Towards Llm-Driven Personalized and Engaging Learning Experiences: A Design Science Research Approach

The rise of Artificial Intelligence (AI) and, specifically, Large Language Models (LLMs), has significantly impacted various sectors, with education emerging as one of the most promising areas of application. LLMs, such as OpenAI’s GPT-3, are increasingly being used by students and educators worldwide. Despite their widespread adoption, their effective integration within educational settings remains insufficiently explored, and no compre-hensive framework exists to guide their optimal use. This research aims to address this gap by investigating the current applications of LLMs in education, identifying emerging patterns, evaluating their impact on learning out-comes, and proposing a framework to maximize their effectiveness in educational contexts. Following the Design Science Research (DSR) methodology, we aim to propose a framework to assist educators in effectively leveraging large language models (LLMs) to enhance learning outcomes. The framework will be validated through the development of a prototype. We also aim to provide empirical insights into how LLMs can foster personalized learning, increase student engagement, and optimize instructional design.