AS.001.219 · Fall 2026
Five assignments designed to build knowledge, practice analysis, and develop a critical relationship with AI tools.
Group project (2–3 students). A 5–7 minute video exploring a Baltimore site through the lens of course themes. Due: Week 4 (Sep 24). Screened in class Week 5.
Individual project. Measure or make progress on a question of your choosing. Presented in class in early December; written report due December 8.
Handwritten in class at the start of most sessions, in response to a prompt posted to Canvas beforehand. AI may be used to understand the reading at home; the written response itself is yours alone.
Active prepared participation throughout the semester, plus one discussion leadership session (in pairs).
A short written piece in Week 1 and a closing piece in Week 13. The course begins and ends with the same question — and your answer may change.
"The most important things to understand about progress are not in textbooks. They're on street corners."
— Course premiseIn groups of two or three, you will produce a 5–7 minute documentary-style video exploring a Baltimore site through the lens of this course. Progress — economic, scientific, social, artistic, or urban — is not an abstraction. It happened in specific places, by specific people, and left visible marks on the built environment.
You will choose from a curated list of Baltimore sites. No two groups may select the same site. Groups are formed and sites confirmed in the first week of class. See the Baltimore page for the interactive map of all sites, with background on each location.
Videos are screened in class in Week 5 (around October 1). Each group gives a 5-minute live introduction before the screening, followed by class discussion.
| Component | Weight |
|---|---|
| Content and analysis | 40% |
| Connection to course readings | 20% |
| Production quality | 20% |
| Live presentation | 10% |
| Individual reflection note (submitted separately) | 10% |
Due dates: Groups and sites confirmed Week 1 (Sep 3) · Final video submitted Week 4 (Sep 24) · Screening Week 5 (Oct 1).
You will choose one of two tracks and pursue a focused empirical or practical inquiry into a question about progress. The project culminates in a 10-minute in-class presentation and a written report.
Gather data to answer a specific empirical question about progress (or its absence). Has air quality improved in Baltimore neighborhoods over the past decade? Has a particular policy reduced X over time? What does the evidence say about a trend you care about?
Your report must include: a clear research question, a methodology note (how you gathered and analyzed data), evidence and data (presented in at least one chart or table), analysis (what the data shows and what it doesn't), and a conclusion.
Do something concrete to create progress on a small scale. Document it with data. Analyze what you learned about how change actually works. Examples: organize a community initiative, build something useful, run an experiment, implement an improvement in an organization you're part of.
Your report must include: what you tried to do and why, a documentation of what you did (with data where possible), an honest account of what worked and what didn't, and a reflection on what this taught you about progress.
Due dates: Research question confirmed November 3 · Presentations December 1–8 (assigned slot) · Written report due December 8.
At the start of most class sessions — at least once a week, often twice — you will spend the first 5–8 minutes writing a handwritten response to a prompt posted to Canvas before class. These are collected and graded as part of your course record.
The prompt will be posted to Canvas (and sometimes to this site) before class. You should arrive having done the reading and having thought about the prompt — but the writing itself happens in the room, on paper, without any tools open.
These responses give Simon D. Halliday a window into how you think and write, unmediated by editing tools or AI. They are not graded on polish — they are graded on genuine intellectual engagement. A messy, honest response that takes a real position is worth more than a tidy one that says nothing.
You are fully permitted — and encouraged — to use AI tools to help you understand the readings before class. If you do, you should upload a brief record of that interaction to Canvas alongside your other work for that week: a screenshot, a copied conversation, or a short note describing what you asked and what you got. This is not punitive — it is a way of making AI use visible and reflective rather than invisible and unconsidered.
The handwritten in-class response is the one place in this course where the work is unambiguously yours. That is the point.
This is a 12-person seminar. Your presence and engagement are not incidental — they are constitutive of the course. The quality of discussion depends entirely on everyone arriving prepared and willing to think out loud.
Discussion leadership: Once during the semester, you will lead discussion in partnership with one other student. You will design two or three discussion questions based on the week's reading, open the discussion, and help facilitate it for the first 20 minutes of class. A brief one-page discussion plan is due the evening before your session.
Participation is assessed holistically at the end of the semester, based on sustained engagement across all sessions — not performance on any single day.
In the first class session (September 1), you will write a short, ungraded free-write in response to the question:
"What do you think the world's biggest unsolved problem is, and why?"
This piece will be returned to you at the end of the semester. In the final class session (December 10), you will write a closing reflection that revisits your opening answer: how may your thinking have changed? What do you see differently? What do you still believe?
The closing reflection (400–600 words) is submitted on Canvas and is graded on thoughtfulness and intellectual honesty. There is no "right" direction for your thinking to have changed.
AI tools are permitted and encouraged in this course. Use them to understand difficult readings, explore ideas, get feedback on drafts, and push your thinking further. Assignments are designed so that thoughtful AI use improves your work — not so that it replaces the thinking you need to do yourself.
The course has one deliberate AI-free zone: the in-class reading responses. These are handwritten in the room because they are the one place where Simon D. Halliday gets to see how you think without any mediation. Everything else is open.
For the Baltimore Video and Data Project, the AI log requirement makes AI use transparent and reflective rather than invisible. You should be able to say: what did I ask, what did it produce, and what did I add or change? For reading responses, if you used AI to prepare, upload a brief record of that to Canvas — a screenshot or a short note is fine.
The standard across everything in this course is not "did you use AI?" but "did you think?"