Artificial Intelligence subjectId: 690674

Welcome to the Artificial Intelligence Research Guide. This guide is for students, staff and faculty at UM and supports your AI research by highlighting resources from the University of Miami Libraries and beyond. Whether you’re new to AI or more experienced, librarians are here to help—contact a Subject Librarian for personalized assistance.

*Portions of this research guide were created using Gemini, Microsoft Copilot or Adobe Firefly to edit/create content.

• Mastering AI for Research
• Prompt like a Pro

Tools listed below are either free, freemium, or accessible via subscription for University of Miami students, faculty and staff. There is a full listing of UM enterprise AI tools at AI Tools at the U. For more AI tools, check out the Generative AI Product Tracker which lists generative AI products that are, "marketed towards postsecondary instructors, researchers, or students for teaching, learning, or research activities."

Disclaimer: The tools included in this guide are intended to provide an overview of various AI tools that may be beneficial for university students, faculty, and staff. The inclusion of any specific tool does not constitute an endorsement. Users are encouraged to independently verify the suitability, reliability, and appropriateness of each tool for their specific needs. You can evaluate AI tools using VALID-AI.

Appendix I: A Short History of AI | One Hundred Year Study on Artificial Intelligence (AI100). (n.d.). Retrieved July 31, 2026, from https://ai100.stanford.edu/2016-report/appendix-i-short-history-ai

Artificial intelligence (AI) algorithms: A complete overview | Tableau. (n.d.). Retrieved June 3, 2024, from https://www.tableau.com/data-insights/ai/algorithms

Artificial intelligence tools at the U | University of miami information technology. (n.d.). Retrieved June 3, 2024, from https://it.miami.edu/about-umit/resources/ai-tools/index.html

ChatGPT | Definition & facts | Britannica. (n.d.). Retrieved June 3, 2024, from https://www.britannica.com/technology/ChatGPT

Facebook, V. S. S. at, Instagram, V. S. S. at, LinkedIn, V. S. S. at, X, V. S. S. at, Flickr, V. S. S. at, Youtube, V. S. S. at, TikTok, V. S. S. at, University, C. S., Compliance, Safety, C., Statement, A., Statement, P., IX, T., & Comments. (n.d.). Glossary of AI terms. Retrieved June 3, 2024, from https://www.csus.edu/information-resources-technology/ai/glossary-of-ai-terms.html

Introducing ChatGPT. (n.d.). Retrieved June 3, 2024, from https://openai.com/index/chatgpt/?ref=blog-what-is-chat-gpt-understanding¶ms=ref-blog-what-is-chat-gpt-understanding

TLS, U. L. (n.d.). LibGuides: Artificial intelligence (AI): Introduction. Retrieved June 3, 2024, from https://guides.lib.utexas.edu/c.php?g=1363366&p=10070740

What are AI hallucinations? | IBM. (2023, September 1). https://www.ibm.com/topics/ai-hallucinations

What are AI prompts? (& How to make them better) | Copy.ai. (n.d.). Retrieved June 3, 2024, from https://www.copy.ai/blog/what-are-ai-prompts

What are large language models (LLMs)? | IBM. (2023, November 2). https://www.ibm.com/topics/large-language-models

What is ChatGPT? (n.d.). Retrieved June 3, 2024, from https://www.brandeis.edu/teaching/chatgpt-ai/index.html

What is ChatGPT and why does it matter? Here’s what you need to know. (n.d.). ZDNET. Retrieved June 3, 2024, from https://www.zdnet.com/article/what-is-chatgpt-and-why-does-it-matter-heres-everything-you-need-to-know/

What is generative AI? | IBM. (2024, March 22). https://www.ibm.com/topics/generative-ai

What is machine learning? Definition, types, and examples. (2024, March 27). Coursera. https://www.coursera.org/articles/what-is-machine-learning

What is natural language processing? Definition and examples. (2024, March 19). Coursera. https://www.coursera.org/articles/natural-language-processing

University of Miami Researchers who work with data may want to use Artificial Intelligence (AI) in their research to help with many tasks including, but not limited to:

• Locating data
• Processing data
• Sharing data
• Finding and writing grant applications

This page will share some tools for using AI when working with research data, as well as responsible and ethical use guidelines. 

The University of Miami Libraries Data & Visualization Services Department provides training and computing resources and consultations related to data analysis, data management, and data visualization.

For assistance, please either book a consultation or email dataservices@miami.edu.

The University of Miami's AI Team provides many resources for University of Miami researchers who want to learn more about AI's role at the University.

• AI Tools at the U - What AI tools the University is currently providing access to.
• Known AI Tools - List of publicly known AI tools and a data use guidance for each tool.

Additional Important AI Use Guidance from the University of Miami

The University of Miami does not permit the use of any patient, student, regulated, internal, and/or confidential data in any public AI tool. Entering data into these platforms could lead to public disclosure and loss of our ability to protect University information, including intellectual property. Be aware of the University's Data Classification Policy. Misuse of University data may result in disciplinary action—to that end, exercise caution when using AI tools.

