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Paper Citation Record · LEDGER

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights

As of 7 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2506.04851.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.04851 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:37:17.847179Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

27 of 27 outbound references displayed

  • verified exact0
  • verified fuzzy25
  • unresolved2
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7fba996f-747a-4c76-ad13-ad533977ab0f · outbound

This paper cites A taxonomy for learning, teaching, and assessing: A revision of bloom's taxonomy of educational objectives: complete edition, 2001.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights A taxonomy for learning, teaching, and assessing: A revision of bloom's taxonomy of educational objectives: complete edition, 2001

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:18.139920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:15.820324Z digest=sha256:739c323b1fc65b3f947c3926336e798369c121dd5b51169045b8625a5afa83ad

Observation 4d738ce9-7f09-4fb3-a724-acd1da17afc7 · outbound

This paper cites Generating questions and multiple-choice answers using semantic analysis of texts.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights Generating questions and multiple-choice answers using semantic analysis of texts

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:18.130120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:15.911609Z digest=sha256:2de4dad9ed77402f5b7f59792e41196cfaf7a4861072cbe91b61807949eb5452

Observation f4d36a73-89ac-48fb-984c-b592812a60ae · outbound

This paper cites Personality-based recommendation in e-commerce.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights Personality-based recommendation in e-commerce

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:18.120708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:16.087782Z digest=sha256:7e100e5ba4491546c1c6c78b35b16d521dd1f73673fc00ce13bc6a35f1f503e9

Observation cca9c41b-24ec-44b0-b5f9-378b21bb85d9 · outbound

This paper cites Scalable educational question generation with pre-trained language models.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights Scalable educational question generation with pre-trained language models

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:18.111659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:16.171749Z digest=sha256:6311555723909ef5c24ce9f0550ab418ebb695aaa4047a33063791747eb627f8

Observation 6a36b44b-dcfd-4c32-8a4c-b6c233162a85 · outbound

This paper cites Toward personalized xai: A case study in intelligent tutoring systems.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights Toward personalized xai: A case study in intelligent tutoring systems

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:18.101838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:16.293984Z digest=sha256:484b411990d9548c4471109afa9ff68f8aafe2d248e72a21e2188a41ec053856

Observation 88288ca8-8fdd-4648-966a-4180345d1de9 · outbound

This paper cites A preliminary inquiry into using corpus word frequency data in the automatic generation of english language cloze tests.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights A preliminary inquiry into using corpus word frequency data in the automatic generation of english language cloze tests

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:18.092020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:16.446747Z digest=sha256:cf1bec552567451f0216d3c3c9ffa2d5ce8549a3263a599893b3ffea756804bf

Observation 4ceedd62-4217-46fd-b054-16c120a41b21 · outbound

This paper cites Automatic multiple choice question generation from text: A survey.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights Automatic multiple choice question generation from text: A survey

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:18.081729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:16.558581Z digest=sha256:f3543d50cee1cd0fb24d6759f82096bf6a7b54185bee240d59edb71b719ff639

Observation 08dc2506-2dc0-4897-bb53-e654b51ecd79 · outbound

This paper cites A social cultural recommender based on linked open data.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights A social cultural recommender based on linked open data

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:18.072535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:16.650045Z digest=sha256:55585f0bc7c9efb548a76d6f50ee0f4d6eca0857552433403fe39c1c8debe380

Observation a30c82c8-5958-4601-b729-b813aea161cd · outbound

This paper cites an unresolved cited work.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights Unresolved cited work

Reference 9

Resolution
unresolved
raw_fallback, observed 2026-08-07T10:37:18.062585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:16.807647Z digest=sha256:f10118e650d3414c9df50001529a8767685fd8a4dda3e8692e59beb476c68ab9

Observation 8f98dd1f-e698-4edd-8890-b343247e08f4 · outbound

This paper cites The meta4rs proposal: Museum emotion and tracking analysis for recommender systems.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights The meta4rs proposal: Museum emotion and tracking analysis for recommender systems

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:18.051643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:16.933454Z digest=sha256:abbf98b4ddbaa3f58377c956607c192ef265f9f50a7e2eb62e8e7e9532c50a04

Observation 9f7343cd-664e-4123-8f52-8c740ad6462c · outbound

This paper cites Using deep learning for collecting data about museum visitor behavior.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights Using deep learning for collecting data about museum visitor behavior

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:18.041736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:17.017810Z digest=sha256:936fe13ca5ab926bfca35874ea788b7175cba23860dbd716ad9a550624b482c1

Observation 2f0e9eac-c2db-466e-8206-0b8f47bf33f7 · outbound

This paper cites Optimising moodle quizzes for online assessments.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights Optimising moodle quizzes for online assessments

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:18.031005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:17.124692Z digest=sha256:f6c8acf399d61d635ea6db66ad1369181138a36c649e3251914804536ae430cd

Observation e285bba4-7f01-49d3-a181-6eb659729290 · outbound

This paper cites Shaping the future of education: exploring the potential and consequences of ai and chatgpt in educational settings.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights Shaping the future of education: exploring the potential and consequences of ai and chatgpt in educational settings

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:18.021042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:17.302504Z digest=sha256:36d50c6555f40365555a9eaf2c254cfd633601d278a41f170dbd5f2d53a5a895

