Leveraging Large Language Models to Generate Course‐Specific Semantically Annotated Learning Objects.
Background: Over the past few decades, the process and methodology of automatic question generation (AQG) have undergone significant transformations. Recent progress in generative natural language models has opened up new potential in the generation of educational content. Objectives: This paper exp...
| Publicado en: | Journal of Computer Assisted Learning Vol. 41; no. 1; pp. 1 - 14 |
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| Autores principales: | , , , , |
| Formato: | pictorial research Journal Article |
| Publicado: |
Wiley-Blackwell
Feb2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=183981450&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 183981450 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Feb2025 vid: 41 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 183981450 183981450 183981450 10.1111/jcal.13101 183981450 ppf: 1 ppct: 13 formats: tig: atl: Leveraging Large Language Models to Generate Course‐Specific Semantically Annotated Learning Objects. aug: au: Lohr, Dominic Berges, Marc Chugh, Abhishek Kohlhase, Michael Müller, Dennis affil: Professorship for Computer Science Education, Erlangen, Germany sug: subj: Natural Language Processing Artificial Intelligence, Generative Computers and Computerization Computer-Assisted Instruction Automation Information Retrieval Semantics Learning Methods Human Cognition Structured Questionnaires Education Standards Course Content Decision Support Systems, Clinical Funding Source ab: Background: Over the past few decades, the process and methodology of automatic question generation (AQG) have undergone significant transformations. Recent progress in generative natural language models has opened up new potential in the generation of educational content. Objectives: This paper explores the potential of large language models (LLMs) for generating computer science questions that are sufficiently annotated for automatic learner model updates, are fully situated in the context of a particular course and address the cognitive dimension understand. Methods: Unlike previous attempts that might use basic methods such as ChatGPT, our approach involves more targeted strategies such as retrieval‐augmented generation (RAG) to produce contextually relevant and pedagogically meaningful learning objects. Results and Conclusions: Our results show that generating structural, semantic annotations works well. However, this success was not reflected in the case of relational annotations. The quality of the generated questions often did not meet educational standards, highlighting that although LLMs can contribute to the pool of learning materials, their current level of performance requires significant human intervention to refine and validate the generated content. pubtype: Academic Journal doctype: pictorial research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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