Harnessing AI for Personalized Academic Major Recommendations An Application of Large Language Models in Education

In the domain of educational counseling, the utilization of Large Language Models (LLMs) in conjunction with sophisticated AI technologies, such as embeddings and vector databases, introduces a groundbreaking methodology for advising students on their academic specializations. This study explores the complexities associated with conventional recommendation systems, which include issues like data imbalance and insufficient contextual awareness etc. By adopting a FewShot Learning framework, we harness the flexibility of LLMs to identify critical factors related to students’ interests and competencies. Our approach enables the dynamic extraction of important contextual information, thereby enhancing the predictive efficacy of the models. Through comprehensive experimentation with varied student datasets, we will experiment on if our AI system outperforms traditional recommendation techniques implemented by academic advisors and if it can yield detailed analyses customized to individual student profiles. This pioneering strategy holds considerable potential for advancing academic advising and assisting students in making well-informed choices regarding their educational trajectories.