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

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Title: Leveraging Large Language Models to Generate Course-Specific Semantically Annotated Learning Objects
Language: English
Authors: Dominic Lohr (ORCID 0000-0002-6330-2327), Marc Berges (ORCID 0000-0002-9982-547X), Abhishek Chugh (ORCID 0000-0001-6765-5229), Michael Kohlhase (ORCID 0000-0002-9859-6337), Dennis Müller (ORCID 0000-0002-4482-4912)
Source: Journal of Computer Assisted Learning. 2025 41(1).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 13
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Resource Units, Semantics, Automation, Questioning Techniques, Natural Language Processing, Artificial Intelligence, Technology Uses in Education, Computer Science Education, Discussion (Teaching Technique), Cognitive Processes
DOI: 10.1111/jcal.13101
ISSN: 0266-4909
1365-2729
Abstract: 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.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1459051
Database: ERIC
FullText Text:
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  Data: Leveraging Large Language Models to Generate Course-Specific Semantically Annotated Learning Objects
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  Data: <searchLink fieldCode="AR" term="%22Dominic+Lohr%22">Dominic Lohr</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6330-2327">0000-0002-6330-2327</externalLink>)<br /><searchLink fieldCode="AR" term="%22Marc+Berges%22">Marc Berges</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-9982-547X">0000-0002-9982-547X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Abhishek+Chugh%22">Abhishek Chugh</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-6765-5229">0000-0001-6765-5229</externalLink>)<br /><searchLink fieldCode="AR" term="%22Michael+Kohlhase%22">Michael Kohlhase</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-9859-6337">0000-0002-9859-6337</externalLink>)<br /><searchLink fieldCode="AR" term="%22Dennis+Müller%22">Dennis Müller</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-4482-4912">0000-0002-4482-4912</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Computer+Assisted+Learning%22"><i>Journal of Computer Assisted Learning</i></searchLink>. 2025 41(1).
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: 10.1111/jcal.13101
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  Data: 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.
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