LLMs for Knowledge Modeling: NLP Approach to Constructing User Knowledge Models for Personalized Education

This study proposes a method for developing a user knowledge model based on their past learning experiences. The focus is on analyzing academic data, particularly lesson records, to extract information about educational concepts. The ultimate goal is to construct a comprehensive profile that reflects the user's accumulated knowledge throughout their learning journey. Two distinct methods are introduced for concept extraction: a gazetteer-based Named Entity Recognition approach and prompt engineering using ChatGPT. The effectiveness of these methods is assessed through a case study involving a graduate student at the University of Pisa. These knowledge profiles hold significant relevance in today's educational landscape. With the prevalence of lifelong learning, individuals from diverse academic backgrounds participate in professional development courses. This diversity in past learning experiences can pose a challenge for instructors and course designers who must adapt lessons to be understandable and engaging for an audience with heterogeneous knowledge bases. The analysis of academic data offers a systematic approach to modeling each individual's acquired knowledge. This, in turn, facilitates the personalization of learning content and pathways based on students' unique learning experiences. The outcome is an inclusive learning environment that caters to the specific needs of each participant, thereby promoting compelling and stimulating learning experiences.