Student research has evolved alongside the technology itself. The earliest projects explored creative applications, from theater set design to musical composition and game design alongside computational text analysis. Research since then spans many domains, each organized around a central question.
AI and Creativity — Can AI create?
Faculty and students investigate whether AI systems produce creative work and what that production reveals about human creativity itself. Students trained neural networks to generate sheet music and 3D sculpture in 2018 and 2019, the year before GPT-2 was announced, and were fine-tuning GPT-2 within months of its staged release. One team asked whether the model could replace a Sex and the City writers’ room. Another coined “centaur screenwriting” for human and machine writing together. Elkins and Chun’s GPT-3 Writer’s Turing Test (Journal of Cultural Analytics, 2020), which drew directly on this mentored research, is now treated as a canonical case in debates about machine authorship. Recent projects test narrative convergence in AI-generated fiction, cross-model patterns in identity-based storytelling, and whether camp, comedy, and rap are computable.
Computational Humanities and Social Sciences — Can AI help us research?
This is the program’s largest research domain. Faculty and students use AI as a method for investigating questions across literature, philosophy, religious studies, political science, economics, and public health. Student projects have mapped emotional arcs across literary traditions and their translations, from the Odyssey to Kafka, run natural language processing on the Septuagint and Protestant sermons, traced political discourse across 316,000 tweets, applied network analysis to Mrs. Dalloway and Little Women, and studied financial sentiment from FOMC transcripts to convertible bond investing. The work flows into the program’s publications: student research contributed to "The Shapes of Stories" (Cambridge University Press, 2022), which credits a student with proposing one of its central questions, to “Beyond Plot” (Journal of Cultural Analytics, 2025), which cites nine student projects, and to “In Search of a Translator” (Frontiers in Computer Science, 2024).
AI and Society — Can AI solve real-world problems?
Faculty and students design and build working systems that address real-world needs, and investigate how AI intersects with justice, governance, and social welfare. The IBM/Notre Dame Tech Ethics Lab grant ($60,000, one of eleven internationally) work benchmarked AI decision-making in juvenile recidivism contexts. This domain also includes entrepreneurship: students have built a retrieval-augmented film recommendation system, an AI voice coach for high-stakes performance, a privacy-first platform translating West African exam grades for global university admissions, and a crowdfunding platform for Venezuela. Others have traced surveillance capitalism through social media terms of service, studied the opioid epidemic in Ohio, and fine-tuned a biomedical language model to help diagnose pediatric rheumatological disorders. In 2025, students trained in the program’s AI courses won the Most Original Project award at HackOH/IO, Ohio’s largest hackathon, and were invited to Y Combinator.
AI Safety and Governance — Can AI be trusted?
This is one of the program’s most visible research areas. Students were early here, too: within weeks of ChatGPT’s 2022 launch, a student completed a systematic jailbreak study of later cited by Meta AI researchers. Others audited GPT-4 as soon as it was released, testing whether it could fool plagiarism detection and how consistently it judged literary quality. Current projects probe bias in image generators, build contamination-resistant benchmarks for mathematical reasoning, and test negation sensitivity and robustness in high-stakes AI reasoning, work that feeds directly into the program’s published safety research.
AI and Human Experience — What does AI reveal about us?
Faculty and students investigate what AI systems reveal about human cognition, creativity, and self-understanding. When a machine can write, what does that tell us about authorship? When an algorithm translates, what gets lost? Students study the human side of the exchange directly and they have topic-modeled 3,275 real ChatGPT exchanges as the first large interaction datasets became available, then 17,000 more to understand what kinds of relationships we are forming with our AI systems. Others have paired sentiment analysis of autism-diagnosis narratives with an audit of AI attitudes toward disability language, and built AI tutors for economics, educational advocacy, and reading support. Work in this domain contributed to “AI Comes for the Author” (Poetics Today, 2024).
AI and Human Futures — How will AI transform how we live, learn and work?
Faculty and students examine how AI is reshaping institutions, economies, and human possibilities. Increasingly that means studying systems where AIs interact with one another: students have tracked emotion, persuasion, and deception across multi-agent negotiations, used multi-agent debate to automate front-office decisions in baseball, and applied network analysis to Moltbook, a social network populated by AI agents, identifying missing memory as a structural problem. The program’s own classrooms remain a live experiment in the question this domain asks: what happens to learning, work, and institutions when intelligence becomes a technology?
Student research is published on Digital Kenyon at digital.kenyon.edu/dh, where it has been downloaded more than 100,000 by readers at more than 4,000 institutions including Stanford, MIT, Oxford, Cambridge, Berkeley, the Max Planck Institute, the Chinese Academy of Social Sciences, and the World Bank.