About

Dokyun Lee is a Kelli Questrom Associate Professor of Information Systems and Digital Business Fellow in the Questrom School of Business at Boston University. He studies the responsible application, development, and impact of artificial intelligence in e-commerce and the digital economy, with a heavy focus on the economic impact of textual data, along with content extraction, understanding, and engineering. He runs the Business Insights through Text Lab. His research has been recognized with awards such as the Information Systems Society's Gordon B. Davis Young Scholar Award and the Marketing Science Institute's (MSI) Young Scholar Award. His work has received support from organizations including Adobe, Bosch Institute, Google Cloud, MSI, McKinsey & Company, Nvidia, and Net Institute.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Business
• Mathematics
• Sociology
• Information Retrieval
• Data Mining
• Knowledge management
• Internet privacy
• Data science
• Engineering
• Advertising
• World Wide Web
• Social psychology
• Visual arts
• Art
• Marketing
• Geography
• Psychology

Selected publications

• Counting Species of Ideas: A Bayesian Capture–Recapture Ecology Framework for Estimating LLM Novelty

  ScholarSpace (University of Hawaii at Manoa) · 2025-12-23

  article

  Large Language Models (LLMs) are increasingly used for ideation tasks across domains, ranging from product development to marketing and creative writing. Yet, we lack principled methods to quantify their genuine capacity for novelty and ideation. LLMs derive their generative potential from extensive training data, model architectures, and optimization objectives. These factors collectively define a large yet bounded ideation space. However, standard evaluation methods—typically centered around output-level novelty or diversity—only capture a limited view of this broader ideation landscape. To overcome this limitation, we propose shifting the evaluation focus from isolated outputs to this underlying ideation space. Effectively exploring this space involves answering foundational questions: How expansive is this space? How many unique ideas can the model potentially generate? By adapting capture-recapture (CR) theory from ecology, we introduce an estimation framework tailored to the generative behavior of LLMs and infer the unseen idea space beyond observed samples. Validation through asymptotic extrapolation confirms the reliability of our framework, which offers a principled approach to understanding and comparing the innovation capacities of LLMs.

  Publisher

• Who Expands the Human Creative Frontier with Generative AI: Hiveminds or Masterminds?

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Unpacking How GenAI is Revolutionizing and Reshaping the Human Experience in Creative Work

  Academy of Management Proceedings · 2025-07-01

  article

  In recent years, the rollout of generative artificial intelligence (GenAI) for public use has sparked intense debates on the use of this tool for creativity purposes (Amabile, 2020). While some creative professionals have welcomed the use of such tools by creating new categories in creative competitions (ADC Awards, 2024), others have actively resisted GenAI based on concerns of their intellectual property being violated (Akers, 2024; Andersen, 2022). In response to this, an increasing number of studies have examined the use of GenAI in organizational creativity (Berg, Raj, & Seamans, 2023; Doshi & Hauser, 2024; Jia, Luo, Fang, & Liao, 2024). However, as the use of GenAI becomes an inevitable part of the creative process, we argue that the key question is no longer how GenAI affects the creative output, but rather how using GenAI fundamentally changes the way individuals navigate creative work. This change in focus prompts the need to bring the human experience back into the relationship between GenAI and creativity, and explore the situational factors affecting the human experience. This symposium hence aims to showcase the current research on the individual and contextual factors affecting GenAI and creativity. This symposium embarks on an insightful journey through four distinct yet interconnected research streams, delving into different experiences of when and how individuals navigate their creative work when using GenAI. Employing a diverse array of quantitative and qualitative methodologies at multiple levels of analysis, these studies reveal the contingencies involved when incorporating GenAI for creative work, and also mark a paradigmatic shift in our theoretical understanding of creative work. Generative AI and the Reallocation of Creative Effort Author: Nelberto Nicholas Marcos Quinto; University College London Author: Sarah Harvey; Effects of initial AI use and competition outcome on subsequent reliance on AI Author: Velvetina Siu Ching Lim; Author: Yamon Min Ye; Author: Tianyu He; National University of Singapore Creative Markets in the Age of Generative AI: Strategic Shifts and Labor Market Health Author: Eric Zhou; Boston University Author: Dokyun Lee; Author: Gordon Burtch; Boston University Author: Daniel Rock; University of Pennsylvania Author: Prasanna Tambe; Monsters of Our Own Creation: AI, Occupational Cannibalization, and the Future of Work Author: Kevin Woojin Lee; The University of British Columbia

  PublisherDOI

• Generative AI, Open Source, and Application-Layer Product Development

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Multi-Task Learning for Customer Base Analysis: Evidence from 966 Companies

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  preprintOpen accessSenior author
  PublisherDOI

• Who expands the human creative frontier with generative AI: Hive minds or masterminds?

