# sociol99a-ai-workshop [slides here](https://docs.google.com/presentation/d/1iup0dZA4EJpco56bMsquoMrROtPq85FT_U6-dNILHfo/edit?usp=sharing) ## Outline * Intro: what's the lay of the land in 2025-26? * AI literacy: how does AI work and what does it get wrong? * AI research activity ## Introduction * How are students using AI? * Some statistics from [Harvard](https://arxiv.org/pdf/2406.00833) and [across](https://www.chronicle.com/article/how-are-students-really-using-ai) the [country](https://www.grammarly.com/blog/company/student-ai-adoption-and-readiness/) * A July 2025 Grammarly study of 2,000 US college students found that 87% use AI for schoolwork and 90% for everyday life tasks. * Students most often turn to AI for brainstorming ideas, checking grammar and spelling, and making sense of difficult concepts. * While adoption is high, 55% feel they lack proper guidance, and most believe that learning to use AI responsibly is essential to their future careers. * Discussion: how are you using it? * What is the landscape this year? * There are two AI tools that HUIT is supporting. Let's get you connected to them before we move on with the workshop! * here is your link to [Google Gemini](https://gemini.google.com/app) * and here is your link to [the HUIT AI Sandbox](https://sandbox.ai.huit.harvard.edu/) * **Important privacy note:** These HUIT-supported tools have built-in privacy safeguards. Harvard has contracts with these providers ensuring that anything you share won't be used to train their models or be shared with third parties. --- ## AI Literacy: How does AI work? Using AI responsibly starts with AI literacy. This means moving beyond what AI can do and exploring how it works and why it fails. In this section, we’ll focus on two key aspects of how AI functions: - **AI as a Statistical Machine**: LLMs process language as numbers rather than understanding it, leading to predictable errors that users can learn to anticipate and correct. - **AI as a Reflection of its Training Data**: AI models learn from vast amounts of human-generated data, absorbing and amplifying the stereotypes within it. --- ### Activity 1: Tokenization Paste the text below into [tiktokenizer](https://tiktokenizer.vercel.app/). ``` Unsurprisingly, they had to cancel the show. The crowd went home unhappily. ``` * Notice how the model breaks words into tokens. * Try putting in a sentence or a word with complex morphology in your language of choice * Discuss: What does this reveal about how AI “reads” text differently from humans? #### Takeaway AI doesn’t “read” words like humans do. It breaks text into tokens—numbers representing pieces of words. This shows that LLMs process language as math, predicting the next number in a sequence rather than reasoning about meaning. --- ### Activity 2: Multiplication- Predicting vs. "Reasoning" **1. Prompt (for Gemini Flash or Llama 3.2 11b oro older model):** ``` 82,345 × 67,890. give me an immediate response without using code. ``` * Try it yourself first → you’ll see it’s hard to do “in your head.” * See how the AI answers. * Does it get it right? If it's wrong, is it *completely* wrong or close? how? **2. Prompt (for Gemini Flash Thinking or GPT-4.1 Reasoning):** ``` 82,345 × 67,890 ``` * Compare this to when you asked for an “immediate response”. * Does the model get the math right this time? * What’s different about the *style* of its response? #### Takeaway AI doesn’t actually *calculate*—it predicts the next token (number) based on patterns in training data. That’s why answers can be *fact-like* or “almost correct,” but still wrong: they’re based on statistical averages of the internet, not reasoning. AI tools increasingly offer **“thinking” modes** (sometimes called *chain-of-thought* or *reasoning* models). Reasoning models still predict, but showing their work lets you spot errors and better trust or question their output. Asking the model to “think step by step” can improve reliability and helps you check its work. ### Improved Reasoning with RAG: [NotebookLM](https://notebooklm.google.com/)'s' strength is its ability to transform the same source text into different formats, which allows users to engage with source material in a more interactive way. **Try this with a short text (e.g. news article, short story) in the language you teach:** 1. Upload a document into a new NotebookLM notebook. 2. In the Sources view, quickly skim the auto-generated summary and key topics to ensure NotebookLM has grasped the main points. 3. In the Chat box, ask NotebookLM specific questions about the source. 