The rapid expansion of educational systems and the widespread adoption of online learning platforms have highlighted the limitations of traditional manual essay scoring, such as inefficiency, high cost, and subjective bias. Automated Essay Scoring (AES) seeks to address these challenges, particularly in open-ended questions, where the absence of well-defined reference answers and flexible evaluation criteria makes accurate assessment inherently difficult. Existing approaches, including traditional feature engineering and large language model (LLM)-based methods, often struggle with semantic understanding, scoring consistency, and alignment with human evaluation rubrics. To tackle these issues, we propose ADSC, a novel LLM-augmented dual-stream framework that enhances small models’ feature representation through few-shot-guided LLM reasoning and integrates both semantic and comment-based features via interactive attention mechanisms. This design improves interpretability while enhancing scoring robustness. Experiments on the ASAP-AES dataset show that ADSC achieves state-of-the-art performance, attaining the highest average Quadratic Weighted Kappa (QWK) score and delivering substantial gains on narrative essays, while maintaining superior computational efficiency.