3 citations · 6 across the 8 of their papers we have counts for
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cs.AI2026
Can We Trust Item Response Theory for AI Evaluation?
Han Jiang, Sunbeom Kwon, Jinwen Luo +2
AI benchmarks increasingly leverage item-level statistical models, particularly item response theory (IRT), to estimate capabilities, rank systems, select informative examples, and…
cs.AI2026★ 1 cited
AI Evaluation Should Require Standardized Item-Level Data Releases
Han Jiang, Susu Zhang, Dongyao Zhu +6
This position paper argues that standardized item-level benchmark data should become the default infrastructure for AI evaluation. Current evaluations suffer from underspecified it…
cs.AI2026★ 1 cited
Generative AI Assisted Workflows in Architectural Conceptual Design: Performance, Creative Self-Efficacy, and Cognitive Load
Han Jiang, Yao Xiao, Rachel Hurley +1
Generative AI (GenAI) is increasingly adopted in design education, yet evaluating its educational value through final outcomes provides an incomplete picture. This study compares t…