Becoming conversant with AI terminology is crucial for university stakeholders, including faculty, administration, students, and staff, as it permeates various aspects of academic and administrative operations and our day-to-day work environment. Here is a list of AI terms tailored for the broader university community:

1. Artificial Intelligence (AI): The simulation of human intelligence in machines programmed to think and learn like humans. 
2. AI literacy: having the skills and competencies required to use AI technologies and applications effectively and ethically. 
3. AI Bias: Prejudiced outcomes resulting from flawed assumptions or data used to train AI systems.
4. AI hallucinations: Instances where artificial intelligence systems generate false or misleading information that does not accurately reflect the data they were trained on. This phenomenon typically occurs in generative AI models, such as those used in natural language processing or image generation. AI hallucinations can be due to various factors, including biases in the training data, overfitting, or the model's inability to distinguish between reliable and unreliable information.
5. AI-Resistant Learning: AI-resistant learning focuses on tasks and assessments designed to evaluate students' capabilities without the assistance of AI tools.
6. AI-Inclusive Learning: AI-inclusive learning incorporates AI tools and platforms to enhance the educational experience.
7. Chatbots: are AI-powered software programs designed primarily for text-based interactions, to assist users with various tasks like answering frequently asked questions, providing customer support, or assisting with specific processes. Examples include chatbots found in websites, messaging apps, and customer service portals. 
8. Deep Learning: is a subset of machine learning and earns its name “deep” because it utilizes deep neural networks, multiple layers of networks, to process data and make decisions. 
9. Ethical AI: The practice of ensuring AI technologies are developed and used in ways that are morally right and fair.
10. Generative Artificial Intelligence (GenAI): This type of AI specializes in creating a wide range of data, such as images, videos, audio, text, and 3D models. It does so by analyzing patterns within existing data and using this knowledge to generate new and unique outputs.
11. Large Language Model (LLM): LLM is a deep learning algorithm capable of performing various natural language processing tasks. These models utilize transformer architectures and are trained on massive datasets, which is why they are considered “large.” Can recognize, translate, predict, or generate text and other content. 
12. Machine Learning (ML): A subset of AI that enables systems to automatically learn and improve from experience without being explicitly programmed.
13. Neural Networks: Computing systems inspired by the biological neural networks that constitute animal brains, used for pattern recognition and decision-making.
14. Natural Language Processing (NLP): Technology that allows computers to understand, interpret, and generate human language.
15. Prompting: In the context of AI, prompting is the act of feeding specific text inputs to steer the subsequent outputs and requires expertise for optimal results.
16. Prompt Engineering: The iterative process of structuring and refining text that can be interpreted by generative AI models.
17. Virtual Assistants: are AI-powered comprehensive software programs designed to assist users with various tasks and can handle voice-based interactions and perform a wide range of tasks beyond text-based. They simulate human interactions and can manage calendars, set reminders, control smart home devices, answer questions, complete actionable tasks, and even make phone calls. 

Understanding these terms will not only enhance the university community's ability to engage in AI-related discussions but also empower individuals to leverage AI tools and solutions effectively in their respective domains. This foundational knowledge is vital as AI continues to shape the future of education, research, and campus life.

We will try to update this page as needed. If you feel a term should be added, please email jessica.m.sanchez@utrgv.edu or raymundo.garza@utrgv.edu.  

References

ACUE (2023). Basic Artificial Intelligence Terms [pdf]
OpenAI. (2024). ChatGPT4 [Large language model]. https://chat.openai.com 

Contributors

• Dr. Jessica Sanchez, Center for Online Learning & Teaching Technology
• Raymundo Garza, Center for Online Learning & Teaching Technology
• Irma Hermida, Strategy & Bus Relationships
• George Handley, Center for Online Learning & Teaching Technology