Observation bec0761e-7b73-486f-ba8b-05bc4b6ed581 · outbound

This paper cites A novel approach to generate distractors for multiple choice questions.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights A novel approach to generate distractors for multiple choice questions

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:18.011618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:17.392975Z digest=sha256:4c69aadddb4c3594f923d6a855457c371bccf39feb4e349b96199ed3b0174833

Observation 04321597-b416-497c-a230-a95ec6fa73b1 · outbound

This paper cites u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-07T10:37:17.547723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:37:17.547723Z digest=sha256:c28fafc8070745d199409598c51503e171b4207e97e31f83f80cdca064007b01

Observation 4ae826dc-d4b9-49d6-b300-626dd0cc08dc · outbound

This paper cites Multiple choice question generation using bert xl net, 2023.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights Multiple choice question generation using bert xl net, 2023

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:17.994985Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:17.703221Z digest=sha256:ea37f3fbb7110d36a03f99cedab3502e253ff8417ec2f7b7c117f9fc879b267c

Observation 2ae01011-cfd7-4247-9a62-6be33778888c · outbound

This paper cites Automatic computer science domain multiple-choice questions generation based on informative sentences.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights Automatic computer science domain multiple-choice questions generation based on informative sentences

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:17.984366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:17.815097Z digest=sha256:f28c8e461501736faba89721f0dc54b932e4541b63f324db6a58ad0b4baffb94

Observation 300f5585-f63b-4ab6-9a15-83b7963a76fb · outbound

This paper cites A computer-aided environment for generating multiple-choice test items.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights A computer-aided environment for generating multiple-choice test items

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:17.974963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:17.818515Z digest=sha256:be3ebd2b046e2d90715329886e545aabd2f9f6365858f8608263e16f319e94f7

Observation 27e663a9-35d8-48fc-8b61-ba691ffae718 · outbound

This paper cites Knowledge injection to counter large language model (llm) hallucination.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights Knowledge injection to counter large language model (llm) hallucination

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:17.964890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:17.821888Z digest=sha256:6182806051b3f9642b50ef0de6baa21bb7a5c11fceb3f723e5187624bb4d9810

Observation 6c61b4b0-f70b-42e6-8955-69cf0bdf4aa8 · outbound

This paper cites A system for generating multiple choice questions: With a novel approach for sentence selection.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights A system for generating multiple choice questions: With a novel approach for sentence selection

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:17.952029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:17.824999Z digest=sha256:c141fa31abd795a0b3a60427d60c1e4a3b2acbb259586a11cd2fa3ce6fd4a78f

Observation b71fe7f2-590c-43f8-89c5-71a2f2fe6b56 · outbound

This paper cites Evalquiz – llm-based automated generation of self-assessment quizzes in software engineering education.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights Evalquiz – llm-based automated generation of self-assessment quizzes in software engineering education

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:17.942872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:17.828618Z digest=sha256:e0049b7d4a5c1f32fd9a84e1566df28da95a4be9db33b12e263f74a2fc494993

Observation 189b938b-4833-4095-86be-c3f44547c3a4 · outbound

This paper cites Leveraging large language models for multiple choice question answering, 2022.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights Leveraging large language models for multiple choice question answering, 2022

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:17.932887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:17.831637Z digest=sha256:79bd92f158c329225ebe59653182e071dae5a217a644ab7473263df815f652e6

Observation bb93de38-6043-4ee3-9cfc-e64f56bf0c0d · outbound

This paper cites Cross-domain recommendation for enhancing cultural heritage experience.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights Cross-domain recommendation for enhancing cultural heritage experience

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:17.922124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:17.834867Z digest=sha256:e1f12f032edd935e0096cf357ed0f21b67ece20d195faf38b75ccef2f2e73332

Observation 3999d742-b7f0-42a4-ac1b-094fc3b85113 · outbound

This paper cites Harnessing multi-role capabilities of large language models for open-domain question answering.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights Harnessing multi-role capabilities of large language models for open-domain question answering

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:17.911882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:17.837866Z digest=sha256:5024fca32ab20a0f639a71eb78003291c712fb084fba1b5a386b5f5bbaec6bc0

Observation f41510f0-7249-447f-a269-0f6ab14192db · outbound

This paper cites Chatgpt: Challenges, opportunities, and implications for teacher education.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights Chatgpt: Challenges, opportunities, and implications for teacher education

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:17.901204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:17.841035Z digest=sha256:5a3edc9628069da2decb853c49d2425a00753ff60285af120dacb3e22e3d5313

Observation 9fc2bc22-540c-4227-9337-d6cba3aec568 · outbound

This paper cites Siren's song in the ai ocean: A survey on hallucination in large language models, 2023.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights Siren's song in the ai ocean: A survey on hallucination in large language models, 2023

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:17.891192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:17.844156Z digest=sha256:e74d8b30505b7e09e96cccd93a49e87ddd9170e6a5a7f3523fef33406800c478

Observation 6f38b596-ffc5-42d9-8152-717a7f16de2f · outbound

This paper cites Why johnny can’t prompt: how non-ai experts try (and fail) to design llm prompts.

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights Why johnny can’t prompt: how non-ai experts try (and fail) to design llm prompts

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T10:37:17.880062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-07T10:37:17.847179Z digest=sha256:055914f8a003d4b970d9db641e9038bbb81ba116f4088d67e51061ca5f86a92f

Pith citing papers

No inbound Pith citation observations are available.