Artificial Intelligence: Using & Evaluating
Artificial Intelligence: Using & Evaluating
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The Michigan School of Psychology outlines several appropriate ways to use AI when creating or revising your own academic work. Using AI effectively in research requires an ethical, informed, and critical approach grounded in strong AI literacy. This pathfinder introduces key aspects of AI literacy, including how to thoughtfully integrate AI tools in your research process while assessing their usefulness along the way.
What is Artificial Intelligence?
What is Artificial Intelligence?
Artificial Intelligence, or AI, has been around for decades in the computer science field, but it did not enter widespread public conversation until 2022, when major breakthroughs changed our day-to-day lives. IBM, a leader in the generative AI space, offers authoritative research on the subject. They define generative AI, or GenAI as "artificial intelligence that can create original content such as text, images, video, audio software code in response to a user's prompt or request." They further define AI Literacy as "the ability to comprehend various aspects of artificial intelligence -- including its capabilities, limitations and ethical considerations -- and use it for practical purposes."
Understanding these concepts is especially important in academic contexts, where responsible use of GenAI requires you to understand your purpose for using AI and awareness of the potential consequences of that choice. This guide will help you develop the questions needed to determine when and how GenAI can appropriately support your academic work.
Get Familiar with AI
Get Familiar with AI
There are many commonly referenced terms used when discussing artificial intelligence. We have highlighted several of those terms, covering core concepts as well as different training models that shape how AI systems function. For a complete list, see Coursera's AI glossary.
There are many commonly referenced terms used when discussing artificial intelligence. We have highlighted several of those terms, covering core concepts as well as different training models that shape how AI systems function. For a complete list, see Coursera's AI glossary.
Algorithm - A set of rules that a machine follows to learn how to do a task.
Artificial Intelligence (AI) - A technology that simulates or mimics human intelligence with systems that learn, problem solve and make decisions.
Big data - Massive and complex data sets that are collected quickly and studied to reveal patters and trends.
Chatbot - A software application designed to imitate human communication through text or voice.
Data mining - The process of analyzing datasets in order to discover new patterns.
Deep Learning - A learning technique that uses algorithms to improve outcomes through repetition without human intervention.
Emergent behavior - Unexpected or unintended abilities enabled by learning patterns and rules from training data.
Generative AI (GenAI) - Technology that creates content (text, video, code, and images) using patterns from large, trained data sets.
Hallucination - A confident but completely wrong answer provided by an AI tool.
Large Language Models (LLMs) - A powerful AI model that has been trained on a massive amount of text data.
Machine Learning (ML) - The process of training from data without being explicitly programmed for every scenario.
Natural Language Processing (NLP) - A type of AI that enables computers to understand spoked and written language.
Neural Network (neural net) - The structure of a deep learning model. It is a computer system designed to function like the human brain.
Prompt - The text or command you give AI to generate a response.
Reinforcement Learning - A training method where AI learns by trial and error, receiving rewards for correct answers and penalties for incorrect ones.
Supervised Learning - A training method where structured datasets are used to train and develop an algorithm.
Unsupervised Learning - A training method where the algorithm is asked to make inferences from the datasets.
AIs Strengths and Weaknesses
AIs Strengths and Weaknesses
Like any powerful technology, Artificial Intelligence encompasses both benefits and risks. A clear understanding of AI's strengths and weaknesses is necessary to navigate, evaluate, and integrate AI in your work.
ECU has two tutorials available: one on strengths and limitations, and one on weaknesses.
Oxford explores numerous uses and caveats.
UT at Austin also lists some different benefits and limitations of generative AI tools.
Additional academic AI guides for further reading
Additional academic AI guides for further reading
Created by an MLIS student at Wayne State University School of Information Sciences, Winter 2026
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