Paper Citation Record · LEDGER

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects

As of 24 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2412.04185.

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

pith.paper-citation-record.v1
2412.04185 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T21:43:06.086060Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

42 of 42 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 27799f59-65a0-46f5-aa3c-f19031e9291d · outbound

This paper cites Visible learning: a synthesis of over 800 meta-analyses relating to achievement.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Visible learning: a synthesis of over 800 meta-analyses relating to achievement

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.461877Z

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Observation c13f61a5-22e8-4e5e-a0df-a9c1ba529b5e · outbound

This paper cites Enhancing the Quality of Learning: Dispositions, Instruction, and Learning Processes.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Enhancing the Quality of Learning: Dispositions, Instruction, and Learning Processes

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.454661Z

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Observation 400a4cc2-e13c-44fc-9bf3-fc0eb57836ca · outbound

This paper cites Cognitive Load During Problem Solving: Eﬀects on Learning.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Cognitive Load During Problem Solving: Eﬀects on Learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.447479Z

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Observation 58e9c624-d1ee-491c-be35-d777dbb7e9de · outbound

This paper cites Cognitive Architecture and Instructional Design.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Cognitive Architecture and Instructional Design

Reference 4

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Observation 6813a265-6451-4650-aaa6-e83a67b95faf · outbound

This paper cites Learning of mastery.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Learning of mastery

Reference 5

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Observation 2aa2eede-936e-43bf-a09b-aaa74ed1e864 · outbound

This paper cites Good-bye, teacher.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Good-bye, teacher

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.426389Z

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Observation 36cee01b-0967-41b8-8eb1-c21c1d1224f6 · outbound

This paper cites Learning with ALeA: T ailored Experiences through Annotated Course Material.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Learning with ALeA: T ailored Experiences through Annotated Course Material

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.419840Z

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Observation cb41cda8-6bf6-4da3-85d3-3da15735a2cd · outbound

This paper cites Automatic Question Generation from T ext - an Aid to Independent Study.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Automatic Question Generation from T ext - an Aid to Independent Study

Reference 8

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Observation 7b4dea06-8ea6-422f-b82c-00106e445bc4 · outbound

This paper cites Can We T rust AI-Generated Educational Content? Comparative Analysis of Human and AI-Generated Learning Resources.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Can We T rust AI-Generated Educational Content? Comparative Analysis of Human and AI-Generated Learning Resources

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.406424Z

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Observation 50e4cadc-a132-429f-9f2e-f9805417dcfa · outbound

This paper cites Automatic Generation of Multiple-Choice Questions for CS0 and CS1 Curricula Using Large Language Models.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Automatic Generation of Multiple-Choice Questions for CS0 and CS1 Curricula Using Large Language Models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.399588Z

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Observation 0b52ec82-a2dc-4782-baf7-7a5ec7631db0 · outbound

This paper cites Automatic Generation of Programming Exercises and Code Explanations Using Large Language Models.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Automatic Generation of Programming Exercises and Code Explanations Using Large Language Models

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.392660Z

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Observation f78b0f99-0b59-4274-8e75-55b3ae019512 · outbound

This paper cites A Systematic Review of Automatic Question Generation for Educational Purposes.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects A Systematic Review of Automatic Question Generation for Educational Purposes

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.385612Z

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Observation c73d5ad4-451a-4074-9b2c-37eda462ab26 · outbound

This paper cites The Robots Are Here: Navigating the Generative AI Revolution in Computing Education.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects The Robots Are Here: Navigating the Generative AI Revolution in Computing Education

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.378830Z

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Observation 126decdc-706e-405b-b449-dde0db7ec609 · outbound

This paper cites T owards Automated Generation and Evaluation of Questions in Educational Domains.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects T owards Automated Generation and Evaluation of Questions in Educational Domains

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.371858Z

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Observation de487b7c-3242-4474-8c08-cfd2dafb4d9b · outbound

