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Multidimensional Encoding
In this video, Dr. Matthew Hudnall shares how AI understands complex relationships between words, images, and sounds through multidimensional encoding, a mathematical technique that enables AI to translate languages, recognize patterns, and generate content with remarkable human-like understanding.
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Not All AI Are the Same
Dr. Matthew Hudnall explains how the blanket term “AI” makes up a diverse ecosystem of specialized systems, from Netflix recommendation engines to self-driving car sensors to creative content generators. Understanding these different types of AI helps you choose the right tool for each task and set realistic expectations for what each system can actually do.
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Reinforcement Learning from Human Feedback (RLHF)
Dr. Matthew Hudnall explains how reinforcement learning from human feedback (RLHF) teaches AI models to align with human expectations, making them more accurate, polite, and useful while also highlighting its ethical challenges.
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Retrieval-Augmented Generation (RAG)
In this video, Dr. Matthew Hudnall introduces retrieval-augmented generation (RAG), a technique that combines AI’s ability to generate text with real-time information retrieval, addressing one of the biggest limitations of traditional models.
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Supervised vs. Unsupervised Learning
Dr. Matthew Hudnall explores how supervised and unsupervised learning differ, and why both are essential to understanding how AI solves problems and reveals hidden patterns.
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Tackling AI Stigma and Existential Crises
Dr. Katie Chiou outlines fears associated with the use of generative AI and explains how educators can approach these concerns in a manner that promotes the thoughtful discussion of AI’s legitimacy, limitations, and ethical concerns.
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Teaching Students about Ethics and AI
Dr. Katie Chiou shares approaches for teaching students about the ethical implications of generative AI with strategies for classroom discussions to encourage critical thinking on AI development and deployment.
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Teaching Students About Plagiarism
Plagiarism has always been a challenge, but how does AI complicate things for students and instructors? Dr. Katie Chiou dives into what plagiarism really means, the unique dilemmas posed by AI, and practical strategies to address them.
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Teaching Students to Cite AI
As AI tools become commonplace in academia, how should students cite them correctly? Dr. Katie Chiou introduces essential best practices and guidelines for teaching students how to properly attribute AI-generated content.
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Teaching Students to Verify Information
Dr. Laura McNeill discusses the importance of developing students’ critical thinking skills through verifying AI outputs. This video focuses on practical strategies like cross-referencing with reputable sources to evaluate AI-generated information.
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Teaching Students to Write Effective Prompts
Dr. Laura McNeill shares how faculty can teach students to write effective prompts by focusing on clarity and specificity to reduce the likelihood of inaccurate or irrelevant outputs.
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The “Act As” Method
Mastering AI prompts can significantly improve the quality of chatbot responses, and the “ACT as” method provides a simple yet powerful approach to guiding AI effectively. Dr. Lawrence Cappello explains how specifying a role, giving clear instructions, providing examples, and encouraging questions can help educators and others use AI as a valuable tool.
AI Teaching Network
Welcome to The University of Alabama AI Teaching Network! This video library features short, practical clips of use cases and advice for responsibly using generative AI in teaching practices. Use the drop-down menu below to filter by category or explore the entire library for content that fits your teaching needs.