Cover Image

Automated Generation and Tagging of Knowledge Components from Multiple-Choice Questions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Moore, Steven, Schmucker, Robin, Mitchell, Tom, Stamper, John
Format:	Preprint
Published:	2024
Subjects:	Artificial Intelligence Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

The Impact of Item-Writing Flaws on Difficulty and Discrimination in Item Response Theory
by: Schmucker, Robin, et al.
Published: (2025)

An Automatic Question Usability Evaluation Toolkit
by: Moore, Steven, et al.
Published: (2024)

Automated Generation of Curriculum-Aligned Multiple-Choice Questions for Malaysian Secondary Mathematics Using Generative AI
by: Wahid, Rohaizah Abdul, et al.
Published: (2025)

Differentiating Choices via Commonality for Multiple-Choice Question Answering
by: Deng, Wenqing, et al.
Published: (2024)

Pattern Recognition or Medical Knowledge? The Problem with Multiple-Choice Questions in Medicine
by: Griot, Maxime, et al.
Published: (2024)

Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction
by: Lee, Yooseop, et al.
Published: (2025)

Generating Multiple-Choice Knowledge Questions with Interpretable Difficulty Estimation using Knowledge Graphs and Large Language Models
by: Şakiroğlu, Mehmet Can, et al.
Published: (2026)

Question Difficulty Ranking for Multiple-Choice Reading Comprehension
by: Raina, Vatsal, et al.
Published: (2024)

Harnessing Structured Knowledge: A Concept Map-Based Approach for High-Quality Multiple Choice Question Generation with Effective Distractors
by: Scaria, Nicy, et al.
Published: (2025)

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights
by: Biancini, Giorgio, et al.
Published: (2025)

Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation
by: Zhang, Yuhui, et al.
Published: (2025)

Plausibly Problematic Questions in Multiple-Choice Benchmarks for Commonsense Reasoning
by: Palta, Shramay, et al.
Published: (2024)

AraSTEM: A Native Arabic Multiple Choice Question Benchmark for Evaluating LLMs Knowledge In STEM Subjects
by: Mustapha, Ahmad, et al.
Published: (2024)

Knowledge Tagging System on Math Questions via LLMs with Flexible Demonstration Retriever
by: Li, Hang, et al.
Published: (2024)

It is Too Many Options: Pitfalls of Multiple-Choice Questions in Generative AI and Medical Education
by: Singh, Shrutika, et al.
Published: (2025)

MCQG-SRefine: Multiple Choice Question Generation and Evaluation with Iterative Self-Critique, Correction, and Comparison Feedback
by: Yao, Zonghai, et al.
Published: (2024)

UnibucLLM: Harnessing LLMs for Automated Prediction of Item Difficulty and Response Time for Multiple-Choice Questions
by: Rogoz, Ana-Cristina, et al.
Published: (2024)

Answer, Assemble, Ace: Understanding How LMs Answer Multiple Choice Questions
by: Wiegreffe, Sarah, et al.
Published: (2024)

Biomedical Entity Linking as Multiple Choice Question Answering
by: Lin, Zhenxi, et al.
Published: (2024)

Option-ID Based Elimination For Multiple Choice Questions
by: Zhu, Zhenhao, et al.
Published: (2025)

Cognitively Diverse Multiple-Choice Question Generation: A Hybrid Multi-Agent Framework with Large Language Models
by: Tian, Yu, et al.
Published: (2026)

Conformal P-Value in Multiple-Choice Question Answering Tasks with Provable Risk Control
by: Ye, Yuanchang
Published: (2025)

AI Mentors for Student Projects: Spotting Early Issues in Computer Science Proposals
by: Aher, Gati, et al.
Published: (2025)

KCluster: An LLM-based Clustering Approach to Knowledge Component Discovery
by: Wei, Yumou, et al.
Published: (2025)

Balancing Rigor and Utility: Mitigating Cognitive Biases in Large Language Models for Multiple-Choice Questions
by: Zhong, Hanyang, et al.
Published: (2024)

Cognitive Agent Compilation for Explicit Problem Solver Modeling
by: Moon, Hyeongdon, et al.
Published: (2026)

Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models
by: Bao, Qiming, et al.
Published: (2023)

Choices Speak Louder than Questions
by: Cho, Gyeongje, et al.
Published: (2025)

Conformal Sets in Multiple-Choice Question Answering under Black-Box Settings with Provable Coverage Guarantees
by: Yang, Guang, et al.
Published: (2025)

(WhyPHI) Fine-Tuning PHI-3 for Multiple-Choice Question Answering: Methodology, Results, and Challenges
by: Abdellatif, Mohamed Hisham
Published: (2025)

Efficient Medical Question Answering with Knowledge-Augmented Question Generation
by: Khlaut, Julien, et al.
Published: (2024)

Anchored Answers: Unravelling Positional Bias in GPT-2's Multiple-Choice Questions
by: Li, Ruizhe, et al.
Published: (2024)

A Study on Large Language Models' Limitations in Multiple-Choice Question Answering
by: Khatun, Aisha, et al.
Published: (2024)

Self Knowledge Re-expression: A Fully Local Method for Adapting LLMs to Tasks Using Intrinsic Knowledge
by: Wang, Mengyu, et al.
Published: (2026)

Automating the Generation of Prompts for LLM-based Action Choice in PDDL Planning
by: Stein, Katharina, et al.
Published: (2023)

Self-Correcting Large Language Models: Generation vs. Multiple Choice
by: Rahmani, Hossein A., et al.
Published: (2025)

Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident, Especially When They are Wrong
by: Fu, Tairan, et al.
Published: (2025)

Automated Knowledge Component Generation for Interpretable Knowledge Tracing in Coding Problems
by: Duan, Zhangqi, et al.
Published: (2025)

Diversifying Question Generation over Knowledge Base via External Natural Questions
by: Guo, Shasha, et al.
Published: (2023)

Small but Significant: On the Promise of Small Language Models for Accessible AIED
by: Wei, Yumou, et al.
Published: (2025)