This paper cites T owards Human-Like Educational Question Generation with Large Language Models.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects T owards Human-Like Educational Question Generation with Large Language Models

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.365131Z

Source-reported events for the cited work

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Observation 5301f3bd-edaa-44b6-aef2-ecb428e3fe65 · outbound

This paper cites From Hype to Insight: Exploring ChatGPT ’s Early Footprint in Education via Altmetrics and Bibliometrics.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects From Hype to Insight: Exploring ChatGPT ’s Early Footprint in Education via Altmetrics and Bibliometrics

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.358159Z

Source-reported events for the cited work

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Observation 3a8b61f6-5ab0-4bf5-8ced-3a9e0cf7e87c · outbound

This paper cites Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.351147Z

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Observation 6035465d-bf3c-4c37-898a-5d062e6ef331 · outbound

This paper cites Exploring Automated Distractor and Feedback Generation for Math Multiple-choice Questions via In-context Learning.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Exploring Automated Distractor and Feedback Generation for Math Multiple-choice Questions via In-context Learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.343996Z

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Observation 9138c580-23d8-450a-8fb8-44ad401a4c73 · outbound

This paper cites Reading Comprehension Quiz Generation Using Generative Pre-trained T ransform- ers.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Reading Comprehension Quiz Generation Using Generative Pre-trained T ransform- ers

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.337045Z

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Observation 3456e278-1c19-42ee-934e-5b510fe9e96c · outbound

This paper cites Generating Multiple Choice Questions for Computing Courses Using Large Language Models.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Generating Multiple Choice Questions for Computing Courses Using Large Language Models

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.329762Z

Source-reported events for the cited work

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Observation 926efec2-440f-4387-bc1c-75ca786130fd · outbound

This paper cites A T axonomy for Learning, T eaching, and Assessing: A Revision of Bloom’s T axon- omy of Educational Objectives.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects A T axonomy for Learning, T eaching, and Assessing: A Revision of Bloom’s T axon- omy of Educational Objectives

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.323412Z

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Observation cd4e7bea-6044-4fd7-aba9-cdb9bb16d64b · outbound

This paper cites The Y-Model - Formalization of Computer Science T asks in the Context of Adaptive Learning Systems.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects The Y-Model - Formalization of Computer Science T asks in the Context of Adaptive Learning Systems

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.316190Z

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Observation 6e9bb13f-ebd6-4aab-9607-33239e811ae6 · outbound

This paper cites Automatic Multiple Choice Question Generation From T ext: A Survey.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Automatic Multiple Choice Question Generation From T ext: A Survey

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.309285Z

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Observation 988aa202-6e36-4f17-bf71-7f050f980790 · outbound

This paper cites Automatic Question Generation: A Review of Methodologies, Datasets, Evaluation Metrics, and Applications.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Automatic Question Generation: A Review of Methodologies, Datasets, Evaluation Metrics, and Applications

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.302066Z

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Observation 5202383a-3eea-4cd0-b4a9-b2d8af1b422d · outbound

This paper cites The Power of Feedback.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects The Power of Feedback

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.294555Z

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Observation 6dbf9c89-fd16-46c5-a35b-d3f0103c60fb · outbound

This paper cites Exploring the Potential of Large Language Models to Generate Formative Programming Feedback.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Exploring the Potential of Large Language Models to Generate Formative Programming Feedback

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.287790Z

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Observation b851c53f-3d18-4afa-b1e6-19155f8b32f7 · outbound

This paper cites Let Them T ry to Figure It Out First.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Let Them T ry to Figure It Out First

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.280496Z

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Observation 3f06eaf9-f161-4d5e-b8b1-8117dc411bc0 · outbound

This paper cites Exploring the Responses of Large Language Models to Beginner Programmers’ Help Requests.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Exploring the Responses of Large Language Models to Beginner Programmers’ Help Requests

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.273516Z

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Observation 283e7e20-a41e-40c4-aae6-6d5f4a14ec00 · outbound

