IU Libraries Explor"AI"tion Challenge

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Welcome to the IU Libraries Explor"AI"tion Challenge!

 As generative artificial intelligence (GenAI) tools like ChatGPT become increasingly widespread, it’s important to understand the potential benefits and consequences of using these tools. Whether you’re AI-savvy or AI-skeptical, the IU Libraries Explor"AI"tion Challenge is here to help you gain a deeper understanding of the current capabilities and limitations of GenAI.

This Canvas Challenge includes an explanation of how GenAI works, explores some potential weaknesses to be aware of when using GenAI, and provides recommendations for thinking critically about your GenAI usage. To complete this challenge, review the modules and complete the Knowledge Check at the end. 

Upon completing this course, you can enter for the chance to win one of several Lego sets by entering your IU email address!

To begin, head to the Modules section and start with What is Generative Artificial Intelligence?

This is a space eploration themed graphic with a rocket in the right corner with text that reads "Explor“AI”tion in the Age of AI". The poster's title is "Navigate research with confidence in a new era." The rest of the poster's text is as follows: What is generative AI? Generative AI can be used to generate unique and creative content, including text, pictures, videos, and more. AI models are trained through machine learning, where machines learn patterns in data and replicate them on their own without any additional human input. They are trained on large datasets and respond to user prompts to generate new outputs. Large Language Models (LLMs), like ChatGPT, Gemini, Co-Pilot, and Grok, produce content based on the patterns they have learned. Why AI gets it wrong sometimes: Large Language Models create human-like language using statistics. They are programmed, based on large amounts of data, to create responses based on the statistical likelihood words come next. They learn, although not in the same way humans do, patterns from the data they are trained on. First, they utilize patterns they have learned related to the prompt you input. Then, they pick each word based on the statistical likelihood, garnered from their data, that it will come next. This process is why sometimes AI hallucinates. Based on statistical likelihood, AI makes a guess at what should come next. And sometimes that guess is wrong. While it can reference facts and correct resources, it is never guaranteed to be correct. How you can get it right: Verify the information elsewhere When LLMs give you outputs you would like to be factual, double check with a more reliable source. Resources you can use from IU Libraries include peer-reviewed articles from one of the 1,700 databases. Ask LLMs for citations (then check them) LLMs are capable of giving you the citations they used to generate information. But like all generative AI outputs, they’re not guaranteed to be correct. Verify the information in the source. Don’t ask LLMs for information you need to be correct LLMs are not yet capable of reliably producing factual information every single prompt. Double check with reliable primary and secondary sources from places like IU Libraries. Thinking is critical: Generative AI tools may get it wrong, but that doesn’t mean you have to. It is important to think carefully about how the ways you interact with LLMs (if you choose to do so). By utilizing critical thinking skills to inquire about whether information produced is correct and where information came from, you can utilize Large Language Models more efficiently and effectively. Remember: Large Language Models do not have the critical thinking skills that you do. They cannot think the way humans can. This ability is unique and very special.

References:

AI for Education. (n.d.)“Generative AI Explainer,” https://www.aiforeducation.io/ai-resources/generative-ai-explainer. Accessed 27 Jan. 2026.

Banh, L., & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets, 33(1), 63. https://doi.org/10.1007/s12525-023-00680-1

Bergmann, D. (2025, August 18). What is machine learning? | ibm. https://www.ibm.com/think/topics/machine-learning

Denning, P. J. (2025). Artificial intelligence: Generative ai. Ubiquity, 2025(July), 1–19. https://doi.org/10.1145/3747356

 

 

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