Workshop Overview
In this team-taught workshop, we invite scholars to join us in exploring the relationship between generative AI and the future of programming pedagogy in the digital humanities and a frontline of what the MLA-CCCC Joint Task Force on Writing and AI called “critical AI literacy.” Generative AI offers opportunities to make programming more accessible to diverse learners, and we explore how to use these emerging technologies to build inclusive pathways into programming through natural language interfaces and “literate programming.”
This course will emphasize two critical programming languages, JavaScript and Python, that are commonly taught in humanities courses due to their applicability for interactive experiences, public humanities, and textual analysis. This workshop will build participants’ comfort with both generating and debugging code with AI tools, as well as deploying generative AI outside of mainstream commercial projects. Participants will be invited to approach GitHub, Copilot, Hugging Face, TensorFlow, and Jupyter Notebooks through a beginner’s mind, working through, critiquing, and developing assignments and pedagogical applications or their own classrooms. Participants with and without programming experience are welcome.
Prior to the Workshop
Please complete the suggested pre-readings prior to the workshop: note that some of the readings included are for reference, as indicated.
Please install the following software prior to the workshop:
- GitHub - Create an account (ideally using a .edu email for access to all features) and install the desktop software for your operating system.
- GitHub Education - If possible, apply for additional access for advanced models and usage limits. We recommend doing this before traveling to DHSI, as it uses location as part of the application.
- Visual Studio Code - Select “Download” for your operating system
We will walk through the installation process of more complicated tools during the workshop, so please make sure you have administrative access to the laptop you bring for completing exercises throughout.
Workshop Agenda
1. Monday: Introduction and Positioning GenAI (Morning)
Pre-readings
- MLA Student AI Literacy Guide
- “The US of AI” Public Draft, Matt Kirschenbaum
- Intro, Critical Making in the Age of AI, Emily Johnson and Anastasia Salter
- “Interfaced,” Lori Emerson
- Stephen Wolfram, “What Is ChatGPT Doing … and Why Does It Work?”
Additional References
- “All the ways I want the AI debate to be better,” Andy Masley
- Artificial Intelligence: A Guide for Thinking Humans, Melanie Mitchell
- MLA-CCCC Joint Task Force on Writing and AI
- TextGenEd: Teaching with Text Generation Technologies
- “The Limits of Computation,” David M. Berry
2. Monday: Google Colab and Distant Reading with Python (Afternoon)
Pre-readings
- “Can language models predict the next twist in a story?”, Ted Underwood
- Analysis, Critical Making in the Age of AI, Emily Johnson and Anastasia Salter
Slides
Exercises
Additional References
3. Tuesday: Web Scraping and Data Analysis with Python (Morning)
Pre-readings
- “The Ground Truth of DH Text Mining,” Tanya E. Clement
- “Programmable World,” Code to Joy, Michael Littman
Slides
Exercise
Additional References
- “So You Want to Be a Wizard,” Julia Evans
- “Understanding and Creating Word Embeddings,” Avery Blankenship, Sarah Connell, and Quinn Dombrowski
4. Tuesday: P5.js and OpenProcessing (Afternoon)
Pre-readings
Exercise
Additional References
5. Wednesday: ml5.js and Teachable Machine (Morning)
Pre-readings
Exercises
Additional References
- Creating Deep Convolutional Neural Networks for Image Classification, Nabeel Siddiqui
- ml5 Beginner’s Guide, Daniel Shiffman
- Evaluating GitHub for DH, Sean Morey Smith
- GitHub and Git Tutorial
6. Wednesday: Distant Coding in Visual Studio Code (Afternoon)
Pre-readings
Exercise
Additional References
- DHRI VS Code Installation
- Awesome Digital Humanities GitHub Repository
- Collaborative Coding: Pair Programming and Co-Authoring Git Commits, Diego Siqueira
7. Thursday: Ollama and Public Digital Humanities (Morning)
Pre-readings
- Vibe Coding: AI-Assisted Coding for Non-Developers, Niall McNulty
Exercise
Additional References
8. Thursday: DH Pedagogy, DH Futures (Afternoon)
Pre-readings
- “Conclusion,” Algorithms of Oppression, Safiya Umoja Noble
- “DSC #12: The DSC and the New programming Language,” Katherine Bowers, Quinn Dombrowski, and Roopika Risam
Slides
Exercise
Additional References
9. Friday: Applications and Assignments (Morning)
Demo: Coding Education with AI
- Coding Archaeology Mystery (GitHub)
- Coding Archaeology Mystery (Demo)
Demo: Grading Workflow using AI for Rubric Drafts
Collaboration: DH Pedagogy and Policy
Demo: Grading Workflow
Additional Resources and Discussion
- The Professors are Using ChatGPT… New York Times
- Vibe Coding - MIT Technology Review
- “How I learned to stop worrying and love AI,” Leonardo Flores
- Generative AI In the Real World
- Future of Computer Science (K-12)
On Policy
- ACM on Authorship
- Citing Generative AI - MLA
- Citing ChatGPT - APA
- Generative AI and Policy Development
- “Why should your students do the work?,” Ricky Mouser and Savannah Pearlman