This paper cites Investigating the Potential of GPT-3 in Providing Feedback for Programming Assessments.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Investigating the Potential of GPT-3 in Providing Feedback for Programming Assessments

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.266485Z

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Observation ccee1ab5-4538-4c12-8371-e4ee07d7cc31 · outbound

This paper cites A Large Language Model-Assisted Education T ool to Provide Feed- back on Open-Ended Responses.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects A Large Language Model-Assisted Education T ool to Provide Feed- back on Open-Ended Responses

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.258838Z

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Observation 001b3a79-a7fc-4098-98bd-22a978dc6538 · outbound

This paper cites System Description: s T eX3 – A LATEX-based Ecosystem for Semantic/Active Mathematical Docu- ments.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects System Description: s T eX3 – A LATEX-based Ecosystem for Semantic/Active Mathematical Docu- ments

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.251538Z

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Observation 90680ed7-d7ac-4a9e-bf44-02e9d5d35a49 · outbound

This paper cites OMDoc – An open markup format for mathematical documents [Version 1.2].

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects OMDoc – An open markup format for mathematical documents [Version 1.2]

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.238553Z

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This paper cites https://github.com/slatex/sTeX/blob/main/doc/stex-manual.pdf.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects https://github.com/slatex/sTeX/blob/main/doc/stex-manual.pdf

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.231743Z

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Observation 0135d6c3-1709-4b61-a4a2-dc7861a67dd3 · outbound

This paper cites An HTML/CSS schema for T EX primitives – generating high-quality responsive HTML from generic T EX.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects An HTML/CSS schema for T EX primitives – generating high-quality responsive HTML from generic T EX

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.223594Z

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Observation c5443b5c-00b4-4260-b1f1-763048c6bd9b · outbound

This paper cites GPT-4 Technical Report.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects GPT-4 Technical Report

Reference 35

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Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Unresolved cited work

Reference 36

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Observation d465bc09-fe3a-4df8-9bc4-115d83028771 · outbound

This paper cites Why Johnny Can’t Prompt: How Non-AI Experts T ry (and Fail) to Design LLM Prompts.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Why Johnny Can’t Prompt: How Non-AI Experts T ry (and Fail) to Design LLM Prompts

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T21:43:06.077013Z

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Observation acaa1cb2-c42f-4e6a-a438-0e1113ea127d · outbound

This paper cites https://platform.openai.com/docs/guides/prompt- engineering.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects https://platform.openai.com/docs/guides/prompt- engineering

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.207577Z

Source-reported events for the cited work

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Observation be20ebf3-b8ed-4a1d-919f-5c9b26773519 · outbound

This paper cites A Novel Framework for the Generation of Multiple Choice Question Stems Using Semantic and Machine-Learning T echniques.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects A Novel Framework for the Generation of Multiple Choice Question Stems Using Semantic and Machine-Learning T echniques

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.199392Z

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Observation 71207cc8-3816-44b5-bcfb-e25e5fc6901f · outbound

This paper cites Y ou’re (Not) My Type – Can LLMs Generate Feedback of Speciﬁc Types for Introductory Programming T asks? Journal of Computer Assisted Learning (JCAL) 2024;Accepted.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Y ou’re (Not) My Type – Can LLMs Generate Feedback of Speciﬁc Types for Introductory Programming T asks? Journal of Computer Assisted Learning (JCAL) 2024;Accepted

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.190937Z

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Observation 6f4b4f28-d638-48c6-b9ab-207a455f2ff5 · outbound

This paper cites Playing Games with Ais: The Limits of GPT-3 and Similar Large Language Models.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Playing Games with Ais: The Limits of GPT-3 and Similar Large Language Models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:43:06.182412Z

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Observation dd810bc3-01cc-427d-b262-d5bf2aee6e02 · outbound

This paper cites an unresolved cited work.

Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects Unresolved cited work

Reference 2022

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.