Eleven Labs. Amid these various formations, ChatGPT belongs to a specific subcategory of GenAI called Large Language Models.

Large Language Models (LLMs)

Large Language Models are a specific subset of GenAI designed to process and generate human language. These models, trained on extensive textual data, can understand, generate, and manipulate text in ways that resemble human communication. LLMs include popular chatbots like OpenAI’s ChatGPT, Microsoft’s Bing, Google’s Bard, and more.

What should I know about annotated bibliographies before I begin this tutorial?

A Brief Overview

In the world of academic research, an annotated bibliography serves as a valuable tool for summarizing, evaluating, and organizing sources related to a specific topic or research question. More specifically, an annotated bibliography can help you move beyond simply logging a list of sources to actively engaging with research through concise annotations. Conventionally, these annotations include a summary of the source, an evaluation of its credibility and rhetorical merits, and a reflection on its usefulness for organizing your research. Through the process of critically engaging other authors’ work on your topic, you will begin to understand how their research impacts your own. This process includes identifying how their arguments may support or contradict your thesis statement, which helps you recognize where your particular stance fits into the larger academic conversation surrounding the selected topic.

The work that goes into creating an annotated bibliography can be challenging, whether it is the difficulty of refining a research question or the struggle of reading a complex academic article. This tutorial aims to demonstrate three ways GenAI like ChatGPT can assist you in the challenges specific to creating an annotated bibliography. For further information on annotated bibliographies in general, see table 1.

Table 1

Open-Access Resources Related to Composing an Annotated Bibliography

What are three ways ChatGPT can support annotated bibliography composition?

Exploring, Evaluating, and Refining a Research Question

While coming up with a research question to explore might sound like a simple task—After all, it’s just a single question!— a number of important factors make the process challenging, such as scope and depth. For example, the question “What are the effects of climate change on the global environment?” is too broad in scope to cover in an 8-10 page assignment, whereas the question “What is the average temperature change in New York City during the month of July from 2000 to 2010?” is too narrow in depth to derive any meaningful insights beyond statistical analysis. A question like “How are coastal communities in Florida adapting to sea-level rise and what are the challenges they face in implementing adaptive measures?” strikes the right balance between feasibility and specificity, yet getting to such a question can be difficult. This is where ChatGPT can provide valuable assistance.

Once you have a preliminary research question you’re interested in exploring, you can workshop it in ChatGPT with specific prompts geared towards exploring variations of the question, evaluating the question’s strengths and weaknesses, or refining the question. Consider the following example under Strategy in Action #1, in which a user collaborates with ChatGPT to improve their initial research question: “Are high school and college students overworked?” In this conversation, the user’s prompts leads ChatGPT to help the user explore different perspectives of their topic (such as socio-economic factors and institutional practices), evaluate the strengths and weaknesses of their question (a relevant yet subjective topic), and receive suggestions for refinement (targeting a specific academic discipline or student population)—ultimately leading to a more focused starting point for the user’s research. In this way, ChatGPT can provide personalized support in helping you compose a research question that is effective and appropriate for the assignment expectations.

Strategy in Action #1

View the following example prompt and response with ChatGPT focused on exploring, evaluating, and refining a research question.

If you’d like to interact directly with ChatGPT in the above conversation, access the original chat through this link^[4] and click the ‘Continue the conversation’ button in the opened dialogue window.

Brainstorming and Determining Effective Search Components and Keywords

In the process of locating sources for an annotated bibliography, finding the right words to search a database can be tricky. Imagine, for instance, you are researching the impact of social media on mental health. At first, you start with broad terms like “social media” and “mental health,” which returns an overwhelming number of results. To narrow down the search, you learn to focus on mental health key terms, such as “anxiety,” “depression,” or “self-esteem.” Consequently, your query yields more focused results.

