If you haven't seen Anthropic's AI in Education report, it is a must read for higher ed profs. A few highlights: 🧵 First, we see different adoption rates among students by discipline. STEM high, humanities low. This tracks researchers as well. Humanities is largely anti-tech.

My department asked me to give a brief talk about #GPT to them to help folks learn more about what's going on. Here are my #tips in distilled form. 1. Make #GPT part of the classroom. Students know about it, students are using it, bring it into the discussion. Don't pretend it doesn't exist!

Curious if this analogy seems useful or misleading for people. Just emailed a student with this advice and thought I would share more widely. On how to organize writing in "cultural analytics". 🧵 2/ The main thing to focus on is asking yourself what are the primary contributions that you want to make to your chosen area of cultural behavior? If you had to boil everything down to a few salient points, what would they be?

Funny the @nytimes covered this today, but I had an honest conversation with my students today about #chatGPT. 🧵 2/ I showed them how it can be used to answer questions they don't know the answer to. For example, I asked them to explain "type-token ratio" which was discussed in an article we were reading. They went silent. So I let #chatGPT do the talking.

in my #AI and #Literature class we will be spending the next 2 weeks diving into AI co-writing tools. My first impression is that the world just cleaved in two. 🧵 2/ People are right to doubt the hype *right now*. The tools are limited in terms of actual quality. This is what we aim to explore in class. How "good" are these things at which tasks?

1) For those who didn't see it here is a brief thread on our new piece out on "Narrative Theory for Computational Narrative Understanding" with @dbamman and @RichardJeanSo. people.ischool.berkeley.edu/~dbamman/pubs/… 2) The motivation for writing the piece was that we were seeing all this great new work in NLP aiming at understanding narrative.

1) So in addition to being kinda upset by @netflix's The Chair (see previous tweet), I really do just see a really straightforward fork in the road for the future of literary studies. + 2) Door #1 says we can choose to do whatever it is we have been doing for the past two decades where we have only seen decline. +

1) here is a summary of a new paper I have out with Sunyam Bagga on measuring bias in literary classification. txtlab.org/2021/02/measur… 2) the goal of the paper was to see how much biased training data might impact the automated classification of texts. We use the prediction of "fiction" as our case study since it is something we are often trying to do!