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20242026
most citedExploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models

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cs.CL2026

Principal Trait Analysis: Towards Deriving "Skills" in Human-AI Collaboration

Hunter McNichols, Kai Du, Andrew Lan

Large Language Model-powered agents are increasingly used in the workplace via human-artificial intelligence (AI) collaboration. In this new era of work, it is important to underst…

cs.CL2024

Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank

Jaewook Lee, Hunter McNichols, Andrew Lan

In this paper, we study an under-explored area of language and vocabulary learning: keyword mnemonics, a technique for memorizing vocabulary through memorable associations with a t…

cs.CL2024

Can Large Language Models Replicate ITS Feedback on Open-Ended Math Questions?

Hunter McNichols, Jaewook Lee, Stephen Fancsali +2

Intelligent Tutoring Systems (ITSs) often contain an automated feedback component, which provides a predefined feedback message to students when they detect a predefined error. To…

cs.CL20241 cited

Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models

Wanyong Feng, Jaewook Lee, Hunter McNichols +5

Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable format in assessments and practices.…

cs.CL2023

Automated Distractor and Feedback Generation for Math Multiple-choice Questions via In-context Learning

Hunter McNichols, Wanyong Feng, Jaewook Lee +4

Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable form of assessment. An important aspe…