Codesigning Ripplet: an LLM-Assisted Assessment Authoring System Grounded in a Conceptual Model of Teachers’ Workflows

要旨

Assessments are critical in education, but creating them can be difficult. To address this challenge in a grounded way, we partnered with 13 teachers in a seven-month codesign process. We developed a conceptual model that characterizes the iterative dual process where teachers develop assessments while simultaneously refining requirements. To enact this model in practice, we built Ripplet,\footnote{A demo video of the system is provided in supplemental materials.} a web-based tool with multilevel reusable interactions to support assessment authoring. The extended codesign revealed that Ripplet enabled teachers to create formative assessments they would not have otherwise made, shifted their practices from generation to curation, and helped them reflect more on assessment quality. In a user study with 15 additional teachers, compared to their current practices, teachers felt the results were more worth their effort and that assessment quality improved.

著者
Yuan Cui
Northwestern University, Evanston, Illinois, United States
Annabel Marie. Goldman
Northwestern University, Evanston, Illinois, United States
Jovy Zhou
Northwestern University, Evanston, Illinois, United States
Xiaolin Liu
Northwestern University, Evanston, Illinois, United States
Clarissa M. Shieh
Northwestern University, Evanston, Illinois, United States
Joshua Yao
Northwestern University, Evanston, Illinois, United States
Mia Lillian. Cheng
Northwestern University , Evanston, Illinois, United States
Matthew Kay
Northwestern University, Evanston, Illinois, United States
Fumeng Yang
University of Maryland College Park, College Park, Maryland, United States

会議: CHI 2026

ACM CHI Conference on Human Factors in Computing Systems

セッション: Learning in the AI Era

P1 - Room 131
7 件の発表
2026-04-17 18:00:00
2026-04-17 19:30:00