My research centers on understanding how students' self-assessments at small moments during the programming process — like planning before starting to code and looking up syntax — shape and are shaped by students' evolving sense of belonging in computer science. Since these small, commonplace moments can inform the narratives students tell about themselves as computer science students, how can we bring about more supportive peer-to-peer interactions in these moments to help students see themselves as capable of succeeding in computer science?

Some of my past work includes:

• Understanding common concepts students use when evaluating their belonging in computer science and when self-assessing during the programming process.
• Creating a model AI assignment for middle and high school students to learn about bias and ethical concerns with large language models.

My current work builds on the first of these threads to:

• Identify moments during the pair programming process that influence students' sense of belonging and self-assessments.
• Design an AI tool to support peer-to-peer interactions and collaboration during the pair programming process.

Publications

Large Language Madlibs Model AI Assignment
Kristin Fasiang, Duri Long
EAAI 2025