The educational landscape thrives on the continuous influx of content, which is engaging and informative, which is traditionally delivered by educators. Thus, Intelligent Tutoring Systems (ITS) have emerged to automate the delivery of educational materials, all while providing personalized feedback and resources which are specially targeted. However, a significant challenge faced by educators is the extensive time and effort required to develop comprehensive instructional materials in addition to their other duties.

This research explores the potential of the use of state-of-the-art large language models (LLMs) in education to tackle this challenge. LLMs are Artificial Intelligence enabled systems that are capable of processing and generating a large amount of text data. This study investigates the feasibility of leveraging LLMs to automate content creation within the educational sector.

The findings of our research are promising, with the findings indicating that AI-powered systems making use of LLMs could achieve an adoption rate that exceeds 85% amongst educators, thus reflecting a strong interest and willingness to adopt technologies which are tools in streamlining lesson preparation. Moreover, the LLM utilized within this research demonstrated a 96% accuracy in content generation when evaluated. This is significant due to LLMs tendency to produce inaccurate and downright false information, a phenomenon which has been dubbed as a “hallucination”. The high accuracy achieved by this model underscores the potential of LLMs to generate educational content which is reliable and effective.

As AI systems continue to advance in capabilities, we anticipate the emergence of even more innovative technologies and applications within the educational landscape. With the goal being not to replace educators, however, empower them with tools which are powerful and result in an increase in the time and resources available instead. This enables educators to instead focus on fostering meaningful student interactions and personalized learning experiences.

Keywords: Technology, education, Artificial Intelligence, Large Language Models, AI, LLM.