Chigozirim Ifebi
California Institute of Technology
Faculty Advisor: Prof. Danielle Wood
Research Supervisor: Alissa Chavalithumrong
Department: Aeronautics and Astronautics
Biography
ChiChi is a rising senior studying Applied and Computational Mathematics with a
minor in Information and Data Science at Caltech. Having grown up in vibrant, multiethnic
communities in New York, she is passionate about centering underrepresented communities
in the creation and evaluation of AI systems. At MIT, she works under Prof. Danielle Wood
to investigate how computing identity has shifted among middle schoolers due to AI usage
in Zero Robotics. Beyond research, ChiChi is committed to making STEM spaces more
equitable. She is the BSU President and has led student advocacy efforts at Caltech to expand
Black Studies and increase support for underrepresented students. After graduation, she hopes
to pursue a PhD in Human-Centered AI and ultimately shape AI policy that keeps pace with
technological innovation while centering ethics and justice. Outside of work, she enjoys
reading, playing Animal Crossing: New Leaf, browsing Pinterest, and trying new restaurants
with friends.
Who Is The Programmer?: Impact of AI-Assisted Coding Usage on Identity
Formation in Zero Robotics
Chigozirim Ifebi1, Alissa Chavalithumrong2, Danielle Wood2
1Department of Computing + Mathematical Sciences, California Institute of Technology
2Program in Media Arts & Sciences, Massachusetts Institute of Technology
CThis project aims to measure the usage of Artificial Intelligence (AI)-powered code
generation tools over the past decade (2016–2026) in the Zero Robotics Middle School
Competition and examine how this usage relates to middle school students’ perceptions of
their identities as programmers. Research has proven K-12 robotics programming effective in
improving STEM learning outcomes and fostering interest in engineering careers. However,
a gap remains in research investigating the impact of AI coding assistants on K-12 students’
learning outcomes and computing identity — the “belief in one’s performance/competence,
interest, and recognition in computing” — especially in informal environments like robotics
programs. We finetune and evaluate CodeBERT, a pre-trained text classifier, on a custom
labeled dataset of 300 Zero Robotics code submissions (2016–2021) and 300 corresponding
scripts generated by GPT-4o mini, assessing performance via accuracy, precision, recall, and
F1-score. In parallel, we use qualitative worksheets and interviews from 50 Zero Robotics
middle schoolers to analyze computing identity and AI usage. Applying the computing
identity framework, we identify themes of perceived competence, interest, and recognition in
student responses. Results aim to help educators and program designers responsibly navigate
AI integration in youth robotics competitions in ways that promote, rather than undermine,
computing identity.
