For Day 2 of LO9, we are going to have a workshop on building charts in Excel. To do this, you must first create the Online Excel file that has all of your learning log data in it. Do the following:

1. Go to https://forms.office.com/Pages/DesignPagev2.aspx
2. Open your Microsoft Form Learning Log
3. Click on “View Responses” in the top right corner
4. Click on “Open results in Excel” to the right of the results summary. This will take you to the Online Excel file that your form is creating entries into
   1. Do not click to download a csv or open on your Desktop. We want you to create an Online Excel file so that it updates automatically as you make more learning log entries.
5. To confirm that you have completed all these steps, you will create a simple histogram to share for Discussion Prep. Do the following:
   1. Select the column for the question “How much time did you spend, in minutes?”
   2. Pick the “Insert” tab of the menu
   3. Click on the drop-down menu for the charts:
      
   4. Choose Histogram from the drop-down.
      
      
   5. You should get something like this (note this is synthetic data):
      

Now that you know the basics of data visualizations and charts, and last class covered (some) ways to do charts poorly, watch the following videos to learn about more kinds of charts and visualization best practices.

• Every Chart Type Ranked — What to Use and What to Avoid by Analyst Academy
  • Note: You do not need to memorize all of these charts. The goal is to know they exist (or you can go back to the video to find them) and have a sense of what they are so that you can use them.
• Data Visualization Crash Course | Consulting Best Practices by Analyst Academy

Watch the following free videos from Data Literacy Level 1. Videos 1.1 and 1.6 are optional. These are the foundational ideas around data visualization.

• 1.2 A review of Data Scale Types
• 1.3 Graphical Marks & Encoding Channels
• 1.4 Expressiveness of Encodings
• 1.5 Effectiveness of Encodings

Then watch CompSci216: Basic Plot Types video about basic plots and how to explain a spot on a graph from CompSci216’s course. You can skip slide 2 (the one after the title slide) and start the video at slide 3 at timestamp 0:38. We will not be coding in this class, so slide 2 is not relevant. You can stop watching the video at 10:44 after slide 21, since I do not expect you to know about kernel density estimation plots.

Imagine a world where someone got their hands on Thanos’s Infinity Gauntlet and, with a single snap, replaced all the words referring to transportation with the word “vehicle.” No one has a word for “car,” “bike,” “ship,” “airplane,” or “spaceship.”

It would be absolute chaos. One person would say, “Vehicles are great for the environment,” when talking about bicycles, while another may respond, “No! They are destroying our planet,” because they are thinking of heavy-fuel-oil-powered cargo ships. Meanwhile, news of a recently designed, highly efficient, electric-powered, small airplane has people thinking their next car might fly.

It would take years, maybe generations, to rebuild the lost vocabulary. It takes time to articulate the differences between two different vehicles, create a word to differentiate them, and then get others to adopt the word until there’s a shared understanding. And where there’s ambiguity, there’s opportunity to take advantage of the confusion for your own gain.

So, in this world, salespeople lean into this confusion. They advertise a scooter with a basket as a “smart personal vehicle with space for cargo” and point out how it is sleek and eco-friendly. From the ad, it sounds like a car. In reality, it doesn’t go far and barely holds anything. But when the word vehicle could just as easily mean a cargo ship or a skateboard, who’s to say what you’re supposed to expect?

This is what it’s like with the word “AI” right now. So, in this learning objective, we are going to start our journey to build our vocabulary so that AI is seen as generic as the word vehicle, and we can become suspicious if no other details are provided. With a greater vocabulary, we can become skeptical in a more precise way.

Terminology

AI is a very large umbrella term. Overall, it can be broken down into two main categories and a hodgepodge of other things. One category is predictive AI, which refers to a system that uses data to estimate future outcomes or classify current states. How they do this depends on the data they have and the mathematical model that is built using that data. Another main category is generative AI, which refers to a system that creates new content based on the data it is trained on. Rather than predicting an outcome or state, it produces something that resembles its data but is not (usually) exactly like its data. Due to the umbrella nature of the term AI, there are other things that are labeled AI that do not fall in these two categories. We list a few of them in the table below as well.

Note: This is not an exhaustive list. AI is a large and rich research field, with new applications emerging continually. In addition, the categorization between predictive and generative AI is arguable for some of these terms. Some AI systems could really be both. For example, it’s not uncommon for generative AI to rely on predictive AI in some way under the hood. However, for the purposes of this class, we do not have the time to go into all the shades of gray. Therefore, this is how we will characterize things and what we will use for this class.

