The Impact of GPT Models on Education: Enhancing Learning Outcomes and Addressing Challenges - Yang Nuoya (ITM Web of Conferences)
This paper investigates the impact of Generative Pre-Trained Transformer (GPT) models in the field of education, a rapidly evolving area as artificial intelligence (AI) technologies become more integrated into learning environments. The primary' objective of the study is to evaluate both the benefits and challenges of implementing GPT models to enhance educational outcomes. The research delves into how GPT can drive personalized learning experiences, automate routine administrative tasks, and provide immediate, adaptive feedback to students. It also critically examines potential issues, including data privacy concerns and the need for teachers to adapt to these new technologies. The study utilizes a comprehensive methodology’ that involves a detailed analysis of GPT model architecture and its application across various educational contexts. Key areas of focus include the deployment of GPT in adaptive learning platforms, its role in automating grading processes, and its capacity to generate interactive and engaging educational content. This balanced approach aims to provide a nuanced understanding of the transformative potential of GPT in education, highlighting both its ability to enhance learning outcomes and the challenges it presents for broader implementation.