AI in MIS 561 and Getting Started

Description:
Before we build a single chart, we are going to settle two questions: how you will work with AI in this course, and why any of this matters to a business audience in the first place. Both answers make the next eleven weeks of tool work far more purposeful. Starting in Flex 3, you will be building visualizations nearly every week; this is the week we lay the foundation for that work.

This assignment has two parts, completed in order.
1. Guided Build
You will complete a free, self-paced AI fluency course, then publish the resulting certificate to the two platforms that will host your professional portfolio for the rest of the term.
• Complete Anthropic’s AI Fluency: Framework & Foundations course and download your Certificate of Completion. The course introduces the 4D Framework you will use in Part 2.
• Add the certificate to your GitHub profile README so it displays on your public GitHub page, and add it to your Tableau Public profile.

A note on the Tableau piece: Tableau Public will not accept a bare image file, so the tutorial walks you through connecting to data, building a dashboard, placing an image object, and publishing the workbook. That publishing sequence is the actual skill here, and you will repeat it on nearly every assignment from Flex 3 onward. The certificate is simply the first thing you have worth publishing.


2. Applied Deliverable
You will select one podcast episode and then write about what you took from it using the write-first, refine-second workflow. Complete the components below in the order listed. Both the order and the evidence of each step are graded.

Component 1: Podcast Selected with Relevant Details
Listen to your chosen episode in full before you do anything else. Then please include the following:
• The title of the podcast you chose to listen to
• The month and year it was published or most recently updated
• The host and any guests, along with their professional background and credentials

Component 2: Original Reflection (your own words)
Write a 250-to-350-word response to the following prompt:
• Based on the episode you listened to, why do audiences retain and act on stories more readily than they do on statistics, and what does that imply for how you should present analytical findings to a business audience? Address at least one thing the episode got right and at least one thing you thought it missed, overstated, or left unexamined.

Draft this before you open any AI tool. The point is not that AI is forbidden; the point is that the comparison between your draft and the refined version is the thing being assessed, and there is nothing to compare if you never wrote a draft. A refined version that is indistinguishable from an unrefined one earns nothing on the later components.

Component 3: Refinement Prompt(s) and Polished Reflection
Paste the exact prompt(s) you gave the AI tool to refine your draft, then include the “improved” version. A strong prompt supplies your draft, your goal, your audience, and your constraints. This is the Description competency in action, and "make this sound better" will not earn full credit. Your own ideas and conclusions must survive the refinement; if the tool changed what you think, you have delegated too much. The word limit is the same!

Component 4: Four D’s Annotation and Citation
Close by connecting your own process to the framework from the course. In a few sentences each, identify what you delegated to the AI and what you deliberately kept (Delegation), how you described the task (Description), what you caught the tool getting wrong or flattening (Discernment), and how you verified and disclosed your use of it (Diligence). Then provide a complete APA citation for the tool.

This citation is the format you will use on every written component for the rest of the semester, so get it right here. Per the course Generative AI Policy, unacknowledged AI use is treated as a form of cheating or plagiarism.