  Science Advances · 2025-09-03 · 4 citations

  articleOpen access

  Artists are rapidly integrating generative text-to-image models into their workflows, yet how this affects creative discovery remains unclear. Leveraging large-scale data from an online art platform, we compare artificial intelligence (AI)-assisted creators to matched nonadopters to assess novel idea contributions. Initially, a concentrated subset of AI-assisted creators contributes more novel artifacts in absolute terms through increased output-the productivity effect-although the average rate of contributing novel artifacts decreases because of a dilution effect. This reflects a shift toward high-volume, incremental exploration, ultimately yielding a greater aggregate of novel artifacts by AI-assisted creators. We observe no evidence of a human-AI effect above and beyond the productivity effect. The release of open-source Stable Diffusion accelerates novel contributions across a more diverse group, suggesting that text-to-image tools facilitate exploration at scale, initially enabling persistent breakthroughs by select "masterminds," driven by increased volume, and subsequently enabling widespread novel contributions from a "hive mind."

  PublisherDOI

• Out of Unstructured Data, Atlas! Mapping Strategic Landscapes with Generative AI

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Take caution in using LLMs as human surrogates

  Proceedings of the National Academy of Sciences · 2025-06-13 · 26 citations

  articleOpen accessCorresponding

  Recent studies suggest large language models (LLMs) can generate human-like responses, aligning with human behavior in economic experiments, surveys, and political discourse. This has led many to propose that LLMs can be used as surrogates or simulations for humans in social science research. However, LLMs differ fundamentally from humans, relying on probabilistic patterns, absent the embodied experiences or survival objectives that shape human cognition. We assess the reasoning depth of LLMs using the 11-20 money request game. Nearly all advanced approaches fail to replicate human behavior distributions across many models. The causes of failure are diverse and unpredictable, relating to input language, roles, safeguarding, and more. These results warrant caution in using LLMs as surrogates or for simulating human behavior in research.

  PublisherDOI

• Generative artificial intelligence, human creativity, and art

  PNAS Nexus · 2024-02-29 · 335 citations

  articleOpen accessSenior author

  Recent artificial intelligence (AI) tools have demonstrated the ability to produce outputs traditionally considered creative. One such system is text-to-image generative AI (e.g. Midjourney, Stable Diffusion, DALL-E), which automates humans' artistic execution to generate digital artworks. Utilizing a dataset of over 4 million artworks from more than 50,000 unique users, our research shows that over time, text-to-image AI significantly enhances human creative productivity by 25% and increases the value as measured by the likelihood of receiving a favorite per view by 50%. While peak artwork Content Novelty, defined as focal subject matter and relations, increases over time, average Content Novelty declines, suggesting an expanding but inefficient idea space. Additionally, there is a consistent reduction in both peak and average Visual Novelty, captured by pixel-level stylistic elements. Importantly, AI-assisted artists who can successfully explore more novel ideas, regardless of their prior originality, may produce artworks that their peers evaluate more favorably. Lastly, AI adoption decreased value capture (favorites earned) concentration among adopters. The results suggest that ideation and filtering are likely necessary skills in the text-to-image process, thus giving rise to "generative synesthesia"-the harmonious blending of human exploration and AI exploitation to discover new creative workflows.

  PublisherOA PDFDOI

• Generative AI Degrades Online Communities

  Communications of the ACM · 2024-02-22 · 8 citations

  articleOpen access

  How large language models are influencing online communities.

  PublisherOA PDFDOI

Frequent coauthors

• Kartik Hosanagar

  24 shared

• Gordon Burtch

  Boston University

  15 shared

• Zhaoqi Cheng

  Boston University

  12 shared

• Shunyuan Zhang

  11 shared

• Prasanna Tambe

  University of Pennsylvania

  10 shared

• Kannan Srinivasan

  Carnegie Mellon University

  8 shared

• Dongwon Lee

  University of Hong Kong

  8 shared

• Param Vir Singh

  Film Independent

  7 shared

Education

• PhD, Operation and Information Management

  University of Pennsylvania Wharton School

  2015

• Master's, Statistics

  Yale University

  2010

• Bachelor's, Computer Science

  Columbia University

  2009

Awards & honors

• Information Systems Society's Gordon B. Davis Young Scholar…
• Marketing Science Institute's (MSI) Young Scholar Award