International Journal of
Information and Education Technology

Abstract—Advances in educational technology are reshaping learning, with Intelligent Tutoring Systems (ITS) offering personalized education. However, creating high-quality, adaptive content remains a significant challenge for educators, requiring substantial time and effort. This research explores the potential of Large Language Models (LLMs), such as GPT-4, to automate content generation, addressing these challenges and enhancing educational efficiency. LLMs, leveraging deep learning and transformer architectures, are capable of generating human-like, contextually relevant text. By fine-tuning these models, this study investigates their application in producing diverse educational materials, including lesson plans, quizzes, and study guides. The system employs prompt engineering to ensure adaptability and alignment with learner needs. Evaluation results demonstrate promising outcomes. Content generated by the system achieved a 96% accuracy rate, overcoming common issues like hallucination, while surveys indicate an 85% likelihood of educator adoption. These findings underscore the potential of AI-powered tools to reduce the workload for educators, enabling them to focus on meaningful student engagement and tailored teaching strategies.

Keywords—educational technology, intelligent tutoring systems, large language models, automated content generation, personalized learning

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Cite: Andrew Emanuel Attard and Alexiei Dingli, "Empowering Education with AI: Automating Content Generation through Large Language Models," International Journal of Information and Education Technology, vol. 15, no. 9, pp. 2021-2030, 2025.

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).