Prototyping the use of Large Language Models (LLMs) for adult learning content creation at scale (2306.01815v1)

Abstract: As LLMs and other forms of Generative AI permeate various aspects of our lives, their application for learning and education has provided opportunities and challenges. This paper presents an investigation into the use of LLMs in asynchronous course creation, particularly within the context of adult learning, training and upskilling. We developed a course prototype leveraging an LLM, implementing a robust human-in-the-loop process to ensure the accuracy and clarity of the generated content. Our research questions focus on the feasibility of LLMs to produce high-quality adult learning content with reduced human involvement. Initial findings indicate that taking this approach can indeed facilitate faster content creation without compromising on accuracy or clarity, marking a promising advancement in the field of Generative AI for education. Despite some limitations, the study underscores the potential of LLMs to transform the landscape of learning and education, necessitating further research and nuanced discussions about their strategic and ethical use in learning design.

The paper "Prototyping the use of LLMs for adult learning content creation at scale" explores the application of LLMs in the domain of asynchronous course creation aimed at adult learners. With the increasing incorporation of Generative AI into various sectors, the paper addresses both the opportunities and challenges presented by these technologies in the field of education.

The researchers developed a prototype course utilizing an LLM and employed a human-in-the-loop process to scrutinize and enhance the content produced by the AI. This approach is crucial to ensuring that the content generated is both accurate and clear, maintaining a high standard of educational quality.

Key research questions the paper seeks to answer revolve around the feasibility of using LLMs to produce high-quality learning content with minimal human intervention. Initial findings from the paper are promising, indicating that the use of LLMs can indeed speed up the content creation process significantly without sacrificing accuracy or clarity. This marks an important advancement in leveraging Generative AI for educational purposes.

However, the paper also acknowledges certain limitations. These might include potential biases in the generated content, the necessity for ongoing human oversight, and issues related to the scalability of such educational models. Additionally, the authors emphasize the need for further research and nuanced discussions to address strategic and ethical considerations inherent in the deployment of LLMs within learning design.

Overall, the paper underscores the transformative potential of LLMs in the educational landscape, particularly for adult learning, training, and upskilling. It highlights how these advanced models could revolutionize how educational content is created, paving the way for more efficient and scalable educational solutions in the future.