Within this process, ChatGPT can provide support as a personalized super-thesaurus that you can use to fine-tune your search strategies. Engaging ChatGPT with a prompt like “Can you give me a list of words that are associated with mental health?” can help you brainstorm effective keywords (such as “anxiety,” “depression,” or “self-esteem”) to generate more focused results. To illustrate this use, Strategy in Action #2 provides an example prompt and response in which a user leverages ChatGPT to determine keywords for researching social media’s impact on mental health. Through such collaboration, ChatGPT can empower you to access relevant primary and secondary sources that support your research.

Strategy in Action #2

View the following example prompt and response with ChatGPT focused on brainstorming and determining effective search components.

If you’d like to interact directly with ChatGPT in the above conversation, access the original chat through this link^[5] and click the ‘Continue the conversation’ button in the opened dialogue window.

Check out table 2 for additional tips and suggestions related to brainstorming and determining effective search components.

Table 2

Tips and Suggestions: Brainstorm with Boolean Operators

Deciphering Complex Ideas within Academic Articles

As you work on gathering sources to include in your annotated bibliography, you may find yourself reading through multiple, lengthy academic articles that are written in perhaps an unfamiliar format or complex disciplinary jargon—all within the limited window of time allotted to the assignment. This can be a lot of work.

To make the most of this research process, using ChatGPT as a reading aid can be beneficial. When you get stuck in a particularly dense paragraph of an academic article, try copying the paragraph into the chatbot and asking it to rephrase the content in simpler terms or with clarifying examples. ChatGPT’s response can give you a clearer understanding of the article’s key points, as it did in the Strategy In Action #3 example.

In this example conversation, the user prompts ChatGPT to simplify the complex ideas in a paragraph from Jay T. Dolmage’s book Disability Rhetoric.^[7] Following the chatbot’s first response, the user was still unclear on what a particular term meant (e.g., “relative mean”), so the LLM provided a simplified example as a framework for understanding the term. Using ChatGPT in this way would enable you to closely analyze and interpret various sources, as you carefully collect the most relevant texts for your annotated bibliography.

Strategy in Action #3

View the following example prompt and response with ChatGPT focused on deciphering complex ideas from academic articles.

If you’d like to interact directly with ChatGPT in the above conversation, access the original chat through this link^[8] and click the ‘Continue the conversation’ button in the opened dialogue window.

Check out table 3 for additional tips and suggestions related to deciphering complex ideas from academic articles.

Table 3

Tips and Suggestions: Confirm the Accuracy of Socio-Historical Context

What’s a pitfall to avoid when collaborating with ChatGPT on an annotated bibliography?

Don’t Forget to Properly Cite ChatGPT

A key part of composing an annotated bibliography is making sure each source has been properly cited according to your instructor’s preferred style guide (often MLA or APA). Citations are not only important as a practical list of references a reader can follow up on, but also as a mark of academic integrity. But when it comes to using ChatGPT, how do we safeguard academic integrity? Is ChatGPT a source that should be cited as well?

Embedded in these questions is a more complicated question: Should ChatGPT be considered an author? In the traditional sense, ChatGPT cannot be viewed as an author; it cannot take responsibility for generated text or how that information is interpreted and used. However, because this output is also not original thought belonging to the ChatGPT user, any generated content (text, data, etc) or functionality (translation, etc) garnered from the chatbot should be cited by users who incorporate the technology into their work.

Regarding how to cite ChatGPT, the ethical practices and processes of citation, research, and accreditation depend on individual citation styles. Be certain to use the style format required by your instructor or endorsed by your particular field. Moreover, be aware that each of these citation styles have different approaches to the question of authorship: MLA very clearly states that ChatGPT is not an author ^[9]while APA does consider OpenAI, the creator of ChatGPT, to be the author^[10] of information generated by ChatGPT.

Learning Outcomes for ENGL 1302: How does this tutorial apply to state standards?

This tutorial is designed to support your success in ENGL 1302 (Composition II) by aligning with the student learning outcomes established by the Texas Higher Education Coordinating Board. As highlighted in Table 4 below, this tutorial directly addresses three key standards. By aligning with these specific learning outcomes, this tutorial not only provides you with skills and knowledge that transfer across diverse learning environments but also gives your education value outside your institution.

Table 4

Applicable State Learning Outcomes