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# System prepended metadata

title: mk-notes-on-hist-ai-seminar-20231214

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# mk-notes-on-hist-ai-seminar-20231214



## presentations

### Goel and Levy (HKS)

- "StatGPT"
- [Stats API-201](https://www.hks.harvard.edu/courses/quantitative-analysis-and-empirical-methods) at HKS.
- key elements/goals
    - guiding through problems rather than giving answers
    - providing always-on resource
    - consistently supportive and non-judgmental
    - interacting in preferred language
- also Slack DMs
- logs a key feature
- Dan asks
    - easy to use for faculty and teaching team
    - easy to monitor student use and learn from it
    - easy to tweak in response to student use
- Dan used the custom GPT builder
- Q:
    - student-by-student customization mechanics?
    - QC on problems sent to students?
    - how did you handle the logs (quantity esp)?
- 3.5 triage and then on to gpt-4 if needed
- Chris S: interview profs (or fill in form) and then generate prompt from this
- Dan: if the student wants the answer, they can go to ChatGPT, so a lot depends on social capital building with students
- 

### Greg (FAS)

- TeachGPT
- Chris:
    - comparing to one of our best instructors... if this were me... etc
    - 
- Q:
    - one thing that comes across is the way certain assumptions about personhood determine one's thinking here (like when Dustin asks why 3 bots and Greg is saying "well, you could call them three or 1, just different system prompts")
    - so the Q => when to present one vs many personae to students? when is the mental model of personhood valuable to the learner? and is this a question for an engineer or someone used to crafting characters for theatre or film?
- next steps:
    - student tracking
    - query the data, look for students that are struggling, etc
    - simulations
        - build in vision
        - multimodal interactions
    - upload student handwritten work
- 

### Bill and Christian (HGSE)

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- How People Learn
    - students overwhelmed by amount of content
    - need for more personalized feedback
- LLaMa inference engine, eith
    - 78 LLMs including
        - mistral 7b
        - openchat_3.5
        - llama-2-7b
        - sqlcoder-7b
    - langchain
    - autogen
- streamlit for the interface
- Personae
    - talk in different ways for different audiences
- 

### Malan (SEAS)

- goal: provide with virtual office hrs 24/7
- text as ephemeral delivery system for logical structures
- answering questions without ego and/or judgment
- 

## next steps

- send updates to Nick etc
- 

