What we do now
IPHS asks how people make meaning, exercise judgment, remember the past, and live together. Odyssey approaches those questions through literature, philosophy, history, political and social thought, religion, science, and the arts.
Today, some of the same questions arise in our encounters with artificial intelligence. What happens when machines interpret language, imitate human voices, offer moral advice, shape political information, or make recommendations that affect people’s lives? What do these systems understand, and what do they miss?
Students need more than one kind of education to answer such questions well. They need technical skill, but they also need to understand the human phenomena represented in their data. In IPHS, computation, the humanities, and the social sciences are not separate pieces brought together at the end. Each shapes the questions students ask, the methods they choose, and the conclusions they can responsibly draw.
AI Safety & Governance
Engaged in NIST's federal AI-standards consortium (CAISI) through the Modern Language Association. LLM evaluation, red-teaming, ethical auditing, and comparative global AI regulation.
Computational Humanities and Social Science
Narrative analysis, affective AI, social-network modeling, and multi-agent behavioral simulation. Computational and statistical methods applied to questions about language, culture, politics, medicine, law, and human decision-making.
Cultural Heritage & Archival AI
Schmidt Sciences-funded work rescuing endangered archives. Community-governed data sovereignty. AI for multilingual cultural preservation.
Meaningful work, from the beginning
Students undertake original computational projects early in the curriculum, but they do not skip the hard parts. In Programming Humanity, they learn programming, natural-language processing, sentiment analysis, data storytelling, and social-network modeling.
They also learn to prepare and evaluate data, compare methods, interpret results, and ask whether a computational model adequately represents the human phenomenon it claims to measure. The projects are engaging because the technical skills have an intellectual purpose. Students use computation to investigate questions about language, narrative, behavior, culture, politics, and social life—and learn that producing an answer is different from understanding what the answer means.
Students use research, creative practice, and new tools to understand human life—and to help create a world in which people can flourish.
How the curriculum fits together
Odyssey gives students a historical and intellectual foundation in the questions that have shaped human life.
Programming Humanity introduces programming through natural-language processing, sentiment analysis, data storytelling, and social-network modeling. Students learn these methods alongside the conceptual work required to use them responsibly.
AI for Humanity turns to machine learning, language models, model evaluation, and the consequences of placing intelligent systems in human settings.
Advanced seminars bring students to the moving frontier of AI research. They test new models, build evaluations, investigate agents and multi-agent systems, and pursue original projects grounded in their own disciplinary knowledge.
The senior seminar asks students to bring this work into conversation with their majors and with the larger questions of Humane Studies.
Students may also draw on courses in computing, mathematics, statistics, economics, political science, psychology, and other fields. COMP 118 provides one route into the advanced curriculum, and three IPHS courses are currently recognized as contributory courses in Kenyon’s Computing concentration. Students may pursue either program or combine the two.
What students build
Every IPHS student conducts original computational research. Their subjects vary because students bring different interests and disciplinary knowledge to the work. What the projects share is an effort to join technical inquiry with careful thought about the people, cultures, histories, and institutions represented in the data. Students have built sentiment architectures for novels and fan fiction, profiled 17,000 ChatGPT conversations for decision-making bias, traced surveillance capitalism through social media terms of service, run natural language processing on the Septuagint, modeled the opioid epidemic in Ohio, and designed retrieval-augmented film recommenders. Recent cohorts evaluate LLM decision-making for federal AI-safety standards and build tools to rescue endangered archives in New Orleans. All of it lives at Digital Kenyon, where alumni will find their own projects still being read.
Three questions, three recent projects
Ayesha Aslam studied how people respond emotionally and socially to artificial influencers even when they recognize that they are artificial. Her work asks how conscious knowledge, attachment, and human agency relate.
Anne-Duncan Enright examined biblical language and translation, investigating how foundational texts acquire meaning as they pass through languages and interpretive contexts. Her project connects directly to archival intelligence: the tools we build today will help shape how people inherit and understand their past.
Adrian Mangine created a reading application designed around the needs of readers with ADHD and convergence insufficiency. His project joined an understanding of differences in human cognition and perception to the practical work of designing something that helps people flourish.
All three brought that IPHS training to the Schmidt Sciences Archival Intelligence project in summer 2026, with Anne-Duncan serving as project manager.
Program alumni have carried this training into AI-safety research roles, product leadership at companies like eBay, and AI initiatives at institutions like the National Gallery of Art. In 2025, students trained in the Frontiers in Generative AI course won the Most Original Project award at HackOHI/O, Ohio's largest hackathon, and were invited to Y Combinator.
The AI CoLab
In 2016, IPHS launched the AI CoLab, the first interdisciplinary, humanities-led AI program, built in deep collaboration with partners across academia, industry, government, and the nonprofit world. Students bring the integrated tradition to AI from inside, not as an afterthought. It anchors the world's first human-centered AI curriculum. Their work lives at Digital Kenyon, where, by the repository's own counts, it has been downloaded more than 135,000 times by readers in 198 countries since 2016.
An older conversation in a changed world.
Artificial intelligence does not belong to computer science alone. Universities are increasingly bringing computing into conversation with the humanities, social sciences, law, policy, medicine, design, and the arts. Stanford’s Institute for Human-Centered AI describes AI as spanning the entire university; Edinburgh Futures Institute joins data-led research to work on culture, inequality, governance, education, and narrative.
This is familiar territory for Humane Studies. Co-founder Robert Goodhand’s original courses examined the human predicament in nature and history, then freedom and responsibility in the modern world. IPHS still asks the questions they established: What is our nature, and what do we need to flourish? How has history shaped us—and what should we preserve or rethink? Given those conditions, what can we become, and what ought we create?
In 2016, that tradition gave rise to Kenyon’s human-centered AI curriculum. Understanding and building technologies that serve people requires thinking carefully about human nature, intelligence, creativity, autonomy, inheritance, and the good life. AI also gives those questions new urgency because the systems we create can reshape how people think, relate, and live.