The University provides access to leading AI tools—such as Copilot and Gemini—which include some data protection. We encourage you to use the University's enterprise AI tools as alternatives to public tools.

Please review security and terms of service for any AI tool before using.

Prompt Engineering is the process of iterating a generative AI prompt to improve its accuracy and effectiveness. Think of prompt engineering like giving really good directions. It’s like telling your friend exactly how to get to a party, including where to turn and what landmarks to look out for. In the same way, you give an AI clear instructions on what you want it to do or say. You have to be specific, like saying “take a left at the big palm tree” instead of just “go that way.” This helps the AI understand and give you exactly the kind of answer or help you’re looking for.

​​​The CLEAR  framework developed by Leo S. Lo is a set of principles designed to improve the interaction with AI through prompt engineering.

Concise
• Be Direct: Use clear and straightforward language.
• Eliminate Fluff: Remove unnecessary words that don’t add value to the prompt.
Logical
• Order Matters: Structure your prompts in a logical sequence.
• Step-by-Step: Break down complex tasks into simpler steps.
Explicit
• Be Specific: Clearly state what you want the AI to do.
• Avoid Assumptions: Don’t expect the AI to “read between the lines.”
Adaptive
• Flexibility: Be ready to adjust your prompts based on the AI’s responses.
• Iterative Approach: Refine your prompts through trial and error.
Reflective
• Feedback Loop: Use the AI’s responses to reflect on the effectiveness of your prompts.
• Continuous Improvement: Aim to improve the clarity and efficiency of your prompts over time.

In academia, researchers must remain vigilant when using AI, considering both its potential benefits and downsides. By addressing these challenges, you can harness AI’s power while minimizing its risks. This tab is dedicated to describing the issues associated with artificial intelligence in regards to academic integrity, bias, environmental impact, hallucinations, labor, and privacy.

Undergraduate Honor Code Plagiarism Definition

"...is representing the words or ideas of someone else as your own. Examples include, but are not limited to, failing to properly cite direct quotes and failing to give credit for someone else's ideas."

Principles from UM PETAL Regarding AI

1. AI should help you think, not think for you. AI tools may be used to help generate ideas, frame problems, and perform research. It can be a starting point for your own thought process, analysis, and discovery. Do not use them to do your work for you, e.g., do not enter an assignment question into ChatGPT and copy and paste the response as your answer.
2. The use of AI must be open and documented. The use of any AI in the creation of your work must be declared in your submission and explained. Your faculty can provide guidance as to the format and contents of the disclosure. The undeclared use of any AI (including text, images, program code, musical notation, etc) in any work may be considered as plagiarism.
3. Engage with AI Responsibly and Ethically. Engage with AI technologies responsibly, critically evaluating AI-generated outputs and considering potential biases, limitations, and ethical implications in your analysis and discussions. Ensure that the data used for AI applications are obtained and shared responsibly. Never pass off as your own work generated by AI.
4. You are 100% responsible for your final product. You are the user; if the AI tool makes a mistake, and you use it, then it’s your mistake. If you don’t know whether a statement about any item in the output is true, then it is your responsibility to research it. If you cannot verify it as factual, you should delete it. You hold full responsibility for AI-generated content. Ideas must be attributed, and sources must be verified.
5. These principles are in effect unless the instructor gives you specific guidelines for an assignment or exam. It is your responsibility to ensure you are following the correct guidelines. Not following them will result in a breach of the Academic Integrity Policy.
6. Data that are confidential or personal should not be entered into generative AI tools. Putting confidential or personal data into these tools exposes you and others to the loss of important information. Therefore, do not do so. See point 3 above.
7. The rules and practices on the use of AI may vary from class to class, discipline to discipline. Do not assume that what is acceptable in a Computer Science class will be acceptable in a Philosophy class. It is the student’s responsibility to stay informed as to the instructor’s expectations. When in doubt, ask.

Users should be aware of the different biases in AI:

• Selection Bias
  • When the participants selected for a study are not representative of the target population, often due to non-random sampling or retention methods. This can lead to skewed results and inaccurate conclusions.
• Confirmation Bias
  • Confirmation bias is the tendency to seek out, interpret, and favor information that confirms one’s pre-existing beliefs or hypotheses. This bias can lead to flawed decision-making and reinforce existing misconceptions.
• Measurement/Information Bias
  • Occurs when key study variables are inaccurately measured or classified, leading to systematic errors in the study’s results. This bias can significantly distort the association between variables and affect the study’s validity.
• Stereotyping Bias
  • Involves forming generalized beliefs or assumptions about individuals based solely on their membership in a particular group, often ignoring their unique characteristics. This cognitive bias can lead to unfair treatment and reinforce existing prejudices.
• Out-group homogeneity bias
  • Tendency to perceive members of an out-group as more similar to each other than members of one’s in-group, often overlooking individual differences. This bias can lead to stereotyping and misjudgments about out-group members.

