The paper classifies undergraduate LLM reliance into four types and shows that AI literacy predicts type while value beliefs predict intensity, with implications for outcome measurement.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Narrative-UFET shows that adding controlled synthetic narrative context improves ultra-fine entity typing on long-tail types over sentence-level baselines, with type-changing narratives providing stronger gains than natural contexts.
MADRAG combines multi-agent debate with retrieval-augmented generation to produce training-free analytic essay scores that outperform prompt baselines and approach supervised systems.
LLM-based tool scored 1,200 SoPs for an undergraduate research program using GPT models and a rubric, enabling coordinator shortlisting in 4 hours versus prior multi-week effort.
citing papers explorer
-
Four Types of LLM Reliance and Their Predictors Among Undergraduate Writers: A Mixed-Methods Study at a Minority-Serving R1 University
The paper classifies undergraduate LLM reliance into four types and shows that AI literacy predicts type while value beliefs predict intensity, with implications for outcome measurement.
-
Narrative-UFET: Narrative Generation for Ultra-Fine Entity Typing
Narrative-UFET shows that adding controlled synthetic narrative context improves ultra-fine entity typing on long-tail types over sentence-level baselines, with type-changing narratives providing stronger gains than natural contexts.
-
MADRAG: Multi-Agent Debate with Retrieval-Augmented Generation for Training-Free Analytic Essay Scoring
MADRAG combines multi-agent debate with retrieval-augmented generation to produce training-free analytic essay scores that outperform prompt baselines and approach supervised systems.
-
Using Large Language Models to Support High Volume Application Review for an Undergraduate Research Program
LLM-based tool scored 1,200 SoPs for an undergraduate research program using GPT models and a rubric, enabling coordinator shortlisting in 4 hours versus prior multi-week effort.