Syllabus
AS.001.219 · Fall 2026
Syllabus
FYS: Progress — Why now is the best time to be alive (and how we could lose it)
Twenty-six sessions across six modules. Tuesdays and Thursdays, 10:30–11:45 AM. First class: September 1. Last class: December 10.
Course information
Grades
| Assignment | Weight |
|---|---|
| Baltimore Progress Video (group) | 30% |
| Progress Data Project (individual) | 30% |
| In-class reading responses (handwritten, start of class) | 15% |
| Participation & discussion leadership | 15% |
| Opening & closing reflection | 10% |
Full descriptions of each assignment are on the Assignments page. AI use is expected and encouraged — each assignment includes an AI log component.
Key dates
What Is Progress?
We begin with the data. How much has the world actually improved, and how do we know? We then ask what we should even be measuring — and whose definition of progress counts.
Institutions & Innovation
Progress does not happen in a vacuum. Institutions — the rules, norms, and organizations that structure economic life — shape who innovates, who benefits, and who is left out.
Who Benefits?
Progress is uneven. We look at both how much has improved — especially for groups historically excluded from prosperity — and where serious gaps remain. Income, wealth, representation, health: the picture is complicated, and the data matters.
Ambition & Agency
Does progress flow from exceptional individuals or inevitable structural forces? What is the role of ambition, luck, and the systems that enable or suppress it?
Limits & Risks
Progress can stall, reverse, or create new problems faster than it solves old ones. Climate change, pandemic risk, and technological disruption test whether the optimist story holds.
Looking Forward
Students present their Data Projects and the seminar closes with reflection and celebration. What has changed in your thinking since September 1?
A note on AI use
I will use, and have used, AI in this course. AI helps me design the website, create assignments, write session notes, find data, think about readings in ways opposed to the way I think about them, and in many other ways that are hard to describe and name. Moreover, given the current milieu, I think it would be unethical for me not to use AI to provide you with the best course I possibly can, while also retaining strong intellectual ownership over the course's contents and its trajectory. Similarly, I think it would be misguided for you not to use AI.
At the same time, it is crucial that you nonetheless learn how to write well, to develop taste, to hone your intuition, to develop a strong bullshit detector. For all of these, you need to read without AI. You need to write without AI. You need to sit and pause and write and think and reflect alone. You need to develop practices for using AI wisely and for clarifying when its use is inappropriate for your own development as a thoughtful and moral person and citizen.
A variety of pieces have informed what I think about this topic, and I have included some of them below.
- Noy, Shakked, and Whitney Zhang. "Experimental evidence on the productivity effects of generative artificial intelligence." Science 381, no. 6654 (2023): 187–192.
- Mollick, Ethan, and Lilach Mollick. "Assigning AI: Seven approaches for students, with prompts." SSRN Working Paper, 2023.
- Lee, Hao-Ping (Hank), Advait Sarkar, Lev Tankelevitch, Ian Drosos, Sean Rintel, Richard Banks, and Nicholas Wilson. "The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers." In CHI Conference on Human Factors in Computing Systems (CHI '25), April 26–May 1, 2025, Yokohama, Japan. ACM. DOI: 10.1145/3706598.3713778.
- Bjork, Robert A., and Elizabeth L. Bjork. "Desirable difficulties in theory and practice." Journal of Applied Research in Memory and Cognition 9, no. 4 (2020): 475–479.
- Kapur, Manu. "Productive failure." Cognition and Instruction 26, no. 3 (2008): 379–424.
- Ericsson, K. Anders, Ralf Th. Krampe, and Clemens Tesch-Römer. "The role of deliberate practice in the acquisition of expert performance." Psychological Review 100, no. 3 (1993): 363–406.
- Hambrick, David Z., Frederick L. Oswald, Erik M. Altmann, Elizabeth J. Meinz, Fernand Gobet, and Guillermo Campitelli. "Deliberate practice: Is that all it takes to become an expert?" Intelligence 45 (2014): 34–44.
- Branwen, Gwern. "Spaced repetition for efficient learning." gwern.net, 2009–2024.
Consequently, I welcome and expect you to use AI tools thoughtfully in this seminar. Assignments are designed so that AI assistance produces better thinking, not a shortcut around it. Every major assignment includes an AI log: a brief account of how you used AI tools, what they produced, and how your own thinking differed from or built on the AI's output.
The goal is not to police AI use but to make it visible, so that you can develop a critical relationship with these tools, understanding both what they can do and what they cannot replace.