AAAI 2026technical0 citations
Assessing the Quality of AI-Generated Exams: A Large-Scale Field Study
Calvin Isley, Joshua Gilbert, Evangelos Kassos, Michaela Kocher, Allen Nie, Emma Brunskill, Ben Domingue, Jake Hofman
Abstract
While large language models (LLMs) challenge conventional methods of teaching and learning, they present an exciting opportunity to improve efficiency and scale high-quality instruction. One promising application is the generation of customized exams, tailored to specific course content. There has been significant recent excitement on automatically generating questions using artificial intelligence, but also comparatively little work evaluating the psychometric quality of these items in real-world educational settings. Filling this gap is an important step toward understanding generative AI
BibTeX
@inproceedings{aaai2026_assessingthequal,
title = {Assessing the Quality of AI-Generated Exams: A Large-Scale Field Study},
author = {Calvin Isley and Joshua Gilbert and Evangelos Kassos and Michaela Kocher and Allen Nie and Emma Brunskill and Ben Domingue and Jake Hofman and Joscha Legewie and Teddy Svoronos and Charlotte Tuminelli and Sharad Goel},
booktitle = {AAAI 2026},
year = {2026}
}