In social networks, the dynamics of opinion formation are often oversimplified by traditional models that focus solely on pairwise interactions. The proposed study addresses the limitation by exploring higher-order interactions through the lens of simplicial complexes, which better represent the complex relationships among individuals. We extend the Opinion Changing Rate (OCR) model to incorporate the higher-order interactions, allowing us to analyze how group dynamics influence individual opinion changes. Utilizing a co-authorship dataset from arXiv, we construct simplicial complexes to investigate the synchronization of opinions among authors in artificial intelligence and machine learning. Our findings reveal that higher-order interactions significantly enhance synchronization rates compared to conventional pairwise models, leading to distinct patterns of opinion formation. The proposed research advances theoretical frameworks in opinion dynamics and offers practical insights for collaborative decision-making processes in various domains, emphasizing the critical role of group influence in consensus-building.