Kind	Category	Purpose/Goal	Output	Example
Classification	Predictive	Assigns a known label to its input	A textual label like "cat" for images or yes/no, depending on the model	Classifying whether an email is spam or not spam based on the email content and where it came from
Recommendation	Predictive	Suggests relevant content or items based on a set of input data	A ranked list of items, content, or actions	A ranked list of recommendations of what video to watch next based on a particular user's watch history, video likes/dislikes, and the data from other users that are similar to this user.
Decision Making	Predictive	Guides or automates decisions based on a specific context and predicted outcome	A recommended action to take	A tool recommending whether to approve a car loan
Translation	Predictive	Converts text from one language to another	Text in the targeted language	Translating from English to Spanish
Synthetic text generator	Generative	Produce new text based on a given prompt	Text	Producing text to describe a product for marketing
Chatbot	Generative	A subclass of synthetic text generators that focus on turn-taking conversations	Multi-round conversational text from two or more entities	A customer service chatbot that tries to help the customer without the need of a human
Synthetic image generator	Generative	Produce an image based on a text prompt	Image	Producing an image of a space alien from the prompt "draw me a space alien"
Synthetic audio generator	Generative	Produces audio based on text or structured input.	Audio, such as music, speech, or sound effects	An app that turns text into a short audio clip of part of a song
Synthetic video generator	Generative	Produce a video based on (likely) a sequence of prompts	Video	Producing a short video clip of a dancing cat alien
Automation	Other	Performs repetitive tasks or a set of predefined tasks without human intervention	A completed task or process	A machine that installs the door of a car at a manufacturing plant without aid from a human
Robotics	Other	A physical machine that senses the world around it and interacts with it	Physical actions such as movement, manipulation, and sensing	A robot that plays soccer
Artificial General Intelligence (AGI)	Other	A computer that replicates human-level reasoning across any task or domain.	Problem-solving and reasoning ability like a human	This does not exist, and there is no agreed-upon definition or test for this. If someone ever claims this, they made up a definition and claimed it is true.

On the Anthropomorphization of AI

Anthropomorphism is the human tendency to attribute human traits, like intention, emotion, or consciousness, to non-human things. In the context of AI, this becomes especially problematic with synthetic text generators like chatbots. Language is central to how we understand and relate to one another. Linguistics research shows that when we encounter coherent language, we instinctively imagine a mind behind it, a person who is thinking, feeling, and trying to communicate. This is how we evolved to interpret language, and it works well for human relationships. Synthetic text generators have no mind, no goals, no moral judgment, and no understanding. They do not care about us because there is nothing there that can care. They are simply remixing patterns from massive datasets to produce plausible-sounding responses.

A person may anthropomorphize their car and say it “takes care of them on road trips,” but we don’t actually believe the car has emotions or intentions. And yet many of us still treat synthetic text generators as if they had empathy, insight, feelings to be hurt, etc. That’s because language itself triggers social and emotional instincts. We imagine a mind behind the text.

This problem is exacerbated by a common bias that leads people to believe computers are more objective, neutral, and trustworthy than humans. As a result, we are more likely to place undue trust in AI-generated outputs, excuse harmful output as accidental “mistakes,” or assume good intentions where there are none because computers do not have a mind. Synthetic image generators rarely evoke this illusion of sentience, but synthetic text generators routinely do because of how closely human language is tied to our understanding of thought and emotion.

This false perception of humanity in a synthetic text generator and our bias to believe computers are neutral have serious implications. When an AI causes harm, we risk blaming the AI instead of its creators. The creators are the ones who designed it. They decided what data to train it on, what outputs were reasonable, where to use it, and how to profit from it. If a car had a fundamental manufacturing flaw, we would not blame the car. We would blame the automaker and hold them accountable. We should do the same for the creators of the AI.

Learning A/B Test

Now that we have finished the themes “What is learning?” and “How does learning work?” and entered the “What is AI?” theme for the course, you will apply what you have learned to observe and analyze your own learning. You will do this by running an A/B test as you learn LO8 through LO11. An A/B test is a user-experience research method where two variants (A and B) are experienced by either the same or different users to determine which variant is more effective. Of course, you know, at this point, that learning is much more complicated than what you can learn from a simple A/B test. The purpose of this experience is to start deliberately and carefully exploring how AI impacts your learning.

You will do this A/B test as follows:

1. There are two learning units in the “What is AI?” theme:
   1. Data visualization: LO8 and LO9
   2. Probability: LO10 and LO11
2. For one unit, you will commit to not using AI in any way, shape, or form to help you learn the LO’s for that unit until after the first LO checkpoints that test that LO.
3. For the other unit, you will plan to use AI to help you learn the material.

The units are as follows:

• Data Visualization
  • LO8 – Basics of data types, chart types, what kind of data types best match a chart type, how to read a chart, and common ways charts can be ineffective or misleading.
  • LO9 – Create charts using Excel and how to convert/transform data in a spreadsheet into a format that enables you to create chart you want.
• Probability
  • LO10 – Basics of probability, such that given a scenario, you can calculate the probability of that scenario happening.
  • LO11 – Conditional probability, such that given a conditional probabilistic scenario, you can calculate the probability of that scenario happening.

