AI-Driven Content Generation for Adaptive E-Learning Using Generative Pretrained Transformers (GPT-3)

Abstract

The dynamic nature of digital learning has made one-size-fits-all e-learning approaches ineffective, as it fails to accommodate individual student needs and deliver tailored learning experiences. The goal of adaptive e-learning environments is to enhance the effectiveness of learning and engagement by customizing the content to the characteristics of the learner, their performance level, and how to interact with the content. Yet, much of the current collection of adaptive systems relies on resources that are curated manually, thus failing to scale and offer real-time personalization. This study aims to present an adaptive e-learning approach using AI-based Generative Pretrained Transformers (GPT-3) to adapt educational content and deliver personalized education. The suggested framework combines learner profiling, learning analytics, adaptive recommendation systems, and Natural Language Processing with GPT-3 to create personalized learning content like explanations, quizzes, summaries, assessments, and revision activities. A quantitative experimental research methodology was used, and the learner interaction data were retrieved from the online learning environments. Learner engagement, Personalization efficiency, Knowledge retention, Assessment accuracy, and User satisfaction were evaluated as some measures of the system's effectiveness. Experimental results showed that there are far better performance improvements than traditional e-learning systems. The suggested framework has a high level of learner engagement (93%), personalization efficiency (91%), assessment accuracy (92%), and user satisfaction (94%). There was an increase in retention rate from 70% to 89% in the GPT-3 system as compared to conventional systems, while the completion rate increased from 73% to 92%. These findings clearly indicate that artificial intelligence-generated content can make significant contributions in making instruction scalable, adaptive, and engaging. This paper highlights the significance of artificial intelligence-based systems, particularly the GPT-3 model, in adaptive e-learning to develop content that is contextually relevant and personalized. However, there is no mention of content accuracy and ethics related to algorithms and governance. The future research needs are: Explainable AI, Multimodal Learner Analytics, and Domain-specific Educational Language Models for Improving Adaptive Digital Learning Ecosystems.