Automated Essay Scoring systems have traditionally relied on shallow lexical data, such as word frequency and sentence length, to assess essays. However, these approaches neglect crucial factors like text structure and semantics, resulting in limited evaluations of coherence and quality. To address these limitations, we propose a hybrid approach to AES that combines multiple features from different linguistic levels. By leveraging the complementary nature of these features, our model captures the intricate relationships underlying coherent texts. Through extensive experimentation using standard essay datasets, we demonstrate that our large language model based hybrid model surpasses state-of-the-art methods based on shallow features and pure neural networks. This research represents a significant advancement towards the development of an accurate and effective tool for assessing student writing.