Additional Optional Readings if you are interested

• Does AI understand? By Sy Boles

References

The following text was written based on the following materials:

1. Narayanan, A., & Kapoor, S. (2024). AI Snake Oil. Penguin Press.
   1. Vehicle metaphor came from this book
2. Bender, E. M., & Hanna, A. (2025). The AI Con: How to Fight Big Tech’s Hype and Create the Future We Want. Harper.
   1. Much of the original terminology is from this book, and the discussion of anthropomorphization
3. Bender, E. M., & Hanna, A. (Hosts). (2023–present). Mystery AI Hype Theater 3000 [Podcast]. Distributed AI Research Institute.

Learning requires motivation. Otherwise, we wouldn’t learn anything. By better understanding how people are motivated, we can find ways to improve our motivation to learn something. Improving our motivation can be as simple as adjusting our thoughts, plans, and attitudes, or it may involve discussing with peers or teachers to find reasons that resonate with us and can then be used as motivation.

This video is a very short overview of Self-Determination Theory. I recommend you watch it first to get the gist before you dive deeper: Self-Determination Theory Explained by Psychology Exposed

However, the video glosses over some crucial details, predominantly the self-determination continuum. Therefore, next, read the sections below from Self Determination Theory and How It Explains Motivation by positivepsychology.com. This resource is more geared towards teachers. However, it is still a helpful resource for you as a student because, in some ways, you are your own teacher.

1. Introduction
2. What is the Meaning of Self-Determination Theory? – The video here is optional, and skip the Self-Determination Theory Questionnaires section
3. Self-Determination Theory and Goals
4. How to Promote and Encourage Self-Determination Skills

If you want a video overview of self-determination theory that is between the above video and reading, you can optionally watch 3 Basic Needs That Drive Your Behavior [Self-Determination Theory] by Sprouts.

Understanding cognitive load theory will help us better understand the limits of our brains when it comes to learning tasks. By applying this theory, we will be more likely to recognize when a task is cognitively demanding, assess whether that demand is reasonable, and consider ways to restructure the task to enhance our learning, rather than having our cognitive limits reached, resulting in very little learning.

Read Sources of Cognitive Load by learningscientists.org

Read Factors Of Effective Note-Taking: Application Of Cognitive Load Theory by learningscientists.org

Notice how in the note-taking article, it is essential to consider context when deciding what kind of note-taking to do. The advice to “take notes by hand on paper” is not actually backed by all research studies. Instead, we should take into account the cognitive demands of the note-taking task and consider the course itself. Is the lecture very fast-paced, but also recorded? Perhaps it would be better to take minimal notes live and plan to revisit the content by watching the lecture recording, where you can pause as needed to create more comprehensive notes, which also then gives you spaced repetition of the content.

Finally, we can use the framework of intrinsic, extraneous, and germane cognitive load to also help us judge whether help is germane to a learning objective. If that help is part of the extraneous load, it is less likely to be germane compared to the intrinsic load. This identification can then lead us to find ways to reduce that extraneous load by modifying our environment, leveraging tools (including AI), and seeking assistance from others.

Final note: Germane load used to be considered its own category next to intrinsic and extraneous. However, more recent work has shifted germane load’s definition to be the ratio between intrinsic and extraneous. So be careful in what resources you use because more general media have not yet adopted this shift, and the training data of most AI has a lot more data with the old definition than the new one. Case in point, if I ask DukeGPT to define it, and it gives me the old germane load definition. When I direct ChatGPT to check the internet when defining and providing citations (internet search is not a DukeGPT feature yet), it also uses the old definition, but then adds recent developments and caveats that do mention the new definition. In this class, we use the new definition.

To make things clearer, this will be the last time we use the phrase “germane load” in the class. From now on, we refer to it as “germaneness” to make it clear that it is not a kind of load.

Watch: Self-Regulated Learning Explained: How to Become Your Own Teacher by Powerful Learning

Then look through at least two of the following learning strategy resources:

1. Learning STEM at Duke
2. Five Study Strategies by Duke Academic Research Center (ARC)
3. College Reading Tips by Duke Academic Research Center (ARC)
4. How Do I Use Past Exams? by Duke Academic Research Center (ARC)
5. Infographic on Retrieval Practice by The Learning Scientists
6. Infographic on Spaced Practice by The Learning Scientists
7. How To Take Notes In Class by The Learning Scientists

Learning requires your brain. However, the brain does not fully develop until approximately age 25. Since a typical undergraduate student is 18-22 years old, it is crucial to understand what skills and brain development are currently occurring while the student is in college. A vital set of skills closely related to brain development is executive function skills. Executive function skills are the attention-regulation skills that help someone achieve a goal. There are many parts to this skill set, including maintaining focus on the goal, gathering relevant information, developing a plan to achieve the goal, adhering to the plan, resisting distractions, tolerating frustration, and considering the consequences of various decisions in relation to the goal.

While undergraduates possess adult-level capabilities in many areas, their executive function skills may be inconsistent, which is normal. Understanding these skills and that they are developing will help you recognize when learning challenges are part of normal brain development versus when you might benefit from adjusting your learning strategies or seeking additional support/guidance.

Required: Teens can have excellent executive function — just not all the time