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

arXiv: https://arxiv.org/abs/2306.01815 · HTML: https://mlsanity.com/paper/2306.01815/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2023.

## Built on by (most cited first, 12 of 12)

- [Large Language Models for Education: A Survey and Outlook](https://mlsanity.com/paper/2403.18105/) (2024)
- [A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applications](https://mlsanity.com/paper/2503.17003/) (2025)
- [A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in Programming Education](https://mlsanity.com/paper/2312.03173/) (2023)
- [On the Impact of Fine-Tuning on Chain-of-Thought Reasoning](https://mlsanity.com/paper/2411.15382/) (2024)
- [LegalLens: Leveraging LLMs for Legal Violation Identification in Unstructured Text](https://mlsanity.com/paper/2402.04335/) (2024)
- [Generating Educational Materials with Different Levels of Readability using LLMs](https://mlsanity.com/paper/2406.12787/) (2024)
- [The Life Cycle of Large Language Models: A Review of Biases in Education](https://mlsanity.com/paper/2407.11203/) (2024)
- [From Motion Signals to Insights: A Unified Framework for Student Behavior Analysis and Feedback in Physical Education Classes](https://mlsanity.com/paper/2503.06525/) (2025)
- [An Invariant Latent Space Perspective on Language Model Inversion](https://mlsanity.com/paper/2511.19569/) (2025)
- [From Words to Workflows: Automating Business Processes](https://mlsanity.com/paper/2412.03446/) (2024)
- [SGA: Plug&Play Geometric Verification for Educational Video Synthesis](https://mlsanity.com/paper/2607.18116/) (2026)
- [An Educator-Guided LLM Pedagogical Agent for Scaffolded Feedback in Conceptual Database Design](https://mlsanity.com/paper/2610.00870/) (2026)

## Abstract (arXiv)

As Large Language Models (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.