Benjamin Xie he|they

Postdoctoral Fellow

Stanford University

I design for data equity.

I am an Embedded EthiCS Postdoctoral Fellow at Stanford University’s Institute for Human-Centered AI and Center for Ethics in Society. My research interest is in designing interactions for critical discourse with and about data for equitable learning, community advocacy, and ethical AI use. I engage with computing education, human-computer interaction, and AI ethics research communities.

I completed my PhD at the University of Washington Information School with Prof. Amy Ko, where I was a National Science Foundation (NSF) Graduate Research Fellow and also a research intern at non-profit Code.org. Prior to my PhD, I was an MIT EECS-Google Research and Innovation Scholar as an undergraduate and Master’s student at MIT researching with Prof. Hal Abelson and MIT App Inventor.

For more information, see about me, projects, or CV.


  • Human-Computer Interaction (HCI)
  • Computing Education
  • Critical Data Studies
  • Artificial Intelligence, Ethics, and Society
  • Design Justice


  • PhD in Information Science, 2022

    University of Washington

  • MEng in Computer Science, 2016

    Massachusetts Institute of Technology

  • BS in Computer Science, 2015

    Massachusetts Institute of Technology


Sep 2022: Started Postdoc at Stanford!

Started as Embedded EthiCS Fellow at Stanford Inst. for Human-Centered Artificial Intelligence (HAI) & Center for Ethics in Society see announcement

Aug 2022: Presented paper at ICER 2022!

Paper on ‘A Decade of Demographics in Computing Education Research: A Critical Review of Trends in Collection, Reporting, and Use’ presented at ICER 2022! see paper

Feb 2022: Paper published to CSCW 2022 / PACMHCI!

Journal article titled ‘Surfacing Equity Issues in Large Computing Courses with Peer-Ranked, Demographically-Labeled Student Feedback’ published to PACMHCI and will be presented at CSCW 2022! see paper

Nov 2021: Defended my dissertation! 🎓

I successfully defended my dissertation on ‘Stakeholders’ Interpretations of Data for Equitable Computing Education.’ see dissertation (summaries)


Modeling thoughts processes to provide more equitable practice writing code

Using keystroke logging to provide metacognitive intreventions for novice programmers.

StudentAmp: Contextualized student feedback to amplify minoritized voices in computing courses

StudentAmp contextualized student feedback so teaching teams of large, remote computing courses could interpret it more equitably.

Improving equity of Code.org's CS Discoveries curriculum

Investigating how Code.org CS Discoveries curriculum serves different sub-populations to improve the equity of learning experiences and outcomes.

Codeitz: An adaptive learning experience that lets learners decide

Designing for learner agency in self-directed online learning experiences

Improving computer science education w/ explicit instruction

Providing explicit instrutions to make introductory programming more approachable.

Recent Papers

Surfacing Equity Issues in Large Computing Courses with Peer-Ranked, Demographically-Labeled Student Feedback

Developed and evaluated StudentAmp, a tool that contextualized student feedback to enable teaching teams to perspective take for more equitable interpretation of student feedback data.

Stakeholders' Interpretations of Data for Equitable Computing Education

Interactions with data that consider prior knowledge, perceptions of power relationships, and cultural competency can enable computing education stakeholders to connect their interpretations of data with their domain expertise in service of equity-oriented goals.

Interpretations and Uses of Data for Equity in Computing Education

My submission and poster for the ICER 2021 Doctoral Consortium provided an overview of the three main projects of my dissertation. Combined, these projects are design explorations of how stakeholders (students, teachers, curriculum designers) can interpret and use data to support equity-oriented goals.

Domain Experts' Interpretations of Assessment Bias in a Scaled, Online Computer Science Curriculum

Explored a new use of Differential Item Functioning (DIF) where domain experts (Code.org curriculum designers) interpreted data on potential test bias by gender and race.

Recent Posts

Hire me as a postdoc or research scientist!

I’m seeking postdoc opportunities related to data equity & education!

Job Calls for Research in CS & STEM Edu, HCI

Sharing a spreadsheet of job calls related to computing edu, STEM edu, HCI & edu research


Please reach out to me by email or on Twitter direct message!