• Identify instances of manipulated or misleading data

• Develop skills in data analysis and visualization using R

• Critically evaluate metrics and numbers used in everyday decision-making

• Collaborate effectively on data-driven projects

• Communicate data insights through various formats (presentations, visual displays, written reports)

Adrienne Kinney (she/her) a.kinney@bowdoin.edu 

Office hours in person: Mondays 2:15-3:15pm, Wednesdays 4:30-5:30pm, and Fridays 10-11am. No appointment necessary for these hours! Just stop by whenever you can.

If those hours don't work, you can always schedule a 1:1 appointment.

Office: Dudley Coe, Room 110

Vianney Gomezgil Yaspik (she/her) v.gomezgilyaspik@bowdoin.edu 

Office hours: Tuesdays 2-3:30pm & always by appointment

Office: Mills Hall 211

Hear my name 

Grace Kinum  - Sundays 7-9pm

Kate Saccaro - Mondays 7-9pm

Henry Ladd - Tuesdays 7-9pm

Room: Mills 105

Assignments & Grading Structure

• • Number Presentation: 15%

  • Dear Data Activity (10 weeks): 20%

  • Quizzes (10 weeks): 25%

  • Final Project: 25%

  • Participation and Engagement: 15%

You can request two excused absences due to health-related issues. If you need more accommodations, please, reach out to your academic dean, and they will work with you and your instructors on the best strategies to address your needs.

Since our in-person meetings are core components of the course pedagogical strategy, 2 percentage points will be deducted from your final grade for each unexcused absence. To be clear, absences beyond the first two can only be excused by the dean’s office. If, for example, you tell a professor that you will be away for the next class and they say “ok”, they are not excusing your absence, they are merely acknowledging it.

Each student has two "extra days" that can be used at any point during the semester for assignment extensions (other than the Final Project and Presentations). These days must be requested by notifying your instructor before the assignment deadline. No questions asked - simply communicate your need to use one of these days.

After using your two extra days, or for assignments submitted late without prior notification, the following penalties apply:

• Up to 24 hours late: 10% deduction

• 24-48 hours late: 25% deduction

• 48-72 hours late: 50% deduction

• More than 72 hours late: Assignment will not be accepted unless a note from the Dean is provided

What counts as Generative AI?

Generative AI encompasses all of the artificial intelligence systems that can create new content like text, code, images, audio, and other types of media. This includes but is not limited to:

• Large language models (ChatGPT, Claude, Gemini, etc.), apps and agents derived from them, and aggregator interfaces (Amplify, LibreChat, etc.)

• Code generation tools (GitHub Copilot, Google Colab, Cursor, etc.)

• Image generators (DALL-E, Midjourney, etc.)

• AI-powered writing assistants and autocomplete features

• NotebookLM, Quizlet AI features, and other AI-enhanced study tools

Always acknowledge what generative AI tools were used to help you complete assignments.

How can generative AI help you to learn in this course?

• Converting slides and notes to study guides and podcasts (i.e. NotebookLM)

• Creating practice materials like flashcards (i.e. Quizlet) and mock quizzes or exams

• Proposing a study plan

• Providing alternative explanations or examples

• Suggesting refinements to grammar and corrections to spelling

• A guide to debugging code

How can generative AI impair your learning in this course?

• Reduces critical thinking and problem-solving abilities - overreliance on AI for analysis can prevent you from developing your own reasoning skills and prevent you to work through problems independently

• Can limit creativity

• Creates a sense of false confidence - AI-generated work almost always seems correct but can contain small errors or miss a deeper understanding

• Creates dependency 

• Reduces retention

• Limits research skills

• It denies you the possibility of self-expression in a welcoming and constructive learning environment.

• For coding-intensive projects, AI tools often miss the layers of design that go into solving a problem and instead produce a “one-shot” solution that is hard to understand and debug

Other costs of using AI

• Environmental impact - AI systems use very large amounts of energy to train the models and operate the apps and interfaces

• Labor exploitation - AI development more often than not relies on poorly paid workers in difficult work conditions for tasks such as content moderation, data labeling, etc. 

• Digital colonialism - most AI systems have been created by developed Western nations, which can potentially reinforce global inequalities and impose Western perspectives on other cultures/populations severely limiting your exposure to innovative and challenging ideas

• Digital divide - AI tools are widening the gaps between those with and those without access to advanced technologies

• Aggregation - AI is concentrated in the hands of a few large companies who are gaining outsized influence

• Privacy - Things you put into Generative AI systems often are fed into those systems. The history of AI companies suggests that despite their disclaimers your information may be kept private.

When should generative AI be used along with other tools?

