About

Kathi Fisler is a Research Professor of Computer Science at Brown University, where she also serves as the Director of CS Undergraduate Studies and Research Director of Bootstrap. Her research interests lie at the intersection of human learning, cognitive science, education, data structures, and socio-technical systems, with a foundation in formal methods. Over her career, her research has evolved through several areas including diagrammatic logics for hardware design in the late 1990s, modular verification of feature-oriented programs in the early 2000s, reasoning about access-control and privacy policies in the late 2000s to early 2010s, and computing education from the 2010s through the 2020s. She has taught introductory computing and data structures for over two decades and is actively involved in Brown's efforts in socially-responsible computing education. Fisler has also held various official and unofficial administrative and leadership roles that have influenced her interests. She is one of the lead authors of a textbook focused on teaching computing through a data-centric lens, combining data science and data structures. Since the late 1990s, she has been heavily involved in outreach for K-12 computing education, primarily through Bootstrap and participation in standards committees for K-12 computing education. Much of her work is conducted in collaboration with teams in cognitive engineering, computing education, and programming languages and tools at Brown, as well as with Bootstrap.

Research topics

• Computer Science
• Mathematics education
• Psychology
• Engineering
• Artificial Intelligence
• Pedagogy
• Mathematics

Selected publications

• Artifact For: Meaningful Human-in-the-Loop Checking of GenAI Synthesis for Restricted Languages

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-17

  otherOpen accessSenior author
  PublisherDOI

• Artifact For: Meaningful Human-in-the-Loop Checking of GenAI Synthesis for Restricted Languages

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-16

  otherOpen accessSenior author
  PublisherDOI

• Artifact For: Meaningful Human-in-the-Loop Checking of GenAI Synthesis for Restricted Languages

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-16

  otherOpen accessSenior author
  PublisherDOI

• Artifact For: Meaningful Human-in-the-Loop Checking of GenAI Synthesis for Restricted Languages

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-17

  otherOpen accessSenior author
  PublisherDOI

• Artifact For: Meaningful Human-in-the-Loop Checking of GenAI Synthesis for Restricted Languages

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-15

  otherOpen accessSenior author
  PublisherDOI

• Sharing Is Scaring: Linking Cloud File-Sharing to Programming Language Semantics

  2025-10-09

  articleOpen accessSenior author

  Users often struggle with cloud file-sharing applications. Problems appear to arise not only from interface flaws, but also from misunderstanding the underlying semantics of operations like linking, attaching, downloading, and editing. We argue that these difficulties echo long-standing challenges in understanding concepts in programming languages like aliasing, copying, and mutation.

  PublisherOA PDFDOI

• Computing in Data Science or Data in Computer Science? Exploring the Relationship between Data Science and Computer Science in K-12 Education

  2024-03-14 · 3 citations

  article

  students to learn in order to succeed in an increasingly data-driven world. Foundational data literacy skills currently live in a number of subjects across K-12 (e.g., data collection and analysis in science classes, statistical calculations in mathematics/statistics, data visualization and communication in civics/social studies), however, a growing number of schools and districts are introducing stand-alone data science (DS) courses. Given the centrality of computing and programming in the contemporary practice of DS, many of these courses include topics historically reserved for computer science (CS) classes. Further, many CS courses include dedicated time for DS topics (e.g., AP Computer Science Principles' unit on Data). In many ways, DS educators and CS educators are working towards the same ends in complementary ways. However, at other times, the two disciplines are in tension, especially given the scarcity of time in K-12 student schedules for non-core subjects. This panel will explore what DS education and CS education can learn from each other, how each can contribute and advance the goals of the other, and how these two intertwined disciplines can productively live alongside each other in K-12 settings.

  PublisherDOI

• Iterative Student Program Planning using Transformer-Driven Feedback

  2024-07-03 · 11 citations

  articleOpen access

  Problem planning is a fundamental programming skill, and aids students in decomposing tasks into manageable subtasks. While feedback on plans is beneficial for beginners, providing this in a scalable and timely way is an enormous challenge in large courses.

  PublisherOA PDFDOI

• Expanding Models for Physics Teaching: A Framework for the Integration of Computational Modeling

  Education Sciences · 2024-08-08 · 2 citations

  articleOpen access

  Teaching computation in science courses can enhance science education, but doing so requires that teachers expand the vision of their discipline beyond the traditional view of science presented in most curricula. This article describes a design-based research (DBR) program that included collaboration among high school teachers and professional development leaders in physics and computer science education. Through three years of professional development and teacher-led development, field testing, and refinement of integrated curricular resources, we have combined instructional modeling practices, physical lab materials, and computer programming activities. One of the outcomes is a co-created framework for the integration of computational modeling into physics that is sensitive to teachers’ interests and expressed needs in addition to learning goals. This framework merges two evidence-based approaches to teaching: Bootstrap:Algebra, a web-based computing curriculum that emphasizes using multiple representations of functions and scaffolds that make the programming process explicit, and Modeling Instruction in physics, an approach that emphasizes the use of conceptual models, modeling practices and representational tools. In doing so, we uncover the need to balance teachers’ visions for integration opportunities with practical instructional needs and emphasize that frameworks for integration need to reflect teachers’ values and goals.

  PublisherOA PDFDOI

• Observations on the Design of Program Planning Notations for Students

  2024-03-07 · 5 citations

  articleOpen access

  Program planning is the process of splitting a problem description into subtasks that can be solved independently, then composed into a solution. While much has been written about planning since the 1980s, little research looks at modern contexts such as programs to process data tables. Tool support for this sort of planning is even rarer. As part of a project to develop such tools, we have run two studies to try to identify steps, representations, and interactions that would support novice university students in planning and programming multi-task programs that process data tables. This experience report describes our observations so far, while also raising questions about how to make planning useful for students.

  PublisherOA PDFDOI

Recent grants

• BPC-DP: Deploying a Vertically-Integrated Computing Curriculum to At-Risk Students

  NSF · $615k · 2011–2015

• Designing Professional Development to Foster Mastery and Interest for Integrating Computer Science into Mathematics Classes

  NSF · $1000k · 2021–2025

• Collaborative Research: Hybrid Professional Development to Enhance Teachers' Use of Bootstrap

  NSF · $683k · 2017–2022

• SaTC-EDU: EAGER: Enhancing Cybersecurity Education through Peer Review

  NSF · $229k · 2015–2017

• SHF: Small: User Studies to Improve Novice Programming

  NSF · $288k · 2011–2016

Frequent coauthors

• Shriram Krishnamurthi

  143 shared

• Emmanuel Schanzer

  23 shared

• Joe Gibbs Politz

  University of California, San Diego

  20 shared

• Ren Yan-yan

  Brown University

  12 shared

• Daniel J. Dougherty

  10 shared

• Preston Tunnell Wilson

  John Brown University

  10 shared

• Benjamin S. Lerner

  Northeastern University

  9 shared

• Francisco Enrique Vicente Castro

  New York University

  9 shared

Education

• Ph.D., Computer Science

  University of Washington

  1990

• M.S., Computer Science

  University of Washington

  1986

• B.S., Mathematics

  University of California, San Diego

  1983