325 citations · 400 across the 19 of their papers we have counts for
6 papers · 1 filter
Generative AI Produces and Transmits Beauty Bias
AJ Alvero, Deepak Varuvel Dennison, Alexandra Crespin +5
Societies reward people judged to be attractive by standards intertwined with gender and race. In this paper, we examine if and how generative AI may contribute to these dynamics.…
Sandpiper: Orchestrated AI-Annotation for Educational Discourse at Scale
Daryl Hedley, Doug Pietrzak, Jorge Dias +9
Digital educational environments are expanding toward complex AI and human discourse, providing researchers with an abundance of data that offers deep insights into learning and in…
LLM Reasoning Predicts When Models Are Right: Evidence from Coding Classroom Discourse
Bakhtawar Ahtisham, Kirk Vanacore, Zhuqian Zhou +2
Large Language Models (LLMs) are increasingly deployed to automatically label and analyze educational dialogue at scale, yet current pipelines lack reliable ways to detect when mod…
Utility-Preserving De-Identification for Math Tutoring: Investigating Numeric Ambiguity in the MathEd-PII Benchmark Dataset
Zhuqian Zhou, Kirk Vanacore, Bakhtawar Ahtisham +7
Large-scale sharing of dialogue data is key to advancing the science of teaching and learning, yet rigorous de-identification remains a major barrier. In mathematics tutoring trans…
Does Algorithmic Uncertainty Sway Human Experts? Evidence from a Field Experiment in Selective College Admissions
Hansol Lee, AJ Alvero, René F. Kizilcec +1
Algorithmic predictions are inherently uncertain: even models with similar aggregate accuracy can produce different predictions for the same individual, raising concerns that high-…
Codebook-Injected Dialogue Segmentation for Multi-Utterance Constructs Annotation: LLM-Assisted and Gold-Label-Free Evaluation
Jinsook Lee, Kirk Vanacore, Zhuqian Zhou +3
Dialogue Act (DA) annotation typically treats communicative or pedagogical intent as localized to individual utterances or turns. This leads annotators to agree on the underlying a…