• Finding resources to support your research 

Also use Bowdoin’s Compass and Google, for examples

• Testing discussion questions that you would like to pose to the class

Compare AI results to what you hear from classmates and the professor

• Checking your work and getting preliminary feedback

Use AI as a first pass, then seek “human” feedback from your classmates, LAs, or office hours

• Outlining

Create the initial “ingredient list” yourself, then generate initial structure with AI, then develop your own unique perspectives and arguments

Why do we give you assignments?

“We don’t assign essays because the world needs more student essays.” - Emily Bender

Assignments are designed to help you develop essential skills and ways of thinking that will serve you throughout your transformation as a student/learner. Think of each assignment as an opportunity to: practice critical thinking, develop your unique voice, learn from struggling, engage deeply with the course material (you might find your passion! or not…), demonstrate your learning, and more importantly prepare for future challenges, skills/tools/programs might change but developing deep critical skills will help you continue to adapt as tools change throughout time. 

When is generative AI not allowed?

• To write - Please avoid autocomplete as well. We want to hear your voice, not the most probable internet-based voice based on a temperature setting determined by programmers who have never taken our courses or studied at Bowdoin.

• To code - Unless stated otherwise, write the initial code yourself and use GenAI to help you debug.

• To brainstorm - Your perspectives are broader than this course, your background is richer than what LLMs can represent. Practice creating and evaluating new ideas.

• To solve problems - The problems presented in this class are designed to help you practice a process of critical thinking and evaluation. Taking a shortcut here only robs you of the opportunities to cultivate existing skills and learn new skills.

• As a search engine for “facts.” At the time of writing this document, generative AI tools have not demonstrated 100% reliability here. Always confirm.

• To analyze data or interpret results without your own critical thinking

• To generate ideas for creative projects or original research questions

• To complete any type of exams or quizzes unless explicitly permitted

• To translate assignments or course materials into other natural or formal languages without permission

What you can expect from your Professors

• We will design assignments and assessments that prompt you to assess and demonstrate your individual strengths

• We will read and evaluate your assignments using our own expertise and judgement

• We will clearly disclose any AI tools we use for course preparation (such as generating synthetic datasets)

• We will provide our personal guidance and support during office hours and email

Academic Integrity Expectations

• Be transparent about AI use 

  • When in doubt, mention its use

  • Submit your prompts with your assignments

• Understand that overreliance on AI will hinder your learning and skill development

• Violations of this policy will be treated as academic dishonesty

No screen use is permitted in class unless explicitly stated by the instructor or in cases of learning accommodations. This includes phones, tablets, and computers. Most courses will display a symbol during presentations that indicates when screen use is allowed. When screens are not permitted, all devices should be put away and out of sight. 

If you have a documented accommodation that allows the use of a laptop or tablet for note-taking, please make sure to submit the appropriate materials to the professor during the first 2 weeks. If you are interested in learning more about accommodations, please see Lesley Levy in the Office of Student Accessibility, https://www.bowdoin.edu/accessibility/. 

Similarly, reach out to your Professors about religious holidays so that alternative timelines for deadlines can be provided. 

Overview

Throughout the semester, students will collect personal data using a tool of their choosing to track their own digital behavior: screen time, specific apps used, websites visited, or other self-tracked metrics related to course themes. Students may make their tracking as specific or as broad as they like. The goal is for students to arrive at the end of the semester with an in-depth record of their own data use: what they are generating data about, when, and how. Most students find that the volume and frequency of their data is far greater than they assumed going in. The final project asks students to reflect on this directly. 

Data Collection

For the data collection process, students:

1. Choose a tracking tool and a metric, and confirm their choices with the instructors by week 3, September 15, 2026. Students who prefer not to collect personal data, for any reason, may choose an alternative data source instead, approved by the instructors by the same date.

2. Track continuously from week 3 through the last week of class, recording at a consistent daily or weekly interval.

3. Keep a running collection log: what they decided to count, what they decided not to count, and every gap or interruption in the record.

This data belongs to the student. Raw data is never presented in front of the class or submitted to the instructors.

Format

The final project has no fixed format. Students may present their data and reflections as a presentation, an essay, a song, a video, a mock marketing campaign, or any other format of their choosing. This openness is intentional and reflects the spirit of the course!

The final submission is expected to contain:

• The student’s tool, metric, data, and coding methods, presented in a form the class can follow.

• An account of the collection methodology, missing data, and exclusions, drawing from their collection logs.

• An interpretation of the findings, highlighting at least three course concepts (bias, representation, metrics, visualization). What did students learn? What was more than they expected? What was less? What surprised them, and what didn't? Whose perspective is represented and whose is missing?

Timeline

• Week 3: Tool and metric confirmed with instructors.

• Last two weeks of class: Students present their project in class and receive feedback from instructors and peers.  Presentations should be 5-10 minutes per student and will count toward the participation grade.

• End of exam period: Students submit their final deliverable to Canvas, revised based on in-class feedback. This is the graded submission.