Elkins’s research addresses artificial intelligence in relation to literature, narrative, affective computing, and the
ethics of AI. Her book
The Shapes of Stories, published by
Cambridge University Press in 2022, provided a comprehensive methodology for using diachronic
sentiment analysis to analyze the emotional aspects of
plot across dozens of literary classics using SentimentArcs. This method has been used to analyze narrative in diverse forms including literature, translations, TV scripts, end of life medical narratives, and the evolution of social media narratives for elections and economic crisis. Elkins was among the first scholars to analyze GPT-3's capabilities for creative writing. Her September 2020 paper 'Can GPT-3 Pass a Writer's Turing Test?' (co-authored with Jon Chun) -- published just four months after the model's release—was archived on gwern.net, a selective technical repository documenting AI development that is consulted by researchers at organizations including OpenAI and DeepMind. Her computational methodologies have been replicated in multiple studies archived alongside her work. Elkins' research addresses fundamental questions about information processing and transformation across different systems. Her work spans from analyzing how consciousness processes embodied information (Proust scholarship) to developing computational methods for preserving information in degraded cultural archives, identifying how AI systems process information differently than humans (syntactic framing vulnerabilities), and shaping governance frameworks for high-stakes decision-making. This includes analysis of how information transforms across languages, cultures, and media types, with consistent attention to what information is lost or preserved in each transformation. She presented the first
transdisciplinary AI research at leading academic conferences including the
Modernist Studies Association in October 2019, The International Society for the Study of Narrative in March 2020 and the
Modern Language Association Conference in January 2021. Elkins was an early advocate for incorporating AI in literary studies with co-authored essays in
The Journal of Cultural Analytics in September 2020 and Narrative in January 2021. More recently she focused on how AI redefines writing, creativity, authorship, translations of literature, eXplainable AI, and the future of the academia in leading journals like
Poetics Today. Her article "A(I) University in Ruins: What Remains in a World with Large Language Models?" in the
Proceedings of the Modern Language Association addresses how AI may fundamentally redefine traditional academic disciplines. Her collaborative position paper addressing the risks and benefits of open-source AI was selected for oral presentation at
ICML in July 2024. Elkins traditional scholarship includes essays on
Plato,
Virginia Woolf,
Franz Kafka,
Marcel Proust, and
William Wordsworth. In 2001 she won the A. Owen Aldridge Prize in
Comparative Literature for an essay on
Charles Baudelaire. Her 2025 human-centered AI scholarship includes "Beyond Plot: How Sentiment Analysis Reshapes Our Understanding of Narrative Structure" in the Journal of Cultural Analytics, "The Shapes of Cinderella: Emotional Architecture and the Language of Moral Difference" in the journal Humanities, and ""If Open Source is to Win, It Must Go Public" as a Spotlight talk at ICML 2025 CodeML Workshop. In December 2025, Elkins and her co-lead Jon Chun were awarded a grant of up to $330,000 from Schmidt Sciences as part of the Humanities and AI Virtual Institute (HAVI) for their project "Archival Intelligence: Rescuing New Orleans’ Endangered Cultural Legacy." One of only 23 such awards granted globally and totaling $11 million across all recipients, the 18-month initiative develops free, open-access AI tools to preserve and restore endangered materials in small community archives using smartphone photography, with a pilot focus on multilingual newspapers documenting Creole and Cajun communities, as well as early jazz artifacts in New Orleans, while addressing "cultural flattening" in AI models for multicultural and multimodal historical data. == Speaking ==