Course Materials: Foundations and Applications of Humanities Analytics
Course Materials Digital Humanities NLP

Course Materials: Foundations and Applications of Humanities Analytics

As the teaching fellow for Foundations and Applications of Humanities Analytics (2022-2023) at the Santa Fe Institute, I design and teach workshops for participant researchers. I foster an inclusive and intellectually rigorous classroom for researchers with diverse backgrounds, from senior faculty to early career graduate students.

You can view our repository on GitHub for data sets and code examples that we have used in the past.

These courses are supported by National Endowment of the Humanities (NEH) Grant (no. HT-272418-20)

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PANGeA

PANGeA

PANGeA is a system that uses large language models (LLMs) to create narrative content for turn-based RPGs based on game designers' high-level criteria. It introduces a novel validation system for handling free-form text input during development and gameplay, employing "self-reflection" techniques, enabling small/local LLMs to perform comparably to foundational models. It enriches player-NPC interactions by generating personality-biased non-playable characters (NPCs). It improves AI accuracy through crowdsourcing mechanics. PANGeA houses a server with a custom memory system that provides context for LLM generation. The server's REST interface enables integration with any game engine.

GAME-KG

GAME-KG

Knowledge graphs (KGs) can augment large language models (LLMs) while also providing an explainable set of facts that can be inspected by a human. Explainability is valuable for fields that may otherwise avoid LLMs due to hallucinations, such as human trafficking analysis. Creating KGs poses challenges, however. KGs parsed from documents may include explicit connections (those directly stated in a document) but miss implicit connections (those evident to a human, but not directly stated). This research introduces GAME-KG, an approach to modifying explicit and implicit KG connections by crowdsourcing feedback through video games.

Agent-Driven, Game-Based Learning: Personalized CS Education for Diverse Students

Agent-Driven, Game-Based Learning: Personalized CS Education for Diverse Students

AI agents can personalize education by identifying students’ strengths, weaknesses, and personalities to generate content tailored to them. This work presents "personalized education agents" deployed in an educational version of Minecraft. Agents bridge concepts from lessons to "big picture" thinking by creating connections between STEM and interdisciplinary topics, such as the Language Arts. Agents translate student progression and learning outcomes to teachers for their assessment of student progress.