When I was learning photography, there was a lot of conceptual learning about light and depth of field and composition but also learning how to use my digital camera, what all the knobs and buttons did. As I experimented with taking pictures, my use of the camera helped sharpen my understanding of the relevant concepts of photography. And my better understanding of those concepts in turn informed the ways I used the knobs and buttons on the camera to take better photos.

For this first question, spend just a minute or so thinking about how to prompt an AI platform to use what is known from cognitive psychology research to help you come up with a plan for your semester. Feel free to include anything you think would be helpful in generating a helpful plan. If the platform asks you a question, answer it to get better responses.

Consider whether your prompt was successful in creating a plan. If you don't have much experience creating prompts that allow interaction and personalization / customization of the response, you might want to try again with the example prompt below (and respond to the AI as needed). If your experience did provide a good back-and-forth experience with success in generating a plan, you might prefer to respond to the second prompt. It's your choice, only do one.  

In our Sep 24th class, we will hold a debate on the Sapir-Whorf Hypothesis, which posits that the structure of a language influences its speakers' thought processes and worldview. You will be divided into two groups: one supporting the hypothesis (the “linguistic determinists”) and one opposing it (the “cognitive universalists”).

To prepare for this debate, you will need to research arguments, evidence, and examples supporting your assigned position. In this process, you are encouraged to use AI tools to assist with your research, analysis, and organization of ideas. The debate will be your opportunity to present and defend your position using data, studies, and real-world examples.

This methodology breaks the writing process into four stages: planning, drafting, revising, and editing. In the first two, you will rely on your own skills to generate ideas while practicing the vocabulary and grammar that you have already learned. In the last two, you will receive personalized feedback on your draft from Copilot (UVA’s official generative AI tool) and your instructor.

Generative AI (GenAI) can generate output (text, image, sound, video) based on your input (prompt). We recognize the pedagogical potential of this technology if used responsibly and critically. However, we shouldn’t underestimate the risks of delegating the entire writing process to an AI, which could lead to serious learning deficits. Relying too heavily on AI for writing may hinder the practice of important skills, not the least of which is finding and developing your own voice.

A donut plot is VERY similar to a nested pie chart. In the diamonds dataset in R, there are five different levels of the _cut_ variable. For the first 30 minutes of class, use a generative AI tool (Microsoft Copilot, ChatGPT, or another of your choosing) to attempt to get code to make a nested pie chart where the inner pie is based on whether a diamond’s carat weight is greater than 1 and the outer pie is based on the cut of the diamond, as shown below.

You must make sure that the AI tool only uses the tidyverse package. No other packages are necessary. Upload the plot you create that is CLOSEST to the target plot, but make use green and purple instead of the colors used here.

However, our study in the context of providing AI assistance with writing feedback suggests that students tended to rely on AI assistance rather than actively learning from it. In our study, the reliance on AI became apparent when the assistance was removed, as students struggled to provide feedback of the same quality without the AI's guidance. Furthermore, our research explored the effects of replacing AI assistance with guidance and tips on providing constructive feedback, as well as the utilisation of self-monitoring checklists for students to evaluate the quality of their work independently. This approach proved to be effective, resulting in high-quality feedback when compared to the scenario of removing AI assistance entirely. However, it still demonstrated lesser effectiveness in comparison to relying solely on AI assistance.

Kiera Allison: "What students learn very quickly is what AI is actually capable of and what it's not capable of. And in that context, they learn how to pivot and adapt. So they can try a thing and see if AI helps them achieve it. And if it doesn't, they pivot and try something else. So it was, I think, a good way for students to get a feel for the technology in the context of what they were learning, which specifically was persuasion, how to be persuasive, and also to understand what AI could do to fill out their capabilities. So they're learning about AI and they're also learning about themselves and how those two agents can converge to do something hopefully bigger than either of them could do alone."