Producing content for intelligent tutoring systems from a natural language promp

Abstract

The educational landscape thrives on a constant flow of engaging and informative content. Traditionally, the responsibility of delivering this content falls squarely on the shoulders of educators. While Intelligent Tutoring Systems (ITS) have emerged to provide automated electronic delivery of educational materials, complete with features like personalised feedback and targeted resources, a significant challenge remains. Educators, despite their irreplaceable role in fostering a dynamic learning environment, often struggle with the time investment required to develop comprehensive instructional materials and lesson plans alongside their other crucial duties. This research delves into the potential of state-of-the-art large language models (LLMs) to alleviate this burden. LLMs are sophisticated AI systems capable of processing and generating vast amounts of text data. Our investigation explored the feasibility of leveraging LLMs to automate content creation within the educational sector. The findings are promising: our research suggests that AI-powered systems utilizing LLMs could see a positive adoption rate exceeding 85% among educators. This indicates a strong interest in such technology as a tool to streamline lesson preparation. Furthermore, the most effective LLM model evaluated displayed a remarkable 96% accuracy in content generation. This is particularly noteworthy considering the inherent tendency of LLMs to occasionally generate inaccurate information, a phenomenon referred to as ”hallucination.” The high accuracy achieved by this model demonstrates the potential of LLMs to produce reliable and effective educational content. As AI technology continues to develop, we can expect even more innovative applications to emerge in the educational landscape. The ultimate goal is not to replace educators but rather to empower them with powerful tools that free up valuable time and resources, allowing them to focus on fostering meaningful student interaction and personalised learning experiences.