Artificial Intelligence (AI) demands significant energy, necessitating numerous servers in data centers, which in turn require substantial power. This leads to various environmental impacts, including:

• High Water Consumption: Data centers consume large amounts of water to cool servers.
• Air Pollution: Increased energy use can lead to higher emissions of air pollutants.
• High Energy Use: This often results in greater reliance on fossil fuels, contributing to greenhouse gas emissions.
• Inefficient Resource Use: The operation of data centers can lead to inefficient use of natural resources.
• E-Waste: The rapid advancement of technology can increase electronic waste

AI hallucinations refer to instances where artificial intelligence systems generate outputs that are false or inaccurate, often due to limitations in their training data or inherent design flaws. These hallucinations can undermine the reliability of AI tools. To combat AI hallucinations:

• Critically Evaluate Outputs: Always assess the information provided by AI critically.
• Diversify Sources: Use a variety of sources to cross-check and validate AI outputs.
• Stay Vigilant: Remain alert and cautious when interpreting results from any AI tool.

Note: While the term "hallucination" is commonly used in the AI industry, there are concerns that the phrase can possibly be undermining the "efforts to reduce stigma in psychiatry and mental health."

Artificial Intelligence (AI) is reshaping the labor market in profound ways, presenting both opportunities and challenges. On one hand, AI can enhance productivity and create new job categories, particularly in technology-driven sectors. However, it also poses the risk of job displacement, as AI systems can perform tasks traditionally done by humans, potentially leading to lower labor demand, wage reductions, and less hiring. This dual impact is more pronounced in routine tasks susceptible to automation, while jobs requiring complex problem-solving and creative skills may see growth. The integration of AI into the workplace can exacerbate existing inequalities, as those with higher education and specialized skills may be better positioned to benefit from AI, leaving less-skilled workers vulnerable.

The integration of Artificial Intelligence (AI) into various aspects of daily life has raised significant privacy concerns. One of the primary issues is the scale of data collection; AI systems require vast amounts of data to learn and improve, leading to an unprecedented level of personal data being harvested and analyzed. This often occurs without explicit user consent or awareness, resulting in a loss of control over personal information. Additionally, there’s the risk of reidentification and deanonymization, where AI can potentially track and identify individuals across multiple platforms, breaching the users' expectation of anonymity.

HIPAA

"the generative AI tool ChatGPT is not HIPAA compliant, and the University does not permit its use with any patient data. OpenAI, the company behind ChatGPT, also warns against inputting confidential data into the platform, which could constitute a public disclosure and lead to a loss in our ability to protect University information, including intellectual property." -Artificial Intelligence Tools at the U

"Copilot is not HIPAA compliant yet." -Artificial Intelligence Tools at the U

AI Detectors

"The use of systems that claim to detect AI-generated text (e.g. GPTZero, Copyleaks) are not recommended. Submitting students’ coursework to these systems may constitute a FERPA violation since students’ work is considered an educational record." -PETAL

Citing sources is an essential activity in academia and provides proper credit to authors. It allows your reader to find sources you used and also helps combat plagiarism. Citing AI is just as important as citing any other source.

Note: A majority of the text below is copied from APA Style, The Chicago Manual of Style Online, and the MLA Style Center.

• Include a reference and in-text citation for a specific AI chat when doing so will be helpful for readers.

• Credit AI-generated text when you reproduce its words within your own work.
• "You do need to credit ChatGPT and similar tools whenever you use the text that they generate in your own work. 
• For most types of writing, you can simply acknowledge the AI tool in your text (e.g., “The following recipe for pizza dough was generated by ChatGPT”).

Artificial Intelligence (AI) is revolutionizing the landscape of education, offering innovative tools and methods to enhance both teaching and learning experiences. This research guide aims to explore the multifaceted applications of AI in education. It provides educators with ideas on how AI can be effectively integrated into educational practices to foster a more engaging, efficient, and inclusive learning environment.

PETAL has created the Artificial Intelligence (AI) tools for teaching that "provides guidance for the potential use of Artificial Intelligence (AI) tools for teaching, learning, and other scholarly activity."

Gradesope

Helps you seamlessly administer and grade all of your assessments.

Generative AI Product Tracker

Lists generative AI products that are either marketed specifically towards postsecondary faculty or students or appear to be actively in use by postsecondary faculty or students for teaching, learning, or research activities.

Developing an AI Syllabus Statement & Driving Class AI Discussion (NCSU)

Developing Syllabus Statements for AI (Tufts University)

Possible AI Syllabus Statements (Brandeis University)

Is it possible for students to work on your assignments using genAI tools? (Gettysburg College)

Teach with Generative AI (Harvard University)

Artificial Intelligence Teaching Guide (Stanford University)

"The use of systems that claim to detect AI-generated text (e.g. GPTZero, Copyleaks) are not recommended. Submitting students’ coursework to these systems may constitute a FERPA violation since students’ work is considered an educational record." - PETAL

10 Ways Artificial Intelligence Is Transforming Instructional Design

Artificial Intelligence In Education: Teachers’ Opinions On AI In The Classroom

How AI Is Reshaping Higher Education

Support is available from University of Miami Libraries to help you navigate AI resources with confidence and integrity. Explore the other tabs of this guide to discover AI tools and resources avaliable to you. If you have more questions or need help, don’t hesitate to reach out to a Subject Librarian.

• Mastering AI for Research
• Prompt like a Pro