Learning Opportunities in AI Humanities
Each semester, the Lab offers several opportunities for students to get involved in our work, and learn about topics and methods at the intersection of AI and the humanities. If you are an undergraduate student interested in getting involved with us, please consider the courses listed below. We also sometimes offer internships and paid research assistantships in the lab. We are also always happy to work with interested graduate students in the digital humanities.
For more information, please reach out to Prof. Kirilloff (gabik@andrew.cmu.edu) or Prof. Carroll (ccarrol2@andrew.cmu.edu).
Spring 2027 Courses
76817 and 76417: AI Humanities Lab (Prof. Carroll and Prof. Kirilloff)
Everybody (including your mother) is using AI. While news media focuses on what that means for the economy, it also has major implications for literature, culture and the creative arts. This course is a project-based learning class hosted by 麻豆村’s new AI Humanities Lab that offers students practical experience in evaluating AI models through their application to research in literature, culture and social media. The course involves contributing to different aspects of the lab’s research portfolio–such as reviewing research papers, evaluating AI model outputs, producing data visualizations, and performing data analytics. Students will gain valuable, hands-on research experience, including project management, documentation and collaboration skills. The course is suited to students with data analysis and coding skills who want to learn how to apply those skills to questions about culture, humanities students who wish to learn more about the interdisciplinary field of computational humanities, and students from any discipline who wish to learn about the historical, cultural and social implications of AI. Participation in the course has no prerequisites–students will be assigned work based on their interests and skills.
76388/788: Coding for Humanists (Prof. Carroll)
This course provides students with the foundational knowledge and skills to develop and/or utilize computer-aided research tools for text analysis. Through a series of hands-on coding exercises, students will explore computation as a means to engage in new questions and expand their thinking about textual artifacts. This course is designed for students with no, or very little, coding experience. So, if you have already taken a programming course, this course is most likely not for you. Students who have taken 15-110 and/or 15-112 may not take this course. For the final project, you will develop a small research project involving a computational analysis of a corpus of texts. You will plan, design, and write a computer program that processes and analyzes a textual corpus of your choice. Students who are taking the course for 9-unit will write a brief project report (3-5 pages) that summarizes your final project. Graduate students in the MA in Rhetoric/PhD programs must register for 12-unit, and will complete a research paper (4,000-5,000 word). Prerequisites: 76-101 or 76-102 or (76-106 and 76-107) or (76-106 and 76-108) or (76-107 and 76-108)
76444/844: Predicting a Bestseller: Computational Approaches to Publishing Trends (Prof. Kirilloff)
Algorithms that predict the "success" of books are now commonly used in publishing (and also privately by writers and agents). The aim of the class is to use these tools to explore trends in fiction writing and publishing while also thinking critically about the limitations of implementing these tools in the literary marketplace (e.g., does it encourage homogeneity). We will use Jodie Archer and Matthew Jockers' The Bestseller Code: Anatomy of the Blockbuster Novel as our guide through this topic. We will close-read novels that both adhere to and depart from trends among bestsellers. Students will also gain a working familiarity with concepts in natural language processing and the programming language R. No previous programming experience is required.
Fall 2026 Courses
76-314: Data Stories (Prof. Carroll)
Every dataset has a story. In the age of big data, it is vital to understand the unlikely casts of algorithms, data miners, researchers, data janitors, pirates, data brokers, financiers, etc. whose activities shape culture. This course will explore cases such as the London cholera epidemic, Google Books, Netflix, the Oxford English Dictionary, the Strava map, and the Queen Nefertiti scan alongside several pieces of art and fiction that capture aspects of data stories typically obscured elsewhere. Research methods introduced will include book history, media archeology, history of information, infrastructure studies, ethnography, narratology, and digital forensics. Students will read scholarly articles, novels, journalism, and popular non-fiction; they will test algorithms; and they will develop individualized long-form research and writing projects informed by computational methods in data studies, journalism, and art.