About

Mina Lee is an Assistant Professor leading the AI & Me group at the University of Chicago. Her research focuses on the design, evaluation, and responsible use of AI systems, particularly in the context of writing and education. The group approaches AI not as inherently good or bad, but as a technology whose impact depends on how it is designed and used. Mina Lee's work is interdisciplinary, involving collaboration with researchers and practitioners across fields such as writing, education, psychology, and media. Her research directions include building and assessing AI systems like autocomplete tools, exploring design choices for writing assistants, and studying the effects of AI on reading comprehension and social norms such as AI use disclosure. Additionally, she is committed to fostering mindful and responsible AI use by translating research findings into AI literacy efforts through classes, workshops, and the development of tools and materials to support teachers and students.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Human–computer interaction
• Natural Language Processing
• Software engineering
• Cognitive science
• Systems engineering
• World Wide Web
• Linguistics
• Data science
• Multimedia
• Psychology

Selected publications

• A Design Space for Intelligent and Interactive Writing Assistants

  2024 · 116 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Human–computer interaction

  In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities. We seek to address this challenge by proposing a design space as a structured way to examine and explore the multidimensional space of intelligent and interactive writing assistants. Through community collaboration, we explore five aspects of writing assistants: task, user, technology, interaction, and ecosystem. Within each aspect, we define dimensions and codes by systematically reviewing 115 papers, while leveraging the expertise of researchers in various disciplines. Our design space aims to offer researchers and designers a practical tool to navigate, comprehend, and compare the various possibilities of writing assistants, and aid in the design of new writing assistants.

  PublisherOA PDFDOI

• CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities

  CHI Conference on Human Factors in Computing Systems · 2022 · 315 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Artificial Intelligence

  Large language models (LMs) offer unprecedented language generation capabilities and exciting opportunities for interaction design. However, their highly context-dependent capabilities are difficult to grasp and are often subjectively interpreted. In this paper, we argue that by curating and analyzing large interaction datasets, the HCI community can foster more incisive examinations of LMs’ generative capabilities. Exemplifying this approach, we present CoAuthor, a dataset designed for revealing GPT-3’s capabilities in assisting creative and argumentative writing. CoAuthor captures rich interactions between 63 writers and four instances of GPT-3 across 1445 writing sessions. We demonstrate that CoAuthor can address questions about GPT-3’s language, ideation, and collaboration capabilities, and reveal its contribution as a writing “collaborator” under various definitions of good collaboration. Finally, we discuss how this work may facilitate a more principled discussion around LMs’ promises and pitfalls in relation to interaction design. The dataset and an interface for replaying the writing sessions are publicly available at https://coauthor.stanford.edu.

  DOI

• On the Opportunities and Risks of Foundation Models

  arXiv (Cornell University) · 2021 · 2169 citations

  • Computer Science
  • Artificial Intelligence
  • Computer Science

  AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.

  PublisherOA PDFDOI

• Enabling Language Models to Fill in the Blanks

  2020 · 125 citations

  • Computer Science
  • Computer Science
  • Natural Language Processing

  We present a simple approach for text infilling, the task of predicting missing spans of text at any position in a document. While infilling could enable rich functionality especially for writing assistance tools, more attention has been devoted to language modeling-a special case of infilling where text is predicted at the end of a document. In this paper, we aim to extend the capabilities of language models (LMs) to the more general task of infilling. To this end, we train (or fine-tune) off-the-shelf LMs on sequences containing the concatenation of artificially-masked text and the text which was masked. We show that this approach, which we call infilling by language modeling, can enable LMs to infill entire sentences effectively on three different domains: short stories, scientific abstracts, and lyrics. Furthermore, we show that humans have difficulty identifying sentences infilled by our approach as machinegenerated in the domain of short stories.

  DOI

Frequent coauthors

• Q. Vera Liao

  Microsoft (United States)

  3 shared

• Ziang Xiao

  3 shared

• Percy Liang

  3 shared

• John Joon Young Chung

  3 shared

• Disha Shrivastava

  2 shared

• Chinmay Kulkarni

  2 shared

• Wesley Hanwen Deng

  Carnegie Mellon University

  2 shared

• Joshua B. Tenenbaum

  Massachusetts Institute of Technology

  2 shared

Labs

• Writing with AIPI

Education

• Ph.D., Computer Science

  Stanford University

  2023

Awards & honors

• 2025 KOCSEA (Korean Computer Scientists and Engineers Associ…
• 2022 Innovators under 35 Korea, MIT technology review
• Best Paper Award, Honorable Mention, CHI 2017
• Doctoral study abroad scholarship, Korea foundation for adva…
• Stanford University school of engineering graduate fellowshi…

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