# 20250407-ai-lab-planning

## projects

- materials from last AI lab
- MK/MW on cam
- record list of ideas
- ideas of stories/posts
- tool/tutorial/link requests
- Slack & Instagram schedule
- ~20-50 slacks a week
- ~3-5 posts a week for instagram
- anything we can connect to 1189 or other upcoming projects
- prep materials/tools for Madeleine's visit to GenEd 1189
- scrb 50 visit
- plan and tools for Slack
- prep for Friday AI Lab meeting
- tools for thinking
- tutorials
- concepts
- context for next design challenge
- news
- in ed
- in ai
- in research
- activity/designLab portion
- related to design challenge
- all of chris's asks
- projects on horizon
- ai-augmented editing
- intro/title sequence (louisa)
- trailer (taleen)
- mod-heb
- language center
- showcase
- Slack launch
- prep Madeleine for Friday panel
- largely ideas and notes
- prep Karen for Saturday panel
- slides & ideas

## notes
- post-mortem summary of what happened in [bok-ai-lab-20250404](/7uW4cvl7Rz-xz9IWNkTJ5w)
- prep for next ai lab in public facing doc [bok-ai-lab-20250411](/6JT6hNmsTbSGCbuXZjkYVQ)

good for structure to mirror the assets we need (tools for thinking, news, things that people are making)

## brainstorming notes

### Saturday & Sunday:
* slack news and notes, tools etc. (MK, KH, EW?)

### Monday:
* mk+mw: review post-mortem notes in hackmd of what happened and/or if there's time, on camera reflection of what happened
* dd: start doc for the upcoming friday
* by end of day: Post on Bok Instagram about previous week's Bok AI Lab
* slack (insert details)

### Tuesday:
* planning for things ahead:
* look ahead at what's upcoming
* look back at what we didn't get to
* determine which things we might want to focus on for the week ahed
* see how it connects to projects and research
* slack (insert details)

### Wednesday:
* firm up what we will focus on for upcoming lab
* deadline: Post on Bok Instagram about upcoming week's Bok AI Lab
* slack

### Thursday:
* finalize plan for next day
* slack

### Friday:
* Bok AI Lab happens
* then immediate post-mortem summary of what happened
* if there's time, on camera reflection of what happened
* slack
* summary of the week
* summary of the lab (automated)

## schema for understanding the above

- some days have specific events
- some days have specific deadlines
- each day involves human input to slack
- each day involves ai input to slack
- each day involves automated ai digestion/synthesis of slack and external sources

## GenEd 1189 plan

* Flow:
* First, parsing of the images from the future protocol
* mw: "I wasn’t even here, but here’s all the insights I could glean..."
* New poll: “What do you think the purpose of education is *now*?”
* When you think about AI, dont plug it in
* This is why technology hasnt fundamentally changed education
* We approach it technology-first
* But it shouldn't be how does AI impact education, it should be how does education impact AI?
* Think very deeply about "the thing" first, then see where the gaps are
* Have student work on current "pain points"
* List them out on cards, clustering
* GPT creation
* They try to solve this problem
* Design norms (rosie v roomba)
* prompting
* Finale
* Show some GPTs
* Parse the csv of the initial poll in a web app in some cool way

## SCRB 50 plan
optional: really instructive examples of gpts that show differences in quality (like one that doesn't have any assets for retrieval)

## AI@FAS plan

## STS talk plan

mw rhetoric:

* ethos of mw as an inbetween for faculty and students
* skill gap between students, classes, departments
* students are negotiating *themsleves* what the line is
* at the LL, we've helped with this for awhile, helping them parse what they are being graded on in speciifc courses
* (like, when you get help with styling your CSS for a poetry project, but not the poetry)
* plus insight as a tutor w/ how students are doing this alone, in dorm rooms
* "we’re not there as the representatives that are in control of [policy]. We do have an interesting window on what professors doing right now in classes..."

---

Thank you again for agreeing to participate in our roundtable, "The University in Crisis? Smart Education in the Age of Artificial Intelligence" hosted by the Program on Science, Technology and Society's Undergraduate Fellows. I'm writing with some more details about the event. We will be holding two panels over the course of the event (1 hour each). We are hoping each person will offer 5-minute introductory remarks before cross-panel discussion and audience Q&A. Please find below some guiding questions for your panel and a brief program, and please feel free to let us know if you have any questions! Looking forward to seeing you next week (Friday, 4/11 at Harvard Kennedy School, Malkin Penthouse, Littauer Building) — thank you very much.

Roundtable Details

1-2pm: States of Learning

Panelists: Professor Bharat Anand (Business Administration (HBS), Vice Provost for Advances in Learning) ; Director Jane Rosenzweig (Director of Writing Center, Expos Preceptor) ; Professor Jim Waldo (Computer Science and Public Policy, CTO for SEAS) ; **Madeleine Woods (Bok Center, Project Lead AI Initiatives)** ; Sam Weil (‘25, The Crimson) ; Peggy Yin (‘25, Conflux)

2-3pm: Citizens in AI and Action?

Panelists: Professor Rebecca Nesson (Computer Science, Dean of SEAS Academic Programs) ; Professor Amanda Claybaugh (English, Dean of Undergraduate Education) ; Professor David Lamberth (Philosophy and Theology, former Assoc. Dean of Academic Affairs of HDS) ; Serena Jampel (‘25, History & Literature, Harvard Magazine) ; Tommy Barone (‘25, Social Studies, The Crimson)

With the advent of ChatGPT and other similar LLMs, conversations across the university increasingly address how technology is changing or should change learning. Alongside developments in the classroom, LLMs have posed structural, institutional, and disciplinary challenges. Although LLMs and ChatGPT in particular find increasing use among students and have become integrated within the classroom by professors with a distinct ease and speed, the entry of digital technologies into the university has a half-a-century-long trajectory. Even as new technologies continuously raise new questions, we ask whether it might be more helpful not to focus exclusively on emerging technologies but to consider critically what it means for education and the university to be increasingly digitalized. How is Harvard framing the ‘crisis’ (or lack of ‘crisis’) relating to AI?

* What do you see as problems facing/plaguing education right now? Where does AI begin or how can we fit AI within these problem frameworks?
* What “feels” like a risk to you (ethically, scholarly, otherwise) in using LLMs like ChatGPT?
* How are LLMs being treated within your (classroom, discipline, school of the University)?
* Where are limits being drawn (or redrawn) regarding the usage of AI in student and professorial work?
* What intellectual work is being transferred to and transformed through AI, and what remains?
* What should we make of Harvard’s “responsible experimentation” policy? Where are we able to see what it is that AI is “doing” to education anyway – that is, what is made visible about education through a focus on AI?

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