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AI Fundamentals

Essential Course Information

About This Course

This chapter explores the mechanics behind AI-generated content and why these systems sometimes produce inaccuracies. By peering behind the curtain of how large language models function, you'll develop a critical eye for evaluating AI outputs. These discernment skills are increasingly important as AI reshapes our information ecosystem, allowing you to separate reliable content from potentially misleading information.

Pattern Completion vs. Factual Knowledge

How AI Generates Information

This chapter explores the potentially deceptive nature of AI-generated information, particularly how systems present both accurate and inaccurate content with the same authoritative voice. As college students encounter AI tools in academic settings, understanding these limitations is important for maintaining academic integrity.

The AI Confidence Paradox

Why AI-Generated Information Can be Misleading

This chapter builds on your understanding of AI limitations by providing practical verification techniques. You'll learn strategies to validate AI-generated content, assess citations, and identify unreliable information before it undermines your academic work.

Evaluation Techniques and Strategies

Evaluating AI-Generated Content

The chapter guides learners through creating personalized documentation systems to track AI interactions, establishing clear protocols that balance academic integrity with efficient AI use, and implementing the "Evaluate Before You Integrate" decision-making framework. Students learn to develop sustainable evaluation practices that help them distinguish between AI-generated content and their original thinking, properly attribute AI contributions, and apply appropriate verification methods based on the importance of the information and type of assignment, ultimately fostering responsible AI use habits that support both academic honesty and effective learning.

Documentation Methods for AI Interactions

Establishing Personal AI Use Protocols

The "Evaluate Before You Integrate" Framework

Additional Resources

Developing Responsible AI Information Practices

Pass the knowledge quiz with a score of 100% to earn the course completion certificate.  

Course Completion Quiz

AI tools have become ubiquitous companions for students—but they can confidently present fiction as fact. This course equips you with essential skills to distinguish reliable AI-generated information from convincing misinformation that could undermine your academic work.Through hands-on exercises and practical techniques and strategies, you'll learn to identify AI hallucinations, apply verification strategies, and recognize when AI systems are operating outside their knowledge base. You'll develop a systematic approach to evaluating AI responses before incorporating them into your assignments ensuring your work remains credible and academically sound.Whether you're using AI for research papers, presentations, or projects, these skills will protect your academic reputation while allowing you to leverage AI's capabilities effectively. Join us to build the critical thinking community needed to navigate the complex relationship between artificial intelligence and academic integrity in college.

Develop skills for distinguishing between reliable AI-generated information and convincing misinformation to protect your academic reputation. Learn valuable skills and have the confidence that your work is truly your own.

AI Information Literacy for College Students

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What you will learn

Identify AI-generated misinformation and explain how AI systems can produce plausible but incorrect information.

Articulate why AI information can be misleading, including the differences between pattern completion and factual knowledge.

Apply critical evaluation techniques such as triangulation, source validation, and confidence scoring to assess AI-generated content.

Recognize signals of uncertainty in AI responses, including hedging language and vague attributions.

Develop responsible AI use practices by systematically documenting interactions and verification processes.

Implement the "Evaluate Before You Integrate" framework when incorporating AI-generated information into academic work.

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