Work

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

Year: 2024

Type: article

Abstract: 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.

Source: Journal of Computer Assisted Learning

DOI: 10.1111/jcal.13101

Authors: Dominic Lohr, Marc Berges, Abhishek Chugh, Michael Kohlhase, Dennis Müller

Institution: Friedrich-Alexander-Universität Erlangen-Nürnberg

Language: English

Topic: Artificial Intelligence in Education

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

Estimated APC paid: $3,950