4. You can also create podcasts and other materials. --- ### Activity 3: Ethical Error — Bias in Images Image generation models (like Gemini, DALL·E, or Midjourney) work by sampling from high-dimensional probability distributions conditioned on a prompt. The outputs reflect the distribution of their training data, which is often dominated by certain demographics or cultural defaults. As a result, even seemingly neutral prompts (e.g. “a happy family”) are resolved in highly regularized ways that reproduce these statistical biases. **Prompt an image generator:** ``` Create an image of a happy family ``` or ``` Create an image of a happy couple ``` * Compare the outputs to those sitting next to you--what patterns do you see? What kinds of people or relationships appear most often? * What patterns or omissions do you see? What’s the risk of using these images in class slides for instance? [More examples here →](/pvNaRf56T7qhOqx1GUlcrA) #### Takeaway Generative image models do not “choose” representations; they maximize likelihood based on patterns in skewed datasets. Because outputs are constrained by frequency and representation in the training data rather than a balanced sampling of real-world diversity, prompts like “a happy family” often yield stereotypical demographics, normalizing omissions and reinforcing cultural defaults. --- ## AI Research Activity For the second half of today's workshop we're going to do a couple of hands-on activites in pairs. The goal is give you a chance to use AI to test and evaluate its effictiveness as a research tool. At the end of the activities, we invite you document which kinds of applications and methods felt more/less useful, reliable, ethical, etc. --- ### Activity 1: Crash Course into a New Area of Research For this activity, we'll break up into pairs. Ideally, you'll be paired with someone whose thesis topic doesn't overlap too much with your own. Once you're in pairs: 1. Identify a term, theory, person, etc. that you don't know much about, and 2. Prompt an LLM to help you gain enough expertise with it to engage with a scholar working on it. Follow up with whatever prompts seem to take the chat in a productive direction. 3. After about 5 minutes or so, take turns presenting your chat to your partner. After the presentations, we'll debrief as a group. --- ### Activity 2: From Theory to Variable Many social scientists start with an abstract construct—like trust, social capital, or autonomy threat—and need to operationalize it. 1. Pick a social science concept that interests you. 2. Ask an LLM to help you identify ways to measure that concept quantitatively. (e.g., survey items, behavioral indicators, index structures, etc.) 3. Then prompt the AI to justify or critique those measures using relevant theories or classic studies. As a group, we’ll discuss what assumptions the model made and consider how plausible or biased they were. Example prompt: ``` Suggest three ways to operationalize trust towards AI agents. Cite key works that have used similar measures. ``` --- ### Activity 3: Vibe-coding Text Processing Tools For this activity, we’ll be working together in [Google Colab](https://colab.research.google.com/), with the goal of “vibe-coding” a simple text processing tool you could actually use in your research. We’ll walk through the process of taking your own PDFs or text files, extracting the contents, and using Python (with the help of the NLTK library) to search for keywords—including different forms of those words. Example prompt: ``` Help me build a Python program that allows me to upload a .txt file, and uses NLTK to process the file. Make sure to used punkt_tab instead of punkt. The program should then let me enter a keyword, and extract the 20 words before and after any form of the keyword (i.e., matches on lemma, not just the exact word). ``` You can use your own .txt file if you have one handy, or download one from websites like [Project Gutenberg](https://www.gutenberg.org/ebooks/bookshelf/691). You can also get Gemini to help you tweak the code according to your needs, and see how you can customize it for your particular research questions or data. [Sample Colab notebook](https://colab.research.google.com/drive/1n_26uMBc-4H9Ij_dYZCHaiUbWabt6-EF?usp=sharing) --- ### Activity 4 (If Time Allows): Model Comparison Present a research question and two different analytic approaches (e.g., multiple regression vs. logistic regression; linear vs. polynomial). Ask the AI to help you reason which method fits better and why. As a group, we’ll discuss whether the AI justified its choice appropriately. Example prompt: ``` “If I’m studying whether students’ AI literacy predicts their likelihood of adopting new learning apps (binary outcome), which model should I use and why?” ``` ----- ### Stay connected! Feel free to reach out at generativeAI@fas.harvard.edu for any AI-related inquiries. A calendar of events and workshops can be found [here](https://bokcenter.harvard.edu/generative-ai-events).